20 Tough GCP Generative AI Leader Practice Exam Questions and Answers

To support real time in session personalization that adapts within about 30 seconds instead of showing a static top 50 list, which type of data is most critical to capture and process?

• ❏ A. Daily product sales aggregates

• ❏ B. Streaming user interaction events

• ❏ C. Rich item metadata

• ❏ D. Customer demographic profiles

Which GCP deployment would ensure a 95th percentile latency under 70 ms for users in Germany and at least 99.95 percent availability if a single zone fails?

• ❏ A. Deploy resources in two regions one in Frankfurt and one in Singapore

• ❏ B. Regional multi-zone deployment in Germany

• ❏ C. Deploy all components in a single zone within a German region

• ❏ D. Global HTTPS load balancer with a single German backend zone

Which phase describes a two week generative AI discovery sprint that focuses on brainstorming use cases, assessing feasibility, and estimating impact?

• ❏ A. Model fine tuning and optimization

• ❏ B. Use case ideation and ranking

• ❏ C. Vertex AI Studio pilot and experimentation

• ❏ D. Data readiness assessment

Over the past 60 days, click-through rates for a generative recommendation model have declined as the catalog and user preferences have changed. Which Google Cloud recommended practice should you implement to detect and address this performance degradation?

• ❏ A. Schedule retraining every 120 days with Vertex AI Pipelines

• ❏ B. Maintain strict model versioning

• ❏ C. Enable autoscaling on Vertex AI endpoints

• ❏ D. Continuous performance monitoring with data and concept drift alerts

Which Google Cloud product provides the enterprise foundation that unifies virtual agents, real time agent assist, and interaction analytics into a scalable contact center AI solution?

• ❏ A. Dialogflow CX

• ❏ B. Contact Center AI Insights

• ❏ C. Google Cloud Contact Center as a Service

• ❏ D. Vertex AI Agent Builder

Which data quality attribute describes a situation in which the same product has different identifiers in BigQuery and an external API, preventing the system from matching records and causing missed reorders?

• ❏ A. Accuracy

• ❏ B. Consistency

• ❏ C. Timeliness

• ❏ D. Completeness

Which Google Cloud service provides enterprise search with intent understanding and personalized suggestions for a catalog of approximately 250,000 products and integrates with web and mobile applications?

• ❏ A. Recommendations AI

• ❏ B. Vertex AI Search

• ❏ C. BigQuery

Which type of model generates images by progressively denoising random noise guided by a text prompt?

• ❏ A. Autoregressive Transformer

• ❏ B. Diffusion Model

• ❏ C. Generative Adversarial Network

• ❏ D. Variational Autoencoder

Which practice requires a human expert to review and approve AI outputs before they are used?

• ❏ A. Reinforcement Learning from Human Feedback

• ❏ B. Human in the loop review

• ❏ C. Prompt engineering

Which Google open foundation model is built on the Gemini research stack for code assistance and can be easily installed and run locally on a laptop with 12 GB of RAM?

• ❏ A. Gemini Pro

• ❏ B. Gemma

• ❏ C. Gemini Nano

My Generative AI Udemy Course has over 500 exam questions.

Which responsible AI principles ensure that automated credit decisions provide understandable reasoning and defensible outcomes?

• ❏ A. Reliability and Safety

• ❏ B. Explainability with Accountability

• ❏ C. Fairness and Privacy

Which advantage of Google Cloud’s generative AI portfolio provides vendor neutrality and supports open source libraries and bringing your own models from a catalog or registry?

• ❏ A. Comprehensive security and compliance

• ❏ B. Vertex AI AutoML

• ❏ C. Vertex AI Model Garden

• ❏ D. Open ecosystem with model and tool choice

Which model property enables a model to read and summarize a 150-page document in a single pass and produce a coherent result?

• ❏ A. Larger parameter count

• ❏ B. Adequate context length for the full document

• ❏ C. Lower temperature

An AI model performs well on common inputs but fails on rare and atypical cases. Which limitation is most likely responsible?

• ❏ A. Concept drift

• ❏ B. Rare edge cases outside the model’s training distribution

• ❏ C. Insufficient grounding context

• ❏ D. Hallucination

Which Google Cloud services offer automated PII discovery and classification with centralized governance to ensure PII is excluded from model training?

• ❏ A. Data Catalog and IAM

• ❏ B. Vertex AI Feature Store and Vertex AI Pipelines

• ❏ C. Cloud DLP and Dataplex

Which model performance characteristic should be prioritized for real-time camera text translation overlays that must respond in about 90 milliseconds?

• ❏ A. Longer context window

• ❏ B. Low per-request latency

• ❏ C. High throughput

In an AI demand forecasting scenario where data is spread across separate systems and locked in scanned PDFs that require substantial integration work, which data quality dimension is the primary limitation?

• ❏ A. Completeness

• ❏ B. Availability

• ❏ C. Timeliness

• ❏ D. Consistency

Which Google Cloud commitment ensures that enterprise data in Vertex AI remains private and is not used to train Google general purpose models?

• ❏ A. Confidential VMs

• ❏ B. Customer managed encryption keys in Cloud KMS

• ❏ C. Google Cloud enterprise data privacy commitment

• ❏ D. VPC Service Controls

Which sampling parameter restricts token selection to the smallest set whose cumulative probability meets a threshold such as 0.90?

• ❏ A. Temperature

• ❏ B. Nucleus top p sampling

• ❏ C. Frequency penalty

• ❏ D. Top-k sampling

Which translation capability maintains headings, clause numbering, tables, and overall page layout when translating documents between languages?

• ❏ A. Streaming translation for interactive conversations

• ❏ B. Text Translation API

• ❏ C. Layout-preserving document translation

• ❏ D. Domain adapted translation with a legal glossary

The correct option is Streaming user interaction events for real time in session personalization that adapts within about 30 seconds rather than showing a static top 50 list.

These events provide immediate behavioral signals such as clicks, views, add to cart actions and dwell time. They can be ingested and processed continuously so the personalization logic updates during the session. This captures fresh intent and context which is exactly what is needed to change recommendations in near real time.

Daily product sales aggregates are batch oriented and typically update once per day which is far too slow for in session adaptation and cannot reflect moment to moment behavior.

Rich item metadata improves relevance and understanding of items but it is mostly static and does not tell you what the user is doing right now within the session.

Customer demographic profiles can inform long term personalization but they change slowly and lack the immediacy required to react within tens of seconds during a session.

When you see real time or in session personalization, favor event streams that capture current user behavior over batch aggregates or static attributes.

The correct option is Regional multi-zone deployment in Germany.

This design keeps users close to the workloads which helps maintain p95 latency under 70 ms for German users. By distributing instances across multiple zones within the same region the system continues to serve traffic during a zone outage which meets the at least 99.95 percent availability requirement. Using regional instance groups and a regional or global frontend that prefers in-region backends allows health checks and failover to shift traffic between zones automatically without leaving the region.

Deploy resources in two regions one in Frankfurt and one in Singapore is not suited to a Germany focused latency goal because Singapore is far from Germany which can increase tail latency when traffic or data crosses regions. It also adds unnecessary complexity for replication and failover that does not help German users.

Deploy all components in a single zone within a German region cannot satisfy the availability requirement because a single zone failure would cause downtime and therefore cannot achieve at least 99.95 percent.

Global HTTPS load balancer with a single German backend zone does not meet the availability target because the backend exists in only one zone and a zone outage would interrupt service. While a global load balancer can route efficiently it does not make a single zone backend highly available.

Map the latency requirement to user geography and the availability target to failure domains. For a country specific audience place workloads in a nearby region and spread them across multiple zones. Remember that a global load balancer improves routing but cannot protect a single zone backend from outages.

The correct option is Use case ideation and ranking. This phase matches a two week generative AI discovery sprint because it centers on structured brainstorming, feasibility assessment, impact estimation, and prioritization so that the team can select the most promising use cases for subsequent prototyping.

In a discovery sprint you bring business and technical stakeholders together to generate a wide set of ideas and you rapidly evaluate them against criteria such as business value, user impact, technical feasibility, data availability, and risk. You use this information to score and rank the candidates which produces a short list for pilots and experiments. The output is a prioritized backlog and clear next steps rather than production models.

Model fine tuning and optimization is a later execution activity that occurs after a high value use case has been selected and data and evaluation criteria are defined. Fine tuning does not describe the two week discovery stage whose purpose is to decide what to test, not to optimize a model.

Vertex AI Studio pilot and experimentation refers to hands on prototyping and testing which typically follows the discovery sprint. Pilots validate assumptions and gather evidence, while the discovery sprint focuses on identifying and ranking ideas to decide which pilots to run.

Data readiness assessment is an important preparatory analysis that often complements discovery but it is narrower in scope. It evaluates data quality, availability, and governance, while the discovery sprint covers ideation, feasibility, and impact ranking across multiple potential use cases.

When you see a short timebox like two weeks with a focus on brainstorming, feasibility, and impact, map it to the discovery phase. Look for verbs like ideate, prioritize, and rank rather than build or tune.

The correct option is Continuous performance monitoring with data and concept drift alerts. This matches a situation where catalog attributes and user behavior have shifted over time and click-through has declined, so you need timely detection and automated signals to investigate and remediate.

This practice tracks live performance metrics such as click-through rate and surfaces changes in input distributions and target relationships. Data drift can occur as the catalog refreshes and user segments evolve, while concept drift emerges when the relationship between features and clicks changes. Vertex AI provides model monitoring that can raise alerts when thresholds are breached, and those alerts can drive incident review or trigger retraining workflows so you correct degradation quickly rather than on a fixed timetable.

Schedule retraining every 120 days with Vertex AI Pipelines is not the best response because a rigid schedule can miss rapid shifts or retrain when nothing has changed. Pipelines are useful to orchestrate retraining, but you should initiate it based on observed drift and performance signals.

Maintain strict model versioning improves traceability and rollback but it does not detect when quality falls nor does it tell you why. Without monitoring and alerts you will discover issues late and lack the signals needed to act promptly.

Enable autoscaling on Vertex AI endpoints addresses throughput and latency under variable traffic, yet it does not improve recommendation quality or detect drift. Scaling more replicas will not fix declining click-through that stems from changing data or concepts.

When a scenario describes metrics degrading over time or changing data, look for monitoring with drift alerts and action hooks rather than fixed retraining schedules or infrastructure tweaks.

The correct option is Google Cloud Contact Center as a Service.

This platform is designed to be the enterprise foundation for contact centers. It brings together virtual agents, real time agent assist, and interaction analytics under one unified solution so organizations can scale, manage, and secure their customer engagement across channels with native integrations and operational controls.

Dialogflow CX is focused on designing and managing conversational virtual agents and complex dialog flows. It does not provide the full contact center foundation or unify agent assist and analytics into a single enterprise platform.

Contact Center AI Insights delivers conversation analytics and quality management features. It is a component that analyzes interactions rather than the overarching platform that unifies virtual agents, agent assist, and analytics.

Vertex AI Agent Builder helps teams build generative and search grounded conversational agents. It is not a contact center platform and it does not serve as the enterprise foundation that integrates telephony, agent assist, and analytics for an end to end contact center solution.

When a question asks for a unifying enterprise foundation, look for a platform or as a service offering rather than a point product that handles only virtual agents or only analytics.

The correct option is Consistency.

This attribute addresses whether the same entity is represented in the same way across different systems. When a product has different identifiers in BigQuery and in an external API, the values do not align across sources. That lack of uniform representation prevents reliable joins or lookups, which leads to missed reorders even if each source is otherwise sound.

Accuracy focuses on how closely data reflects real-world truth. Each system might hold a correct identifier for its own context, yet the problem here arises from disagreement between systems rather than incorrect values in isolation.

Timeliness concerns whether data is up to date and available when needed. The scenario is about mismatched identifiers, not data that is late or stale.

Completeness measures whether all required fields or records are present. The issue is not missing data but conflicting identifiers that prevent matching.

When options reference data quality attributes, map the symptom to the dimension. Choose consistency for cross system mismatches, accuracy for wrong values, timeliness for late or stale data, and completeness for missing fields or rows.

The correct option is Vertex AI Search.

This service provides enterprise and retail grade search that understands user intent and context. It supports personalized ranking and suggestions across large product catalogs that can include around 250,000 items and more. It also offers APIs and components that integrate cleanly into web and mobile applications so teams can deliver high quality search experiences without building ranking and relevance from scratch.

Recommendations AI focuses on generating personalized product recommendations based on user behavior and catalog data. It is not a search engine and it does not provide intent based query understanding or full text relevance over a large product catalog.

BigQuery is a serverless data warehouse for analytics using SQL. It is not an application search service and it does not provide intent understanding, product ranking, or ready to use web and mobile search integration.

Look for cues like intent understanding, personalized ranking, and web and mobile integration to identify a search service. Map search to Vertex AI Search, recommendations to Recommendations AI, and analytics to BigQuery.

The correct option is Diffusion Model.

Diffusion Model fits the description because it starts from random noise and learns a reverse process that removes noise step by step to produce an image. When conditioned on a text prompt, the model uses the text representation to guide each denoising step so the emerging image aligns with the prompt. This iterative denoising under text guidance is the defining characteristic that the question is pointing to.

Autoregressive Transformer is not correct because it generates outputs by predicting the next token in a sequence rather than by progressively denoising noise. Even when adapted to images, it models pixels or tokens sequentially and does not perform the iterative denoising process described.

Generative Adversarial Network is not correct because it relies on a generator and discriminator trained adversarially and typically produces images in a single forward pass. It does not generate by iteratively denoising noise under text prompt guidance.

Variational Autoencoder is not correct because it samples from a latent space and decodes to an image in one or a few passes. It does not carry out the multi step denoising process that the question specifies.

When you read phrases like iteratively denoising noise or reverse diffusion you should immediately think of diffusion models. If the wording emphasizes one step generation or adversarial training then consider other families instead.

Only Human in the loop review is correct.

This practice places a knowledgeable person between the model output and its use. The human evaluates the AI result for quality and safety and then explicitly approves or rejects it before it reaches users or systems. This creates a deliberate control point that reduces risk and supports compliance and accountability.

Reinforcement Learning from Human Feedback is a model training approach where human preference data shapes the reward model and policy during fine tuning. It does not require a person to approve each individual output at inference time, so it is not a review gate before use.

Prompt engineering focuses on crafting inputs to guide the model toward better answers. It can improve responses but it does not introduce a mandatory human approval step before outputs are used.

When you see words like review, approve, or oversight, think about a workflow where a person must sign off before the AI output is used. Techniques that tune models or improve inputs usually do not guarantee human approval of each result.

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The correct option is Gemma.

Gemma is Google’s open family of lightweight models that are simple to install and can run on consumer hardware such as a laptop with around 12 GB of RAM. It is derived from the same research and technology as Gemini and includes a code oriented variant that provides code assistance.

Gemini Pro is a hosted proprietary Gemini model that you access through managed services like the Gemini API or Vertex AI. It is not an open foundation model and it is not intended to be installed and run locally on a laptop.

Gemini Nano is designed for on device inference on Android through AICore and it is not distributed as an open downloadable checkpoint for general laptop installation. It targets mobile devices rather than typical laptops.

When you see hints like open, runs locally, and modest RAM requirements, map them to the lightweight family built from Gemini research. If code help is mentioned, think about the code focused variant in that same family.

The correct option is Explainability with Accountability.

This principle focuses on making a model’s reasoning understandable so stakeholders can see why a particular outcome was produced. It also adds governance and oversight so there is a clear record of how decisions were made and who is responsible. Together these elements enable transparent rationale and auditable processes that support defensible automated credit decisions.

Reliability and Safety centers on robustness, safe operation, and risk mitigation, which are important qualities, but it does not specifically provide traceable reasoning or defensible explanations for individual credit decisions.

Fairness and Privacy addresses bias reduction and protection of personal information, which are essential requirements, but they do not by themselves explain how a decision was reached or ensure accountable review of that rationale.

When a question emphasizes understandable reasoning and defensible results, map it to principles that provide explanations and accountability rather than general robustness or data protection.

The correct option is Open ecosystem with model and tool choice.

This option is right because it describes the portfolio advantage that lets teams avoid lock-in and choose from open source libraries and frameworks. It also supports bringing your own models that you can register and govern in a registry or discover and select from a catalog. This approach enables you to mix first party, third party, and community models while using the tools and runtimes that fit your workflow.

Comprehensive security and compliance is important for governance and risk management, yet it does not address vendor neutrality or the ability to adopt open source libraries and bring your own models.

Vertex AI AutoML focuses on training and tuning models from your data with managed workflows. It is not the portfolio-level advantage that emphasizes open source choice and broad bring your own model support.

Vertex AI Model Garden is a catalog of models that you can browse and use. It is a specific service rather than the overarching advantage of vendor neutrality and broad tool and model choice across catalogs and registries.

Look for keywords like vendor neutrality, open source, and bring your own model. These usually point to a portfolio-wide capability rather than a single product feature.

The correct option is Adequate context length for the full document.

To read and summarize a 150 page document in one pass the model must have a context window large enough to hold the entire tokenized input. A sufficiently large context window lets the model attend to all parts of the document at once, which supports a coherent and globally consistent summary. If the document exceeds the window then content must be chunked or truncated, which prevents a true single pass and risks losing cross references.

Larger parameter count is not the property that determines how much text the model can accept in one request. It influences capability and output quality, yet it does not change the maximum context window, so it cannot ensure a single pass over a very long document.

Lower temperature controls randomness and style and can make outputs more deterministic, yet it has no effect on input length. It does not enable the model to ingest an entire 150 page document in a single request.

When a question emphasizes processing long inputs in one pass, look for options that reference context window or context length and avoid choosing model size or temperature settings.

The correct option is Rare edge cases outside the model’s training distribution.

When a model has not seen certain patterns during training it will often fail on rare edge cases because these inputs fall out of distribution relative to the training data. This is a data coverage problem where the model generalizes well to common patterns yet struggles with the long tail. Addressing this usually requires collecting more diverse examples, targeted augmentation, or techniques to detect and handle out of distribution inputs.

Concept drift describes a situation where the relationship between features and labels changes over time, which is a temporal shift in the target concept. The scenario instead points to uncommon inputs that were not represented in training rather than a changing concept.

Insufficient grounding context pertains to generative systems that lack retrieved or trusted knowledge at inference and therefore cannot support their responses. The issue in the question is about training distribution coverage rather than missing retrieval context.

Hallucination occurs when a generative model produces fluent but fabricated content. The described behavior is a failure on unusual inputs rather than inventing unsupported information.

Match symptoms to root causes. Look for long tail or out of distribution wording to indicate missing data coverage, for concept drift when performance degrades over time, and for generative hallucination when content is fabricated without support.

The correct answer is Cloud DLP and Dataplex. Together they provide automated PII discovery and classification with centralized governance so sensitive data is identified and policies can prevent it from being used in model training.

Cloud DLP performs content aware inspection and data profiling across data sources such as BigQuery and Cloud Storage. It automatically detects common PII using built in infoType detectors and produces profiles and sensitivity classifications that can drive de identification or exclusion rules for datasets that feed ML pipelines.

Dataplex delivers centralized governance across data lakes and warehouses and unifies cataloging, policy tagging, data quality and zone based controls. When you integrate Cloud DLP classifications with Dataplex, tags and policies can be propagated and enforced so training pipelines automatically exclude assets labeled as PII.

Data Catalog and IAM is not sufficient because Data Catalog provides metadata cataloging and tagging but it does not perform automated PII discovery, and IAM controls who can access resources rather than scanning and classifying data content. In addition, the standalone Data Catalog experience has been unified into Dataplex which is the governance layer expected for this need on newer exams.

Vertex AI Feature Store and Vertex AI Pipelines do not discover or classify PII and they do not provide centralized data governance. Vertex AI Feature Store manages and serves features and Vertex AI Pipelines orchestrates workflows, and they rely on upstream services such as Cloud DLP and Dataplex to ensure sensitive data is identified and excluded before training.

Map each requirement to its native service. For automated PII discovery think of Cloud DLP and for centralized governance think of Dataplex. Tools like access control or ML orchestration do not perform data classification.

The correct option is Low per-request latency. A real-time camera translation overlay that must respond in about 90 milliseconds needs each individual request to complete as quickly as possible so minimizing the time from input to output is the key performance goal.

Prioritizing low latency ensures the translated text appears almost immediately on the camera view which preserves a smooth and natural user experience. Optimizing for quick per-request completion reduces user-perceived delay and meets the tight response budget that real-time overlays require.

Longer context window is about how much input a model can consider in a single call and it does not make the response arrive faster. In many cases increasing context length adds processing work which can increase latency, so it is not the priority for a strict 90 millisecond target.

High throughput focuses on how many requests can be served per second across users which is valuable for scale but it does not guarantee that a single request completes quickly. Tuning for higher throughput can increase batching and queueing which can worsen per-request latency and that conflicts with a real-time requirement.

When a question mentions real-time or a tight target like ~100 ms map that to prioritizing low per-request latency. Reserve throughput for many concurrent users and context window for long inputs rather than speed.

The correct option is Availability.

Data scattered across isolated systems and locked in scanned PDFs is difficult to access and use. The data may exist but if it cannot be readily obtained by the forecasting pipeline without major integration or extraction work then the model is constrained by access. This dimension focuses on whether data is obtainable when needed for analysis which is exactly what limits an AI demand forecasting effort in this scenario.

Completeness is not the central problem because the information likely exists somewhere across the sources. The blocker is that it is hard to reach and operationalize rather than that essential fields are missing.

Timeliness is less relevant here because the chief issue is not the freshness or update frequency of the data. The inability to access and integrate the data dominates any concerns about latency.

Consistency may become a concern after integration but it is not the primary limitation described. When data cannot be accessed or unified in the first place you cannot even evaluate or enforce uniform definitions and formats.

Look for clues that the data exists but cannot be reached or used without heavy effort. Map those to Availability rather than to freshness or field coverage.

The correct option is Google Cloud enterprise data privacy commitment.

This commitment is a policy that applies across Google Cloud services and it states that customer content is not used to train Google general purpose models without permission. It explicitly covers data processed by Vertex AI so it ensures that your enterprise data remains private and is not used for training unless you opt in.

Confidential VMs protect data in use through memory encryption and other confidential computing features. These controls improve confidentiality of computation but they do not govern whether Google uses your data to train general purpose models.

Customer managed encryption keys in Cloud KMS let you encrypt data with your own keys and control key lifecycle. This provides cryptographic control over access but it does not determine data usage for training Google models.

VPC Service Controls create service perimeters that reduce data exfiltration risk from Google managed services. This is a network boundary control and it does not provide a commitment about whether data can be used to train general purpose models.

When a question asks whether your data can be used to train Google models look for a policy or contractual commitment rather than technical security controls like encryption or network boundaries.

The correct option is Nucleus top p sampling.

This method selects from the smallest set of tokens whose cumulative probability mass reaches a chosen threshold p. With p set to 0.90 it dynamically includes tokens in descending probability until their combined mass is at least 90 percent. This directly matches the description of limiting selection to a minimal set that achieves the probability threshold.

Temperature changes the sharpness of the probability distribution to make outputs more or less random. It does not create a cutoff based on cumulative probability and therefore does not define a minimal probability mass set.

Frequency penalty lowers the likelihood of repeating tokens based on prior usage. It does not implement selection by cumulative probability and does not determine a candidate set size.

Top k sampling restricts choices to a fixed number of the most likely tokens which is k. It does not use a probability mass threshold like 0.90 and therefore does not ensure a minimal cumulative probability set.

When a question mentions a probability threshold such as 0.90 think of top p and contrast it with top k which fixes the number of candidates while temperature only rescales the distribution.

The correct option is Layout-preserving document translation because it translates whole files while keeping headings, clause numbering, tables, and the overall page layout intact.

This capability processes documents in supported formats such as DOCX, PPTX, or PDF and returns a translated document that mirrors the original structure. It maintains elements like styles, lists, headers and footers, and pagination so the output looks like the source but in the target language.

The option Streaming translation for interactive conversations is designed for real time audio or conversational scenarios and it does not operate on documents and does not preserve page formatting.

The option Text Translation API focuses on translating plain text strings and even when used with HTML it returns translated text rather than a reconstructed file so it does not maintain complex document layout such as tables and automatic numbering.

The option Domain adapted translation with a legal glossary addresses terminology control and domain consistency rather than document structure so it cannot by itself preserve headings, tables, or page layout.

When a question mentions preserving layout or details like headings and tables think of document translation features rather than text or streaming services.