35 Real GCP Generative AI Leader Practice Exam Questions and Answers

Canyon Ridge Insurance plans an internal assistant that must answer employee questions about benefits and compliance using only the most up to date documents stored in the company’s private policy library. Leadership wants a managed way to connect a generative model directly to that repository so responses are grounded in the source and not general knowledge. Which Google Cloud product best streamlines this connection to a private document set to produce grounded answers?

• ❏ A. Cloud Search

• ❏ B. Build a custom retrieval pipeline with the Gemini API and a vector store

• ❏ C. Vertex AI Search with managed RAG over private data

• ❏ D. Fine tune a foundation model only on the policy corpus

An editor at example.com uses a large foundation model to create study guides for a classic novella, and when they request a character overview the model invents an elaborate backstory with specific episodes and relationships that never appear in the original work. The writing sounds convincing yet it conflicts with the source. Which common limitation of foundation models is shown in this situation?

• ❏ A. Bias

• ❏ B. Context window limits

• ❏ C. Hallucination

• ❏ D. Knowledge cutoff

A subscription video platform wants to organize viewers into meaningful segments using features such as genres watched, number of viewing sessions, and average monthly spend over the past 120 days, and there are no existing labels for these segments. The team wants to reveal natural groupings in the audience in order to tailor content recommendations and promotions. Which machine learning approach should they choose?

• ❏ A. Semi-supervised Learning

• ❏ B. Reinforcement Learning

• ❏ C. Unsupervised Learning

• ❏ D. Supervised Learning

A junior business intelligence analyst at a media streaming startup needs to explore about 25 TB of data stored in BigQuery and wants an AI helper inside the BigQuery interface that can draft SQL, interpret schemas, and suggest potential insights to pursue. Which Google Cloud offering provides this integrated assistance within BigQuery?

• ❏ A. Vertex AI Pipelines

• ❏ B. Google AI Studio

• ❏ C. Gemini in BigQuery

• ❏ D. Gemini Code Assist

In Google Cloud, you are building a generative AI service that must return responses in under 300 milliseconds but the region quota limits you to 5 GPUs for real time inference. Which part of the solution will this constraint most directly affect?

• ❏ A. Vertex AI Endpoint autoscaling settings

• ❏ B. Choice of foundation model and parameter size

• ❏ C. Cloud Firestore document schema

BrightOrbit Media plans to train a large generative model for creative content and it needs highly parallel compute from purpose built accelerators to complete multiple training experiments across 256 workers within 72 hours. The team is evaluating which accelerator options on Google Cloud best match this requirement. Which choices represent specialized AI accelerators that align with this need?

• ❏ A. Cloud Spanner and Bigtable

• ❏ B. Vertex AI Training and Dataflow

• ❏ C. Tensor Processing Units TPUs and Graphics Processing Units GPUs

• ❏ D. Virtual Private Cloud and Cloud Load Balancing

A product analytics team at Riverton Retail is building a generative AI assistant that provides immediate shopping suggestions. Each customer record contains profile details and loyalty status along with a nested list of past orders where every order includes a SKU and a timestamp. The source system delivers weekly 25 GB CSV exports and the team has found that extracting and reconciling the nested history from these flat files is fragile and time consuming which is delaying releases. Which data format should they choose so the hierarchy is expressed natively and engineering and processing become more efficient?

• ❏ A. BigQuery

• ❏ B. JSON (JavaScript Object Notation)

• ❏ C. CSV

• ❏ D. PNG

A customer experience director at Solstice Electronics wants to uncover the main reasons for dissatisfaction without manually reviewing about 35,000 recorded conversations each month. They need a Google Cloud capability that can automatically process call and chat transcripts at scale after interactions finish so it can group topics, assess negative sentiment, and surface new complaint trends. Which Google Cloud offering should they use?

• ❏ A. Vertex AI Pipelines

• ❏ B. Conversational Insights

• ❏ C. Agent Assist

• ❏ D. Conversational Agents

A conversational AI in the MealWhiz app helps users discover cooking ideas. When a user asks for a recipe for “vegan lasagna”, the agent contacts a third party cuisine catalog API to retrieve several highly rated vegan lasagna recipes and then summarizes them for the user. In this architecture, what is the role of the cuisine catalog API?

• ❏ A. It represents a prompt engineering pattern that steers the model’s reasoning and formatting

• ❏ B. It serves as a memory layer that stores the agent’s dialogue history for context retention

• ❏ C. It is an external tool the agent calls to retrieve up to date recipe data that it does not inherently contain

• ❏ D. It belongs to the foundation model’s static pretraining knowledge that is not changeable at runtime

Which Google Cloud product provides a centralized repository of machine learning features for consistent reuse in training and online serving?

• ❏ A. Dataplex

• ❏ B. Vertex AI Model Registry

• ❏ C. Vertex AI Feature Store service

• ❏ D. Vertex AI Pipelines

All exam questions come from Cameron McKenzie’s Generative AI Practice Exams Udemy course and certificationexams.pro

A recruitment team at mcnz.com uses a text to image generator to create ads that depict professionals such as pilots and architects. Across about 150 gender neutral prompts the images consistently favor one gender and underrepresent others. What does this pattern most directly indicate?

• ❏ A. An elevated temperature used during sampling

• ❏ B. A small context window for prompts

• ❏ C. Systemic bias in the images used to train the model

• ❏ D. Limited model interpretability

An e-commerce brand at scrumtuous.com plans to launch a conversational shopping assistant that personalizes recommendations and handles complex multi item requests. The assistant must ask clarifying questions, call partner and internal APIs for live inventory, discount eligibility, and shipping estimates, then guide the shopper through full checkout. Which Google Cloud offering should they choose to build a production ready generative AI agent that can reason across steps and use tools?

• ❏ A. Dialogflow CX

• ❏ B. Google AI Studio

• ❏ C. Gemini API with custom Python code and no agent framework

• ❏ D. Vertex AI Agent Builder

A product support analyst at scrumtuous.com is using a large language model to tag social posts as “Positive” or “Negative” or “Neutral”. To shape the model’s behavior, the analyst writes this prompt. Classify the sentiment for the following posts. Post ‘Love the new features and the app feels fast’ Sentiment is Positive. Post ‘The update ruined my settings and I want a refund’ Sentiment is Negative. Post ‘Delivery was fine and the packaging was standard’ Sentiment is what? Which prompting technique is being applied?

• ❏ A. Role prompting

• ❏ B. Few shot prompting

• ❏ C. Prompt chaining

• ❏ D. Zero shot prompting

A merchandising group at scrumtuous.com with no programming background wants to spin up a simple AI that turns a short list of keywords into polished product descriptions within one week. They want to use state of the art generative models without writing any code. Which capability in Google Cloud’s AI portfolio would best support these non technical users?

• ❏ A. Vertex AI Workbench

• ❏ B. Vertex AI Studio

• ❏ C. Cloud TPUs

• ❏ D. Comprehensive MLOps capabilities for pipeline orchestration

Which foundation model combines text, audio, and images to generate a video?

• ❏ A. Vision and language model

• ❏ B. Multimodal foundation model for video generation

• ❏ C. Unimodal Large Language Model

The food delivery marketplace scrumtuous.com is launching AI features and has three distinct storage needs. It requires a scalable repository to hold hundreds of terabytes of raw assets such as menu photos, short videos, and HTTP access logs. It also needs a platform where analysts can run complex queries across seven years of cleaned and structured order history to discover seasonal trends. In addition it needs a store that lets an assistant fetch one customer’s current order status with sub second latency. Which mapping of these needs to data storage paradigms is most appropriate?

• ❏ A. First maps to a Data Warehouse and second maps to a Data Lake and third maps to a Database

• ❏ B. First maps to a Data Lake and second maps to a Database and third maps to a Data Warehouse

• ❏ C. First maps to a Data Lake and second maps to a Data Warehouse and third maps to a Database

• ❏ D. First maps to a Database and second maps to a Data Warehouse and third maps to a Data Lake

At scrumtuous.com, an AI assistant for a retail marketplace sometimes fabricates a 35% storewide discount that is not in the official policy documents. The company has been honoring the invented offer which lowers revenue and frustrates customers. Which technical approach most effectively reduces this specific financial exposure?

• ❏ A. Use multi step prompting to decompose user questions before answering

• ❏ B. Vertex AI Safety Filters

• ❏ C. Implement grounding with retrieval so answers are limited to an approved source of company policies

• ❏ D. Fine tune the model on a larger set of generic support conversations

A mid sized financial services firm is deploying Vertex AI Search to power an internal research portal that spans regulatory reports and proprietary analytics. The team must deliver rapid retrieval while meeting strict security and data governance requirements. During configuration and rollout, which action would not be considered a recommended security or governance practice for this sensitive environment?

• ❏ A. Turning on Cloud Audit Logs to capture search requests and data access events for later compliance review

• ❏ B. Applying VPC Service Controls perimeters around Cloud Storage and BigQuery datasets that feed the search index

• ❏ C. Performing a manual review and redaction pass over tens of millions of source files before uploading them to Cloud Storage

• ❏ D. Enforcing access with Identity and Access Management policies that restrict which staff groups can query the application

BlueWave Media plans to roll out a generative AI powered personalization engine within 120 days. Business analysts have validated a valuable use case and the data science team confirms lawful access to customer subscription and interaction data. However the information resides across multiple legacy repositories, exhibits inconsistent regional formats, and has many missing attributes. To make the data suitable for training an AI model, which stage of the machine learning lifecycle will require the greatest initial investment of time and resources?

• ❏ A. Model Training

• ❏ B. Data Preparation

• ❏ C. Model Management

• ❏ D. Model Deployment

Which Google service offers a paid subscription that grants access to the most capable Gemini models along with premium features and higher usage limits?

• ❏ A. Vertex AI

• ❏ B. Google AI Pro

• ❏ C. Google AI Studio

• ❏ D. Gemini for Workspace Business

An educational publisher that runs example.com plans to release a public generative AI helper for children ages 7 to 13. The leadership insists that preventing toxic or inappropriate material is more important than creativity and they accept safer but plainer replies. In Google Cloud which parameter should the team prioritize configuring to enforce strict content moderation?

• ❏ A. Top k

• ❏ B. Safety settings

• ❏ C. Output length

• ❏ D. Temperature

A project manager at a global accounting firm spends hours each week reviewing 90 minute Google Meet transcripts and then composing follow up notes and emails in Gmail. They want built in assistance that can summarize meetings and draft messages inside the Google Workspace apps they already use without creating a custom solution. Which Google Cloud generative AI product best fits this requirement?

• ❏ A. Vertex AI Agent Builder

• ❏ B. Gemini for Google Workspace

• ❏ C. The standalone Gemini app

• ❏ D. Vertex AI Search

A creative studio working for a fashion retailer needs to produce about 300 unique images in three days from short text descriptions that specify subject, mood, and camera framing. They require a Google model that focuses on generating photorealistic or artistic images directly from prompts with fine control over style and composition. Which foundation model should they select?

• ❏ A. Gemma

• ❏ B. Imagen

• ❏ C. Veo

• ❏ D. Gemini

A mid-sized travel booking company named SkyTrail is introducing a generative AI trip-planning assistant to deliver tailored itineraries and handle customer inquiries. The leadership intends to align the effort with Google’s AI Principles to ensure responsible design and deployment. Which objective would not be a primary goal for this assistant under those principles?

• ❏ A. Providing a simple way for users to escalate to a human travel specialist when the assistant is unable to help

• ❏ B. Reducing unfair bias so that itinerary and pricing recommendations are equitable for different users

• ❏ C. Maximizing engagement and conversion by making the assistant deliberately persuasive so users stay longer

• ❏ D. Conducting rigorous safety evaluations to prevent harmful, toxic, or otherwise inappropriate responses

Which of the following is not a built in capability or core component of Vertex AI Agent Builder?

• ❏ A. Enterprise grounding with Vertex AI Search

• ❏ B. On premises license for Vertex AI Agent Builder

• ❏ C. Integrated safety filters and content moderation

All exam questions come from Cameron McKenzie’s Generative AI Practice Exams Udemy course and certificationexams.pro

The billing team at Alpine Merchants stores customer profiles in a relational database. Each row includes clearly defined attributes such as ClientId, FullName, StreetAddress, ContactEmail, and LastOrderDate that are kept in tables with rows and columns. What category best describes the data maintained in this system?

• ❏ A. Semi-structured Data

• ❏ B. BigQuery

• ❏ C. Structured Data

• ❏ D. Unstructured Data

Solara Components fine tuned a large language model using internal documentation and sales data for products sold from 2019 through 2023. When employees ask about a new device line released this quarter, the model either gives inaccurate responses or says it has no information. Which inherent limitation of the foundation model most directly explains this behavior?

• ❏ A. Hallucinations

• ❏ B. Out of distribution queries

• ❏ C. Dependence on the model’s training data

• ❏ D. Unfair bias

A communications lead at the ClearLake Arts Fund needs to produce a concise and polished update video for donors. The team has a narrative in Google Docs, weekly metrics in Sheets, and a slide deck in Slides. They want an AI-first tool inside Google Workspace that can automatically turn those files into a storyboard, propose stock footage, and generate a voiceover from a written script. Which Workspace tool should they use?

• ❏ A. Gemini in Google Drive

• ❏ B. Vertex AI

• ❏ C. Google Vids

• ❏ D. Google Meet video recordings

A national insurance carrier is piloting a generative AI assistant that proposes outcomes for high value claims under tight regulatory oversight. Executives insist on processes that make it clear who is accountable for each recommendation and that allow auditors to understand how the system arrived at its suggestions. This focus on responsibility and understandability reflects what priority?

• ❏ A. Data storage costs and efficiency

• ❏ B. AI accountability and explainability

• ❏ C. Vertex AI Model Garden

• ❏ D. Model inference speed and latency

Which prebuilt Google AI product functions as a video production assistant by assembling a storyboard from Docs and Drive, applying a consistent brand look, and generating a voiceover for export?

• ❏ A. Gemini

• ❏ B. Google Vids

• ❏ C. YouTube Create

A media streaming startup plans to launch two generative AI services. Service Alpha is a live voice assistant that translates a caller’s speech in real time during support calls. Service Beta runs a nightly job that classifies and tags twelve million social posts within an eight hour window. When choosing models and sizing infrastructure, which performance metric should be prioritized for each service?

• ❏ A. Optimize Alpha for high throughput and optimize Beta for low latency

• ❏ B. Optimize both Alpha and Beta for low latency

• ❏ C. Optimize Alpha for low latency and optimize Beta for high throughput

• ❏ D. Optimize both Alpha and Beta for high throughput

A data scientist at BrightHaul Logistics is using a large language model to work through a multi step math puzzle with four or five stages. Asking only for the final number keeps leading to mistakes. The scientist revises the prompt to say “Think step by step and write out your reasoning before giving the final answer” and the model then produces the correct result. What is the name of this prompting approach that encourages the model to spell out its reasoning process?

• ❏ A. ReAct prompting

• ❏ B. Few-shot prompting

• ❏ C. Chain-of-thought prompting

• ❏ D. Tree-of-thought prompting

A mid sized logistics firm named BlueTrail Freight plans to launch an internal assistant that answers employee questions about HR policies by grounding each response in the most current documents stored in their corporate HR repository of roughly 80 files that are refreshed every two weeks. They want a Google Cloud approach that minimizes custom plumbing and quickly connects a generative model to their private documents using a retrieval augmented generation pattern. Which solution should they choose?

• ❏ A. Build a custom vector store and retrieval stack on GKE from the ground up

• ❏ B. Use Document AI to extract text then call the model without a retrieval layer

• ❏ C. Use prebuilt RAG in Vertex AI Search to index and ground on their HR repository

• ❏ D. Fine tune a foundation model on HR documents with no retrieval step

A creative studio named Aurora Atelier is building a service where a user writes a brief storyline and the system produces an original and coherent oil painting that illustrates a pivotal moment from that narrative. Which type of generative model most likely provides the core image synthesis capability?

• ❏ A. Generative Adversarial Network

• ❏ B. Diffusion model

• ❏ C. Large Language Model

• ❏ D. Reinforcement learning model

Which Google foundation model is intended for on device deployment so data remains on the device and offline operation is supported?

• ❏ A. Gemini Pro

• ❏ B. Gemma

• ❏ C. Chirp

• ❏ D. Imagen

The correct answer is Vertex AI Search with managed RAG over private data. It provides a managed connection from a generative model to private repositories so answers are grounded in the latest indexed documents.

This service ingests and indexes enterprise content using built in connectors and then performs retrieval augmented generation that returns citations to source passages. It handles chunking, embeddings, relevance ranking, and access control so teams do not need to build or operate a custom retrieval stack. Because it continuously indexes the source library, responses stay aligned with the most current policies and the assistant can justify answers with links back to the original documents.

Cloud Search focuses on enterprise search across Workspace and other data sources but it is not a managed RAG solution for grounding large language model responses. It does not directly provide generative question answering with citations over your private corpus.

Build a custom retrieval pipeline with the Gemini API and a vector store can work but it is not the best way to streamline this requirement. It requires you to design and maintain ingestion, chunking, embedding generation, vector indexing, retrieval, security filtering, and citation logic, which the managed service already provides.

Fine tune a foundation model only on the policy corpus bakes content into model weights and does not guarantee use of the most up to date documents. It offers no built in citations and would require frequent retraining to keep pace with policy updates, which is not aligned with the need for grounded answers.

When a prompt asks for grounded answers over private documents, prefer a managed RAG service that offers connectors, citations, and access control. Avoid choices that require custom plumbing or that rely only on fine tuning since these do not ensure grounded and up to date responses.

The correct option is Hallucination.

This limitation happens when a model generates convincing yet unfounded content that is not grounded in the provided source or verified knowledge. In the scenario the model invents episodes and relationships that are absent from the novella, which matches this behavior of fabricating details while sounding authoritative.

Bias is about systematic skew or unfairness in outputs that reflect stereotypes or imbalances. The issue described is fabrication of facts rather than unfair treatment or prejudiced content.

Context window limits refers to errors that arise when inputs exceed the model token window which leads to truncation or loss of earlier context. The scenario does not indicate an overly long prompt or missing context, and the failure is invention rather than forgetting due to length.

Knowledge cutoff means the model lacks awareness of events after a certain date. A classic novella predates common training cutoffs, and the problem is the creation of non existent details rather than missing recent information.

Identify the failure mode by matching clues to the concept. Choose hallucination when the model confidently invents details, choose context window when inputs are too long and earlier parts are dropped, choose knowledge cutoff when recent facts are missing, and choose bias when outputs show unfair or stereotyped patterns.

The correct approach is Unsupervised Learning.

The team has no predefined labels for audience segments and wants to discover natural groupings from features like genres watched, number of sessions, and average monthly spend. Unsupervised Learning is designed to find structure in unlabeled data and clustering methods can reveal coherent viewer segments that the platform can use to tailor recommendations and promotions.

Semi-supervised Learning is not suitable because it depends on having at least a small set of labeled examples along with many unlabeled ones, and this scenario provides no labels and seeks to discover clusters rather than predict known classes.

Reinforcement Learning trains an agent through rewards to make sequential decisions in an environment, which does not match a static audience segmentation task on historical viewer features.

Supervised Learning requires labeled target classes to train a model, and there are no existing segment labels here, so it cannot create the initial segments needed.

When you see a goal to discover natural groupings without labels, think unsupervised clustering. If labels exist, think supervised. If only a small labeled set exists with a larger unlabeled pool, think semi-supervised. If the problem centers on actions and rewards, think reinforcement.

The correct option is Gemini in BigQuery because it embeds an AI assistant directly in the BigQuery interface that can draft SQL, interpret schemas, and suggest insights to pursue.

This feature is built into the BigQuery console, so analysts can ask questions in natural language to generate SQL, get explanations of queries, and receive summaries of table structures. It also proposes joins, transformations, and potential lines of analysis, which makes it ideal for exploring large datasets such as 25 TB without moving data out of BigQuery.

Vertex AI Pipelines is designed for orchestrating and managing machine learning workflows and it does not provide an interactive AI assistant inside the BigQuery UI for drafting SQL or exploring schemas.

Google AI Studio is a web experience for prototyping prompts and working with the Gemini API outside of BigQuery, so it is not the integrated assistant within the BigQuery console.

Gemini Code Assist focuses on helping developers write and modernize application code in IDEs and cloud development environments, not on providing an embedded BigQuery assistant for SQL generation and schema exploration.

When a question asks for AI help directly inside a specific Google Cloud console, look for the product name paired with that service. The presence of the word in often signals the integrated experience such as in BigQuery or in Looker.

The correct option is Choice of foundation model and parameter size.

The latency target under 300 milliseconds combined with a hard limit of five GPUs makes the model selection the primary determinant of feasibility. A smaller or latency optimized model requires less compute for each token and can return results faster, which is essential when you cannot scale out beyond the quota. This choice also allows you to control generation parameters such as maximum output tokens to further reduce per request compute and time to first token.

Vertex AI Endpoint autoscaling settings primarily influence throughput and availability during traffic spikes. Autoscaling cannot exceed the regional GPU quota and it does not reduce the compute required for a single inference, so it will not directly solve a strict per request latency target under these constraints.

Cloud Firestore document schema affects how application data is organized and retrieved, which can influence database query performance. It does not materially change the core inference compute path of a generative model, so it is not the lever that determines whether responses can be generated within 300 milliseconds given the GPU limit.

When a scenario combines tight latency targets with strict resource quotas, first look for levers that reduce per request compute such as choosing a smaller model and limiting tokens rather than thinking about autoscaling or storage design.

The correct choice is Tensor Processing Units TPUs and Graphics Processing Units GPUs. These are specialized AI accelerators that deliver highly parallel numerical computation and interconnect bandwidth that enable distributed training across many workers within tight time constraints.

These accelerators are designed to perform large scale linear algebra operations efficiently and to scale out across many nodes for synchronous training. This aligns with the need to run multiple training experiments across 256 workers and complete them within 72 hours.

Cloud Spanner and Bigtable is incorrect because these are managed database services used for transactional consistency and low latency data access. They are not compute accelerators and cannot perform model training.

Vertex AI Training and Dataflow is incorrect because these are managed services that orchestrate training jobs and data processing. They depend on underlying hardware and can attach accelerators but they are not the accelerators themselves.

Virtual Private Cloud and Cloud Load Balancing is incorrect because these are networking services used for connectivity and traffic distribution. They do not provide specialized compute for model training.

When a question emphasizes highly parallel training across many workers and strict time limits, map the requirement to specialized accelerators rather than general services. Identify whether the option is actual hardware or only orchestration, storage, or networking.

The correct option is JSON (JavaScript Object Notation).

JSON (JavaScript Object Notation) natively represents hierarchical structures using objects and arrays, so a customer profile can contain a nested list of orders where each order includes a SKU and a timestamp without flattening. This makes parsing, validation, and downstream processing more reliable and efficient. It aligns well with large weekly files and allows straightforward ingestion into analytics systems that support nested and repeated data, which reduces the fragile reconciliation work that the team experiences with flat files.

BigQuery is not a data format and is a data warehouse service. While it can ingest and query nested data and works well with files such as JSON (JavaScript Object Notation), it is not something you choose as the file format for your source exports.

CSV is a flat, delimited text format that does not natively preserve nested structures. Representing a list of orders inside each customer record requires custom delimiters or multiple files, which increases fragility and complexity during extraction and reconciliation.

PNG is an image format and is not suitable for storing structured or hierarchical data.

When a scenario emphasizes nested or hierarchical records, choose a format that preserves structure such as JSON or Avro. Verify whether an option is a file format or a service so you do not select a product name when the question asks for a format.

The correct option is Conversational Insights.

It is designed to automatically analyze recorded calls and chat transcripts after interactions complete. It groups topics, scores sentiment, and surfaces emerging trends so leaders can understand drivers of dissatisfaction without manual review. It scales to handle large monthly volumes and provides conversation clustering and insights dashboards that focus on complaint discovery and theme tracking.

Vertex AI Pipelines orchestrates machine learning workflows such as data preparation, training, and deployment. It does not provide built in conversation analytics for transcripts or features like topic grouping, sentiment analysis, and trend detection.

Agent Assist focuses on real time assistance during live calls and chats with suggestions and knowledge retrieval for agents. It is not intended for post interaction batch analysis across thousands of transcripts.

Conversational Agents refers to building virtual agents with Dialogflow to automate customer interactions. It is for creating bots rather than extracting insights from completed conversations.

Match keywords to capabilities. Phrases like after interactions finish, topic grouping, sentiment, and trends point to Insights, while real time suggestions point to Agent Assist and build a bot points to Dialogflow.

The correct option is It is an external tool the agent calls to retrieve up to date recipe data that it does not inherently contain.

In this architecture the cuisine catalog API is invoked by the agent to fetch fresh and authoritative recipe details that the foundation model does not hold in its parameters. The agent uses the external tool at runtime to gather current results from the third party source and then summarizes them for the user.

It represents a prompt engineering pattern that steers the model’s reasoning and formatting is incorrect because prompt patterns guide how the model reasons and formats outputs through instructions in the prompt while the catalog API provides data through an external call.

It serves as a memory layer that stores the agent’s dialogue history for context retention is incorrect because a memory layer preserves conversation state or past context while the catalog API supplies new third party content rather than storing dialogue history.

It belongs to the foundation model’s static pretraining knowledge that is not changeable at runtime is incorrect because pretraining knowledge is fixed during training while the API returns dynamic information that is retrieved on demand.

When you see the agent calling an API for fresh or specific facts, identify it as an external tool rather than memory, a prompt pattern, or the model’s pretraining.

The correct option is Vertex AI Feature Store service.

Feature Store centralizes the definition and storage of machine learning features so teams can ingest, version, discover, and reuse them consistently. It provides an offline store for training and an online store for low latency serving so the same feature definitions are used in both paths. This reduces training and serving skew and speeds up model development because approved features can be reused across projects.

Dataplex focuses on data governance and lakehouse management with capabilities for cataloging, lineage, and data quality. It does not manage machine learning features nor provide an online feature serving layer.

Vertex AI Model Registry manages models, versions, lineage, and approvals, and it integrates with deployment targets. It does not store or serve features for training or online prediction.

Vertex AI Pipelines orchestrates reproducible machine learning workflows and automation. It does not centralize features or provide online feature serving.

Map keywords to the right service. When you see features that must be reused across training and online serving think Feature Store. When you see models and versions think Model Registry and when you see workflow orchestration think Pipelines.

All exam questions come from Cameron McKenzie’s Generative AI Practice Exams Udemy course and certificationexams.pro

The correct answer is Systemic bias in the images used to train the model.

A consistent skew toward one gender across many gender neutral prompts indicates that the model learned biased associations from its dataset. When the same pattern appears repeatedly across a large and varied set of prompts, the most direct explanation is that the distribution of examples in the training corpus overrepresents certain demographics and underrepresents others. The generator reproduces these learned associations even when the prompts are neutral, which points back to the data the model saw during training.

An elevated temperature used during sampling is incorrect because temperature mainly controls randomness and diversity in outputs. Higher temperature makes results less deterministic but it does not reliably push outputs toward one demographic across many prompts. A persistent directional skew is not explained by sampling randomness.

A small context window for prompts is incorrect because context window size limits how much input text a model can consider, which can truncate or omit details. It does not inherently create a demographic bias that consistently favors one group across many neutral prompts.

Limited model interpretability is incorrect because interpretability describes how well we can understand a model’s decisions. It does not cause a model to produce biased outputs. Lack of interpretability can make bias harder to diagnose, but it is not the root cause of the skew.

Look for a systematic pattern across many prompts. If outputs consistently skew in one direction regardless of prompt wording, think training data bias rather than tuning parameters or model explainability.

The correct choice is Vertex AI Agent Builder.

Vertex AI Agent Builder is built for production generative AI agents that perform multi step reasoning and tool use. It supports function calling and extensions so the assistant can call partner and internal APIs for live inventory, discount checks, and shipping estimates. It manages multi turn state and memory so it can ask clarifying questions and then orchestrate the end to end checkout flow. It also provides enterprise features such as grounding, authentication, safety controls, evaluation, monitoring, and straightforward deployment which are essential for a reliable shopping assistant.

Dialogflow CX focuses on intent driven task bots and slot filling and while it can call webhooks it does not provide native generative planning and multi tool orchestration needed for complex multi item shopping. For this scenario the generative agent capabilities in Vertex AI Agent Builder are the better fit.

Google AI Studio is a prototyping and prompt design environment and it is not a managed runtime for production agents with connectors, tool execution, evaluation, and monitoring. It is useful for quick experiments but it does not deliver full checkout orchestration across APIs.

Gemini API with custom Python code and no agent framework would require you to build conversation state, tool routing, safety, authentication, and reliability on your own which increases effort and risk. The managed orchestration in Vertex AI Agent Builder addresses these needs out of the box.

When a scenario emphasizes production readiness, multi step reasoning, and tool or API use, choose the managed agent platform. Map classic intent bots to Dialogflow CX and quick prompt trials to AI Studio, and avoid raw APIs unless the question asks for custom build tradeoffs.

The correct option is Few shot prompting.

The prompt includes labeled examples of how to classify sentiment and then asks the model to classify a new post. Providing a couple of demonstrations within the same prompt teaches the model the pattern to follow, which is exactly how this technique works. The analyst shows the model what a Positive and a Negative example look like, then requests the sentiment for a Neutral case, so the model infers the format and the decision boundaries from the given demonstrations.

Role prompting is not being used because the prompt does not set a role or an identity like instructing the model to act as a sentiment analyst. It instead relies on examples to guide behavior.

Prompt chaining is not being used because there is no sequence of dependent prompts where the output of one step becomes the input to another. This is a single prompt with embedded examples.

Zero shot prompting is not being used because the prompt does not ask for a classification without examples. It explicitly includes examples, which makes it the opposite of zero shot.

When deciding between zero shot and few shot, scan the prompt for explicit examples of inputs and outputs. If examples are present inside the prompt, it is few shot. If the model is asked to perform a task with no examples, it is zero shot.

The correct option is Vertex AI Studio.

It provides a no code web experience for prompt design and text generation with Google foundation models. Non technical users can turn a short list of keywords into polished product descriptions by iteratively crafting prompts, using built in templates, and reviewing samples in the browser. The UI supports fast prototyping within days and requires no programming while still giving access to state of the art generative models.

Vertex AI Workbench is a managed Jupyter notebook environment intended for coding in Python. It targets data scientists and engineers and does not offer a no code path for non technical users to quickly build text generators.

Cloud TPUs are specialized hardware for training and serving deep learning models. They are infrastructure resources that require engineering effort and code and they are not an end user tool for prompt based content creation.

Comprehensive MLOps capabilities for pipeline orchestration focuses on automating ML workflows and CI and CD with pipelines. This is useful for production operations but it does not help non technical users create product descriptions without writing code.

When a scenario emphasizes no code and non technical users who need quick results, choose Vertex AI Studio. Mentions of notebooks point to Workbench, accelerators point to TPUs, and pipelines point to MLOps features.

The correct option is Multimodal foundation model for video generation.

This multimodal model can accept and align text, audio, and images, then synthesize a coherent video that reflects the combined context. It is trained to fuse different modalities and to generate temporally consistent frames guided by prompts and references.

Vision and language model is not correct because it typically focuses on images and text for tasks like captioning or visual question answering. It does not integrate audio and it is not designed to generate full videos.

Unimodal Large Language Model is not correct because it processes only text. It cannot combine multiple input modalities or render video content.

When a question mentions multiple input types such as text audio and images, look for the word multimodal and confirm that the output modality matches the task such as video for video generation.

The correct option is First maps to a Data Lake and second maps to a Data Warehouse and third maps to a Database.

The first need describes hundreds of terabytes of raw images, short videos, and HTTP logs, which are best placed in a Data Lake. This paradigm is built for large volumes of unstructured and semi structured data and offers cost effective and elastic storage that can feed downstream processing and analytics. On Google Cloud, Cloud Storage commonly implements this pattern with durable object storage and lifecycle management.

The second need calls for complex analysis over seven years of cleaned and structured order history to uncover trends, which is exactly what a Data Warehouse is designed for. A warehouse supports analytical SQL at scale with columnar storage, partitioning, clustering, and the ability to efficiently scan and aggregate across large datasets. In Google Cloud, BigQuery is the standard warehouse for this use case.

The third need requires sub second retrieval of a single customer’s current order status, which fits a Database that supports transactional reads and indexed point lookups. On Google Cloud this is typically served by Cloud SQL, Firestore, or Spanner depending on the required consistency model, relational features, and scale.

First maps to a Data Warehouse and second maps to a Data Lake and third maps to a Database is incorrect because it reverses the first two paradigms. Raw files do not belong in an analytical warehouse and curated historical analytics do not belong in a file oriented repository. Even though the third part uses a database, the overall mapping does not match the workloads.

First maps to a Data Lake and second maps to a Database and third maps to a Data Warehouse is incorrect because the second need is analytical at scale and not an operational lookup store, and the third need requires fast transactional access rather than an analytical engine.

First maps to a Database and second maps to a Data Warehouse and third maps to a Data Lake is incorrect because the first need involves massive unstructured assets that do not fit a transactional system, and the third need is an operational lookup which does not fit a file based repository.

Scan the scenario for workload clues. Unstructured files and massive logs point to a data lake. Multi year trend analysis over cleaned tables points to a data warehouse. Single record lookups with tight latency targets point to a database.

The correct answer is Implement grounding with retrieval so answers are limited to an approved source of company policies.

This approach anchors responses to vetted policy documents so the model cites and uses only company approved content. By retrieving the official policy context at inference time and constraining the answer to that context, the assistant is far less likely to invent a discount that does not exist. It directly reduces financial exposure because unsupported offers are filtered out when they are not present in the retrieved policy sources.

Use multi step prompting to decompose user questions before answering improves reasoning clarity for complex requests, yet it does not enforce that answers come from authoritative sources. Without grounding, the assistant can still invent discounts and create the same liability.

Vertex AI Safety Filters mitigate harmful or sensitive content, but they are not designed to verify pricing or policy accuracy. They do not stop benign sounding yet financially risky hallucinations like a fabricated discount.

Fine tune the model on a larger set of generic support conversations may change style or coverage, but it does not guarantee factual adherence to current policies. It can even amplify prior mistakes and will not reliably prevent invented discounts unless it is paired with retrieval from the official policy corpus.

When the risk is wrong facts about company rules, look for solutions that constrain outputs to approved sources such as grounding or retrieval. Recognize that safety filters target harmful content while they do not ensure factuality.

The correct option is Performing a manual review and redaction pass over tens of millions of source files before uploading them to Cloud Storage. This is not recommended because it does not scale, it introduces human error, and it slows delivery in a high volume environment where automated controls are available and more reliable.

For sensitive datasets at scale, you should use automated data discovery and masking such as Cloud DLP to scan, classify, and redact sensitive fields. You should pair this with strong access controls, encryption, and perimeter protections so that the risk is mitigated through consistent and repeatable policy rather than through labor intensive processes. Automated pipelines also help keep pace with continuous updates to source content which a manual pass cannot match.

Turning on Cloud Audit Logs to capture search requests and data access events for later compliance review is a recommended practice because it provides traceability for administrative and data access operations and supports investigations and compliance reporting. You can route logs to BigQuery or Cloud Storage to meet retention requirements.

Applying VPC Service Controls perimeters around Cloud Storage and BigQuery datasets that feed the search index is recommended because it adds a strong defense against data exfiltration by restricting where protected resources can be accessed from and by limiting interactions with external services.

Enforcing access with Identity and Access Management policies that restrict which staff groups can query the application aligns with the principle of least privilege. You can use IAM roles and group based assignments, and you can add IAM Conditions to narrow access based on attributes so that only authorized users can query sensitive content.

When a question asks which action is not recommended, look for approaches that are heavy on manual effort and do not scale, and prefer automated controls such as DLP, IAM, and service perimeters.

The correct option is Data Preparation.

The scenario describes data spread across legacy repositories with inconsistent regional formats and many missing attributes. The biggest initial effort will be to profile the sources, design unified schemas, standardize regional formats like dates, currencies, or locales, resolve duplicates, impute or otherwise handle missing values, and establish validation and lineage. Building reliable ingestion and transformation pipelines and performing feature engineering will take the most time and resources before any model can be trained effectively.

Model Training is not the primary bottleneck because once a clean and well structured dataset exists, training can usually be iterated quickly and scaled efficiently, while the quality of results depends most on the prior preparation work.

Model Management involves experiment tracking, versioning, governance, and monitoring which are important for ongoing operations, yet these activities do not address the fundamental need to clean and transform messy data at project start.

Model Deployment focuses on serving, integration, latency, and reliability concerns after a model is trained, so it does not require the greatest initial investment when the core challenge is preparing fragmented and inconsistent data.

When a question highlights fragmented sources, inconsistent formats, or missing fields, recognize that data preparation is usually the largest early effort and select the lifecycle stage that fixes data quality before modeling.

The correct option is Google AI Pro.

This subscription unlocks access to the most capable Gemini models and provides premium features along with higher usage limits compared to the free tier. It is designed to give power users enhanced capabilities and priority features without requiring them to set up a full cloud billing environment.

Vertex AI is a managed machine learning platform on Google Cloud and you pay for the services you consume, yet it is not a simple subscription that unlocks premium Gemini usage limits for an assistant experience. It focuses on building and deploying models and applications rather than offering a consumer style premium plan.

Google AI Studio is a browser based tool for prototyping prompts and obtaining API keys for the Gemini API. It is not a paid subscription that raises usage limits for end users and it serves as a development environment rather than a premium access plan.

Gemini for Workspace Business is a paid add on for Google Workspace that enables Gemini features in apps like Gmail and Docs. It is not the offering positioned as the premium access plan to the most capable Gemini models for general assistant usage and API rate increases.

When a question mentions a paid subscription with higher usage limits and premium model access, look for the specific subscription brand rather than a platform or a tool. Platforms and tools enable development, while subscriptions unlock premium capabilities.

All exam questions come from Cameron McKenzie’s Generative AI Practice Exams Udemy course and certificationexams.pro

The correct option is Safety settings.

Safety settings directly control content moderation for Vertex AI generative models. They let you set strict thresholds for harm categories such as toxicity, hate speech, harassment, sexual content, and violence. For a helper aimed at children, you would configure stringent block thresholds so the model filters or blocks any responses that could be unsafe, even if that leads to simpler or less creative output. This is the only parameter family designed specifically to enforce safety policies.

Top k tunes how many of the highest probability tokens the model considers when sampling and it affects diversity rather than safety. Lowering or raising it does not reliably prevent harmful or inappropriate content.

Output length controls the maximum number of tokens returned and it can truncate responses but it does not moderate content. Shorter replies can still contain unsafe material.

Temperature adjusts randomness and creativity and a lower value can make output more deterministic, yet it does not enforce content filters. Harmful content can still appear without dedicated safety controls.

When a scenario emphasizes safety or toxicity prevention choose safety controls over creativity controls. Parameters like temperature and top k shape style, while safety settings enforce moderation policies.

The correct option is Gemini for Google Workspace.

This product adds generative assistance directly inside Gmail and Google Meet so the user can draft emails with Help me write and get meeting summaries and notes without leaving the apps they already use. It meets the requirement for built in capabilities and does not require creating or integrating a custom solution.

Vertex AI Agent Builder is designed for building custom enterprise agents and virtual assistants that require configuration and integration work. It is not the built in assistance inside Gmail or Meet that the scenario asks for.

The standalone Gemini app is a separate chat experience and is not the embedded experience inside Gmail and Google Meet that provides in context drafting and meeting summaries. This makes it a less suitable choice when the need is assistance within Workspace apps themselves.

Vertex AI Search focuses on enterprise search and retrieval across internal and external data sources. It does not provide native drafting in Gmail or automatic meeting summaries inside Google Meet.

Match the need to where the user wants the capability. If the question emphasizes help inside Gmail, Docs, Meet, or Chat and without building something new, choose the Workspace integrated option rather than developer tools or standalone apps.

The correct option is Imagen.

Imagen is Google�9s foundation model for text to image generation and it is designed to produce photorealistic or artistic images directly from prompts. It provides strong control over style and composition through prompt engineering and parameters in Vertex AI, which matches the studio�9s need to create hundreds of unique images quickly from short textual descriptions.

Gemma is a lightweight text model family that focuses on language generation and reasoning and it does not generate images from prompts, so it does not meet the requirement.

Veo is a generative video model for text to video creation, so it targets moving imagery rather than still images and therefore does not fit the use case.

Gemini is a multimodal large language model focused on reasoning and understanding across text, images, and other modalities, yet image generation in Vertex AI is provided by the Imagen family, so this option is not the best match for direct text to image creation.

Match the requested modality and output to the model family. For text to image generation with control over style and composition choose the dedicated image model rather than a general LLM or a video model.

The correct option is Maximizing engagement and conversion by making the assistant deliberately persuasive so users stay longer. This objective conflicts with Google�s AI Principles because they prioritize user agency, transparency, safety, and fairness over tactics that intentionally influence users to remain engaged.

The principles emphasize avoiding harm, avoiding the creation or reinforcement of unfair bias, rigorous safety testing, and accountability to people. Designing an assistant to keep users engaged through deliberate persuasion can undermine user autonomy and trust, which is not a responsible objective for this use case.

Providing a simple way for users to escalate to a human travel specialist when the assistant is unable to help aligns with accountability to people and human oversight. It ensures users can seek help, challenge outcomes, and receive meaningful support when the model falls short.

Reducing unfair bias so that itinerary and pricing recommendations are equitable for different users directly supports the principle of avoiding the creation or reinforcement of unfair bias. This is a core goal for responsible AI systems that make recommendations impacting user opportunities and costs.

Conducting rigorous safety evaluations to prevent harmful, toxic, or otherwise inappropriate responses aligns with building and testing for safety. This reduces the risk of harmful content and improves the reliability and trustworthiness of the assistant.

When a question asks for what is NOT aligned with responsible AI, look for objectives that sound manipulative or engagement driven. Favor options about safety, fairness, and human oversight since these usually match the principles.

The correct option is On premises license for Vertex AI Agent Builder.

On premises license for Vertex AI Agent Builder is not a built in capability because Agent Builder is a fully managed Google Cloud service that runs in Google Cloud. There is no on premises licensing or deployment model offered for this product.

Enterprise grounding with Vertex AI Search is a core capability because Agent Builder can ground model answers in your enterprise data by integrating with Vertex AI Search which improves accuracy and ensures responses are based on your content.

Integrated safety filters and content moderation is also built in because Vertex AI provides responsible AI safeguards that let you configure safety settings and moderation for prompts and responses to reduce harmful or unwanted content.

When a question asks for what is not built in, look for answers that imply a different deployment model such as on premises or a separate license. Most Vertex AI services are managed cloud offerings.

The correct option is Structured Data.

A relational database organizes information into tables with rows and columns and enforces a predefined schema. The attributes such as ClientId, FullName, StreetAddress, ContactEmail, and LastOrderDate fit fixed data types and consistent column definitions. This predictable tabular model and explicit schema are the hallmarks of this category.

Semi-structured Data is not the best fit because it typically uses flexible schemas with self-describing tags or keys such as JSON or XML rather than fixed columns, and it does not require a rigid table schema.

BigQuery is not a category of data. It is an analytics data warehouse service, and the question asks about the type of data rather than a specific product.

Unstructured Data does not have a predefined model and often includes free text, images, audio, or video. The described dataset clearly has defined fields and a strict schema, so this choice does not apply.

When a question highlights tables with rows and columns and a defined schema, choose Structured Data. References to flexible keys or formats like JSON point you toward semi-structured instead.

The correct option is Dependence on the model’s training data.

Dependence on the model’s training data explains that a model can only reliably answer questions about information that existed in and was represented by its pretraining and fine tuning corpora. Since Solara Components fine tuned the model using documents and sales data through 2023, it lacks knowledge of the device line released this quarter. This limitation leads the model to either produce incorrect guesses or admit it has no information because the required facts were never present in its training sources.

Hallucinations refer to fabricated or unsupported content even when the model appears confident. While inaccurate answers can look like Hallucinations, the scenario is most directly caused by the absence of new product data in the training set rather than the model inventing facts despite having relevant knowledge.

Out of distribution queries describe inputs that differ significantly from the data distribution seen during training. Questions about a recent product are still ordinary language queries, and the primary issue is missing knowledge rather than a fundamentally different input distribution. Therefore Out of distribution queries is not the best explanation here.

Unfair bias concerns systematic and unjust performance differences across groups or content. The scenario describes stale knowledge and not discriminatory behavior, so Unfair bias does not apply.

When a scenario emphasizes a knowledge cutoff or missing timeframe, prefer dependence on the model’s training data over hallucinations. Look for clues that the information did not exist in the data used to pretrain or fine tune the model.

The correct option is Google Vids.

Google Vids is an AI first video creation app inside Google Workspace that is designed to turn content from Docs, Sheets, and Slides into a structured storyboard. It can propose suitable stock footage based on the narrative and it can generate a voiceover from a written script, which matches the team�s needs for a concise and polished donor update.

Gemini in Google Drive assists with drafting and insights within files but it is not a dedicated video authoring tool that automatically builds storyboards, suggests stock footage, and produces voiceovers from Workspace inputs.

Vertex AI is a Google Cloud developer platform for building AI solutions and it is not a Workspace end user tool for assembling videos from Docs, Sheets, and Slides without custom development.

Google Meet video recordings captures meetings and presentations and it does not provide storyboard creation, stock footage recommendations, or script based voiceover generation.

When a question emphasizes an AI first tool inside Workspace, match the requirement to a specific Workspace app rather than a cloud platform service. Look for cues like storyboard creation, stock footage, and voiceover to identify Google Vids rather than more general AI assistants.

The correct option is AI accountability and explainability.

The scenario emphasizes that executives want clear ownership of each recommendation and that auditors must be able to understand how the assistant produced its suggestions. This is exactly what this priority addresses because accountability establishes who is responsible for model outputs and operational decisions, and explainability provides human understandable insights into how models arrive at their predictions through techniques such as feature attributions and transparent logs.

Under tight regulatory oversight, organizations need audit trails, traceability, and mechanisms to justify outcomes. This priority aligns with governance practices on Google Cloud such as using audit logs to track who did what and when, along with explainability tools in Vertex AI that help interpret model behavior so that stakeholders can trust and verify the assistant’s recommendations.

Data storage costs and efficiency is incorrect because the scenario is not about optimizing storage spend or capacity. It is focused on governance and transparency for model decisions.

Vertex AI Model Garden is incorrect because it is a catalog of models and solutions. It does not define a governance focus on who is responsible for recommendations or how predictions are explained.

Model inference speed and latency is incorrect because performance tuning for response time does not address the need for accountability or the ability for auditors to understand model reasoning.

Scan scenarios for words like auditors, traceability, transparency, and accountability. These usually point to explainability and governance rather than performance or cost.

The correct option is Google Vids, which acts as a video production assistant that assembles a storyboard from Docs and Drive, applies a consistent brand look, and generates a voiceover for export.

The app integrates with Google Docs and Drive to turn outlines and assets into a storyboard. It provides brand templates and brand kits so fonts colors and logos stay consistent. It can also generate a narration and export the finished video for sharing or further editing.

Gemini refers to the underlying AI model and assistants across Google experiences and it is not a dedicated Workspace video creation app that builds storyboards from Docs and Drive with brand kits and voiceover generation.

YouTube Create is a mobile video editing app for creators and it does not assemble storyboards from Google Docs or Drive nor does it offer Workspace brand kit automation and integrated AI voiceover as described.

When questions mention integration with Docs and Drive or brand kits, map them to the specific Workspace app rather than the underlying model or a consumer product. Look for clues like storyboard assembly and voiceover generation to pinpoint the right prebuilt tool.

All exam questions come from Cameron McKenzie’s Generative AI Practice Exams Udemy course and certificationexams.pro

The correct option is Optimize Alpha for low latency and optimize Beta for high throughput.

Alpha is a live voice assistant that translates speech during a call. Interactive audio translation must respond quickly for a natural conversation, so minimizing end to end delay is the priority. You can scale instances to handle more concurrent calls, yet the user experience depends most on how fast each request returns.

Beta is a nightly batch job that must finish tagging twelve million posts within eight hours. This is a throughput bound workload where you maximize parallelism and tokens or examples processed per second to meet the deadline. The latency of any single prediction matters less than total volume processed within the window.

Optimize Alpha for high throughput and optimize Beta for low latency is incorrect because it reverses the priorities. The assistant needs fast responses while the batch job needs aggregate processing capacity.

Optimize both Alpha and Beta for low latency is incorrect because the batch job does not need per item speed as much as it needs to process a very large volume within the time window.

Optimize both Alpha and Beta for high throughput is incorrect because the voice assistant requires quick responses to maintain conversational quality and cannot trade latency for only higher volume.

First classify the workload as interactive or batch. Interactive user facing flows favor low latency while large scheduled processing favors throughput.

The correct option is Chain-of-thought prompting because the prompt instructs the model to think step by step and write out its reasoning before giving the final answer, which directly describes this technique.

With chain-of-thought the model produces intermediate reasoning steps that break a multi step problem into smaller parts. This explicit reasoning often improves accuracy on math and logic problems where asking only for a final number can lead to errors.

ReAct prompting combines reasoning with actions such as using tools or interacting with an environment. The scenario does not involve taking actions or calling tools and only asks the model to write out its reasoning, so this is not a fit.

Few-shot prompting relies on providing example input output pairs to guide the model by analogy. The scenario modifies the instruction to elicit explicit reasoning rather than adding examples, so this is not few shot.

Tree-of-thought prompting encourages exploring multiple reasoning branches and performing a search over them. The scenario asks for a single linear explanation and not a branching exploration, so this does not match.

Look for wording like think step by step or explain your reasoning which often signals chain-of-thought. Mentions of examples suggest few-shot. References to tools or actions point to ReAct. Descriptions of exploring branches indicate tree-of-thought.

The correct choice is Use prebuilt RAG in Vertex AI Search to index and ground on their HR repository.

This option provides a managed retrieval augmented generation workflow that connects a generative model to private HR documents with very little custom code. It can ingest files into a data store, index and vectorize them, retrieve the most relevant passages, and ground answers with citations. It supports simple refresh workflows so updates every two weeks are picked up without rebuilding a custom stack. It integrates directly with Vertex AI models and lets the team focus on the assistant experience instead of infrastructure.

Build a custom vector store and retrieval stack on GKE from the ground up. This is possible but it conflicts with the need to minimize custom plumbing and to move quickly. It introduces operational burden for cluster management, scaling, security, and evaluation that is unnecessary for a small and periodically refreshed repository.

Use Document AI to extract text then call the model without a retrieval layer. This skips retrieval which is the core of a retrieval augmented generation pattern. It may extract text accurately, yet it does not provide search, indexing, or relevance ranking, and it will not keep answers grounded in the most current sources with citations.

Fine tune a foundation model on HR documents with no retrieval step. Fine tuning is not a substitute for retrieval and it does not update automatically when policies change. It risks outdated answers and does not provide source citations, which makes it a poor fit when strict grounding to the latest documents is required.

When a scenario stresses minimal custom work and fresh answers grounded in enterprise data, prefer a managed RAG service over building from scratch. Use fine tuning to adapt style or domain phrasing, and use Document AI when you need structured extraction rather than grounding.

The correct option is Diffusion model.

This family of models is the current standard for text to image synthesis because it starts from random noise and iteratively removes that noise to form a coherent image that follows a prompt. Diffusion models are well suited to capture complex scene structure and style, so they can render an original and coherent oil painting from a narrative description.

Generative Adversarial Network can create images, but it is less reliable for text conditioned generation and often struggles with prompt fidelity and training stability, so it is not the best fit here.

Large Language Model focuses on generating and understanding text rather than producing images, so it does not provide the core image synthesis capability needed.

Reinforcement learning model is a training paradigm for decision making with rewards and is not an image generator, so it does not meet the requirement for text to image synthesis.

Map the task to the output modality. For text to image tasks think of diffusion. For pure text generation think of LLMs. Eliminate options that describe training paradigms rather than generative architectures.

The correct option is Gemma.

Gemma is a lightweight open family of models that can run locally on consumer hardware. It supports offline use and keeps data on the device because inference can execute on the user machine without sending prompts or outputs to a remote service. This makes it well suited for strict on device deployment and privacy sensitive scenarios.

Gemini Pro is a cloud hosted multimodal model that you access through Google APIs and Vertex AI. It is not designed for strict on device execution and typically requires network access.

Chirp is a speech recognition model offered through the Speech to Text service. It targets cloud transcription use cases rather than private offline execution on user devices.

Imagen is an image generation model available as a managed service on Vertex AI. It is not intended for local offline deployment on end user hardware.

When a question emphasizes on device and offline look for lightweight or open models that can run locally rather than fully managed cloud hosted APIs.