GCP Associate Data Practitioner Exam Dumps and Braindumps

AlpineMart ingests about 30 million customer profile records each day from Cloud Storage and wants to run data quality checks upstream so that only valid rows reach BigQuery. Which approach should they use?

• ❏ A. Define check constraints in the BigQuery table schema to enforce data quality rules

• ❏ B. Build an Apache Beam pipeline on Dataflow that applies validation rules and routes invalid records away before loading into BigQuery

• ❏ C. Use Cloud Functions to validate each record individually as it is received

• ❏ D. Ingest all data into a staging dataset in BigQuery and perform cleaning queries afterward

How can you classify 500000 BigQuery rows in table foo.bar as positive neutral or negative using a Google pretrained language model with the least setup while staying in BigQuery?

• ❏ A. Train a BigQuery ML sentiment model on labeled data

• ❏ B. BigQuery remote function calling Cloud Functions for Vertex AI

• ❏ C. BigQuery ML remote model to Vertex AI pretrained text model

• ❏ D. Cloud Natural Language API via Dataflow batch scoring

HarborPay, a fintech startup based in Switzerland, must meet data residency obligations that require all sensitive customer records to remain within Swiss territory. In BigQuery, which feature ensures that data is stored only in a chosen geographic location such as europe-west6 in Zurich?

• ❏ A. Authorized views

• ❏ B. BigQuery dataset location

• ❏ C. VPC Service Controls

• ❏ D. Table partitioning

Which Google Cloud service lets you design and automate a low code ETL that ingests from on premises to Cloud Storage every 3 minutes, transforms data, and loads it into BigQuery?

• ❏ A. Cloud Composer

• ❏ B. Cloud Data Fusion

• ❏ C. Cloud Dataflow

A data platform team at scrumtuous.com is designing a pipeline that must branch into different processing steps after inspecting the contents of each new file, and they want robust branching, looping, error handling, and stateful orchestration across multiple Google Cloud services. Which service should they use to manage this control flow?

• ❏ A. Cloud Composer

• ❏ B. Dataflow

• ❏ C. Cloud Workflows

• ❏ D. Cloud Functions

How can you score sentiment on text stored in BigQuery with an existing pretrained model and return predictions directly in SQL without moving data?

• ❏ A. BigQuery remote function to Cloud Natural Language API

• ❏ B. BigQuery ML remote model to Vertex AI

• ❏ C. Dataflow pipeline with Python sentiment and load results

The analytics group at Maple Harbor Logistics creates temporary tables in BigQuery for exploratory work and they want each table to be removed automatically 90 days after it is created so that costs and clutter are kept under control without manual cleanup. Which BigQuery capability should they configure to delete entire tables on that schedule?

• ❏ A. Partition expiration

• ❏ B. Table expiration time

• ❏ C. BigQuery time travel retention

• ❏ D. Object lifecycle management

Which file format lets BigQuery scan the least data and run faster when queries select only a few columns from very large datasets?

• ❏ A. ORC

• ❏ B. Parquet

• ❏ C. Avro

• ❏ D. JSON

A business intelligence analyst at scrumtuous.com needs to show store revenue by country and state in Looker Studio for the last 90 days so executives can quickly see geographic patterns. Which visualization would be most effective for location-based analysis?

• ❏ A. Pie chart

• ❏ B. Geo map

• ❏ C. Treemap

• ❏ D. Line chart

Which consideration would most clearly indicate choosing Cloud SQL rather than BigQuery for an application database?

• ❏ A. Require globally distributed writes with strong consistency across regions

• ❏ B. Run joins and aggregations across 250 TB of historical events

• ❏ C. Support 5,000 concurrent ACID transactions with single digit millisecond reads and writes

All questions are from my GCP Data Practitioner Udemy course and certificationexams.pro

You manage a telemetry ingestion workflow for a smart building platform at Northwind Utilities where IoT devices publish readings to Pub/Sub and a subscriber streams them into a BigQuery table named telemetry_analytics.device_events_v3. Analysts are seeing gaps in the last few hours of data in BigQuery while Pub/Sub publish metrics indicate that producers are successfully sending messages. You need to find out why some records are not landing in BigQuery and trace failures through the subscription path. What should you do?

• ❏ A. Reduce the subscription acknowledgment deadline to 5 seconds to speed up redelivery and processing

• ❏ B. Configure a dead-letter topic on the subscription and inspect dead-lettered messages to identify processing failures

• ❏ C. Enable message ordering with an ordering key so events are delivered to the subscriber sequentially

• ❏ D. Increase the Pub/Sub topic message retention to 168 hours and create a new subscription to replay recent data

Which managed Google Cloud service provides DAG based orchestration with dependencies, scheduling every 30 minutes and nightly, and failure retries across multiple services?

• ❏ A. Workflows

• ❏ B. Cloud Composer

• ❏ C. Cloud Dataflow

• ❏ D. Cloud Scheduler

Cedar Leaf Publishing accumulates about 18 TB of PDFs, scanned images, and plain text files each month and needs to ingest this unstructured content into Google Cloud for downstream analytics. The team wants a fully managed pipeline that can automatically extract metadata and support simple transformations before the data is analyzed. Which approach and services should they use to prepare and load the data efficiently?

• ❏ A. Load the files directly into BigQuery using a custom loader that stores both the raw blobs and their metadata

• ❏ B. Keep the files in Cloud Storage and run Dataproc with Spark jobs to parse metadata and then write outputs to BigQuery

• ❏ C. Stage the content in Cloud Storage and use Cloud Dataflow to derive metadata and apply basic transforms, then load structured results into BigQuery

• ❏ D. Store the files in Cloud Storage and trigger Cloud Functions to parse metadata and save it to BigQuery

Which BigQuery feature lets you share a live filtered subset of specific columns and rows with a partner without creating copies?

• ❏ A. Analytics Hub

• ❏ B. Authorized views in BigQuery

• ❏ C. Row-level access policies

• ❏ D. BigQuery policy tags

A digital analytics lead at example.com needs to map how users progress through key touchpoints such as ad clicks, product views, trial signups and purchases in order to understand the most common paths and dropoffs. In Looker, which visualization best represents the ordered flow of steps in these journeys?

• ❏ A. Bar chart

• ❏ B. Path analysis

• ❏ C. Pie chart

• ❏ D. Scatter plot

How should you ingest high-volume raw telemetry into BigQuery with end-to-end latency under 60 seconds while allowing transformations and enrichment later for analytics?

• ❏ A. Batch loads to BigQuery on a schedule

• ❏ B. Streaming ETL in Dataflow

• ❏ C. BigQuery ELT after ingest

• ❏ D. Dataproc Spark ETL then load to BigQuery

A global marketplace at example.com needs to store customer profile photos and downloadable receipts that are read frequently by shoppers in North America, Europe, and Asia. The team requires very low read latency for a worldwide audience, effortless scaling as data volume grows, and a highly available platform. Which Google Cloud service should they implement?

• ❏ A. Cloud Spanner

• ❏ B. Cloud Storage with a multi-region bucket

• ❏ C. Cloud SQL

• ❏ D. Compute Engine with a Persistent Disk

In Looker you must compute gross revenue as unit_price times units_sold and you do not have Develop permission. How can you add this calculation to your analysis?

• ❏ A. Create a custom field in Explore

• ❏ B. Create an Explore table calculation for unit_price times units_sold

• ❏ C. Add a revenue measure in the LookML view and commit

• ❏ D. Use SQL Runner to edit the SQL to multiply the fields

At scrumtuous.com the analytics team receives about 25 files each day in a mix of CSV and JSON with inconsistent field formats, and they need to clean and standardize the records with real time previews before loading curated tables into BigQuery. Which Google Cloud service offers the most intuitive visual interface for this data preparation task?

• ❏ A. Dataproc

• ❏ B. Dataprep by Trifacta

• ❏ C. Dataflow

• ❏ D. BigQuery

Which Cloud SQL feature ensures the database remains available during a zonal outage in a region?

• ❏ A. Automatic backups

• ❏ B. Read replicas

• ❏ C. High availability configuration

• ❏ D. Cross-region replica

A data analyst at mcnz.com needs to forecast monthly customer support ticket counts for the next 9 months using 4 years of historical data that shows clear seasonal cycles. Which BigQuery ML model type should they select to build this forecast?

• ❏ A. AUTOML_REGRESSOR

• ❏ B. LINEAR_REG

• ❏ C. ARIMA_PLUS

• ❏ D. BOOSTED_TREE_REGRESSOR

Which Google Cloud service lets you design reusable managed pipelines to standardize formats, remove duplicates, and handle missing values for CSV and JSON data before analysis?

• ❏ A. Dataproc

• ❏ B. Google Cloud Data Fusion

• ❏ C. BigQuery

• ❏ D. Dataflow

A travel bookings company named Meridian Trips operates several GCP projects and needs to attribute BigQuery slot consumption to users and job types across all projects for the last 90 days. Which method provides the most granular per query visibility across projects?

• ❏ A. Export BigQuery audit logs to BigQuery

• ❏ B. Information Schema views

• ❏ C. Cloud Monitoring metrics

• ❏ D. Query Execution Details

Which Google Cloud Dataflow metric shows that a streaming pipeline is falling behind incoming events rather than just using more resources?

• ❏ A. Pub/Sub subscription backlog

• ❏ B. System lag

• ❏ C. Autoscaling worker count

• ❏ D. CPU utilization

A regional media firm that operates mcnz.com needs to schedule a recurring sync that runs every 24 hours and moves only newly created or modified log files from Amazon S3 into Cloud Storage. Which Google Cloud tool should they use to accomplish this?

• ❏ A. Transfer Appliance

• ❏ B. Dataflow

• ❏ C. Storage Transfer Service

• ❏ D. BigQuery Data Transfer Service

The correct option is Build an Apache Beam pipeline on Dataflow that applies validation rules and routes invalid records away before loading into BigQuery.

This approach lets you implement row level validation in a scalable pipeline that reads from Cloud Storage and writes only clean data to BigQuery. With Apache Beam running on Dataflow you can apply transforms that validate each record, branch valid and invalid outputs, and send bad rows to a dead letter sink for review while committing only valid rows to BigQuery. It scales to tens of millions of records per day and reduces downstream cost and operational risk by preventing bad data from landing in the warehouse.

Define check constraints in the BigQuery table schema to enforce data quality rules is incorrect because BigQuery table constraints are informational and are not enforced to block writes, so they cannot guarantee that invalid rows are rejected before they land in the table.

Use Cloud Functions to validate each record individually as it is received is not appropriate for processing about 30 million records per day from Cloud Storage because it leads to excessive per record invocations and operational overhead and does not provide the efficient parallel batch processing and routing capabilities that a managed Beam pipeline provides.

Ingest all data into a staging dataset in BigQuery and perform cleaning queries afterward contradicts the requirement to run checks upstream so that only valid rows reach BigQuery, since this pattern loads bad data first and cleans later which is ELT rather than ETL.

When a question says only valid data should reach BigQuery, think ETL with an upstream pipeline. Prefer Dataflow with Apache Beam for large volumes and for branching valid and invalid records with a dead letter sink, and avoid per request services for bulk file processing.

The correct option is BigQuery ML remote model to Vertex AI pretrained text model.

This approach lets you stay in BigQuery and use SQL while delegating the classification task to a Google pretrained language model in Vertex AI. You create a remote model once and then run ML.PREDICT over the 500000 rows. This minimizes setup because there is no need to build a serving endpoint or manage pipelines and it scales with BigQuery so you can classify large tables efficiently.

Train a BigQuery ML sentiment model on labeled data is not the least setup because it requires collecting labeled examples and running a training job. The question asks to use a Google pretrained model rather than training your own.

BigQuery remote function calling Cloud Functions for Vertex AI adds unnecessary components and operational overhead. Remote functions require deploying and maintaining a Cloud Function and custom code which is more complex than using a remote model that BigQuery ML supports natively.

Cloud Natural Language API via Dataflow batch scoring leaves BigQuery and introduces a Dataflow pipeline and API orchestration. This increases setup and complexity and does not meet the requirement to stay in BigQuery with minimal configuration.

When a question asks for the least setup while staying in BigQuery and using a Google pretrained model, think of BigQuery ML remote models to Vertex AI. If an option introduces extra infrastructure or training, it is usually not the simplest path.

The correct option is BigQuery dataset location.

When you create a dataset in BigQuery you choose the geographic location for that dataset such as europe-west6 in Zurich and BigQuery stores the table data only in that location. This setting enforces data residency because jobs must run in the same location as the data and cross location queries are not allowed. You retain control over any data copies so data remains within the selected region unless you explicitly move it.

Authorized views define a way to share query results while restricting access to underlying tables, yet they do not control where the data is physically stored.

VPC Service Controls reduce the risk of data exfiltration by creating service perimeters around Google Cloud services, but they do not determine the storage location of BigQuery data.

Table partitioning organizes data within a table by time or by an integer range to improve performance and cost, and it does not set or constrain the geographic storage location.

When a question asks about keeping data in a specific country or region, look for a setting that is chosen at dataset or bucket creation. Features that manage access or network perimeters do not change where data is stored.

The correct option is Cloud Data Fusion because it lets you visually design and automate a low code ETL that can ingest from on premises systems into Cloud Storage every 3 minutes, apply transformations, and load the results into BigQuery.

This service provides a visual pipeline designer, prebuilt connectors for Cloud Storage and BigQuery, and built in transformations. You can configure time based schedules with cron expressions to run every three minutes, and it can reach on premises sources through supported networking and connectors, which covers the full ingest, transform, and load workflow described.

Cloud Composer is primarily a workflow orchestrator based on Apache Airflow. It excels at scheduling and coordinating tasks, yet it does not offer a visual low code ETL engine or built in data transformations, so you would still need another processing service to perform the ETL work.

Cloud Dataflow is a managed data processing service for Apache Beam pipelines. It is powerful for batch and streaming ETL but it requires code or templates rather than a visual low code experience, so it does not meet the low code requirement in the question.

When you see keywords like low code, visual pipelines, and built in connectors to Cloud Storage and BigQuery, think of Cloud Data Fusion. If the emphasis is on workflow orchestration rather than transformations, that points to Cloud Composer, while heavy code based data processing suggests Cloud Dataflow.

The correct option is Cloud Workflows.

This service provides serverless and stateful orchestration across Google Cloud services and it is built for rich control flow. It supports conditionals and branching, loops, parallel steps, retries, timeouts, and exception handling, which matches the need to inspect each new file and decide the next step. A workflow can be triggered by a Cloud Storage event through Eventarc, then it can call downstream services and branch based on file metadata or inspection results while maintaining centralized state and error handling.

Cloud Composer is managed Apache Airflow and it can orchestrate complex pipelines, yet it is optimized for scheduled DAGs rather than per file event driven invocations. It introduces environment management and cluster overhead that is unnecessary for a lightweight serverless control flow, so it is not the most direct choice for many small file driven branches.

Dataflow is a data processing engine for batch and streaming transformations and not a control plane for coordinating multiple services. While a pipeline can branch within a job, it does not provide the cross service state management and workflow level error handling required for this scenario.

Cloud Functions runs single functions in response to events, but it does not offer native multi step workflows with built in branching, loops, retries, and centralized state. Achieving this would require custom orchestration code, which is more fragile and harder to operate.

Scan for keywords that imply service orchestration, branching, and retries across multiple Google Cloud services. That points to Workflows, while scheduled DAGs suggest Composer and large scale data transformations suggest Dataflow. Single event handlers suggest Cloud Functions.

The correct option is BigQuery ML remote model to Vertex AI. This lets you invoke a pretrained sentiment model hosted on Vertex AI from BigQuery and return predictions with standard SQL while keeping the data in place.

With a BigQuery ML remote model you register a model object in BigQuery that points to a Vertex AI endpoint for online prediction. You can then use ML.PREDICT against your table of text so the scoring happens during query execution and the results are returned directly in SQL. This satisfies the need to use an existing pretrained model and to avoid exporting or copying data.

BigQuery remote function to Cloud Natural Language API is not the best fit. While remote functions can call external services, they are not integrated with BigQuery ML model objects or ML.PREDICT, they rely on per row HTTP calls with tighter quotas, and they add operational complexity. The question emphasizes in database prediction with SQL against an existing model, which the remote model integration provides natively.

Dataflow pipeline with Python sentiment and load results requires building an external pipeline and moving data for processing, then writing results back. That does not meet the requirement to return predictions directly in SQL without moving data.

When a question highlights without moving data and directly in SQL, think of BigQuery ML features such as remote models to Vertex AI rather than external functions or ETL pipelines.

The correct option is Table expiration time because it automatically deletes entire BigQuery tables after a specified interval such as 90 days from creation.

This setting can be applied per table or set as a dataset default so that all newly created tables inherit the same expiration. When the expiration is reached the table and its data are removed without manual intervention which is exactly what you need for temporary exploratory tables that should disappear after 90 days.

Partition expiration manages the lifetime of individual partitions in a partitioned table rather than removing the whole table. Unpartitioned tables are not affected and even in partitioned tables the table itself remains which does not meet the requirement to delete entire tables.

BigQuery time travel retention controls how long historical versions of data are available for query or restore. It does not schedule or perform deletion of tables based on their age.

Object lifecycle management applies to Cloud Storage objects and buckets rather than BigQuery tables. It cannot be used to delete BigQuery tables on a schedule.

When the requirement is to delete whole tables on a schedule in BigQuery look for table expiration time or the dataset default table expiration. If

the option mentions partition expiration or time travel or targets Cloud Storage then it is not for deleting tables.

The correct option is Parquet. It is a columnar storage format that lets BigQuery read only the columns you query so it scans fewer bytes and can run faster on very wide tables.

Parquet supports column pruning and predicate pushdown, and it stores data by column with efficient compression. BigQuery can project only the referenced columns from Parquet files, which reduces I O and often lowers both query time and cost for selective queries.

ORC is also a columnar format and it can reduce scanned data in many systems. However, BigQuery commonly emphasizes Parquet for external data interoperability and performance, so ORC is not the best choice in this context.

Avro is row oriented, so BigQuery cannot skip unneeded columns and must read entire records. This increases scanned bytes and slows queries that select only a few columns.

JSON is a text based row format that is verbose and not columnar. Queries that read a small number of fields still require scanning most of the data, which is inefficient for very large datasets.

If a question highlights fewer bytes scanned for a few columns then think columnar. Choose Parquet over row oriented formats like Avro or JSON when running selective queries in BigQuery.

The correct option is Geo map.

A Geo map is purpose built for spatial analysis and lets you plot metrics like revenue by geographic dimensions such as country and state. It makes patterns immediately visible because it encodes values using filled regions or bubbles on an actual map, and a date control for the last 90 days can be applied so executives can scan for geographic trends quickly.

Pie chart is not effective here because it lacks any spatial context and becomes hard to read with many geographic categories such as multiple states or countries. It does not reveal where values occur on a map.

Treemap can show hierarchical proportions but it does not convey geographic location or spatial relationships. It would not help executives see patterns across a real map of countries and states.

Line chart focuses on trends over time rather than distribution across locations. While it can show revenue over the last 90 days, it does not provide geographic insights by country and state.

When you see a need for location-based insights, think in terms of a map. Choose a map visualization when the question mentions countries, states, regions, or geographic patterns, and reserve other charts for category breakdowns or time trends.

The correct option is Support 5,000 concurrent ACID transactions with single digit millisecond reads and writes.

This points to Cloud SQL because it is a managed relational database for OLTP workloads that require ACID compliance and low latency reads and writes. It provides the transactional guarantees and indexing you need for high concurrency request patterns and can deliver single digit millisecond operations within a region when sized and tuned appropriately. BigQuery is designed for analytical scans rather than per row transactional access, so it is not a fit for this requirement.

Require globally distributed writes with strong consistency across regions is not an indicator for Cloud SQL. That requirement aligns with Cloud Spanner since it offers multi region strong consistency and horizontally scalable writes. Cloud SQL does not support globally distributed strongly consistent writes and BigQuery is not a transactional database.

Run joins and aggregations across 250 TB of historical events describes a classic BigQuery use case. BigQuery excels at interactive SQL over very large datasets and columnar analytics, while Cloud SQL is not intended to store or query hundreds of terabytes efficiently.

Map workload patterns to the right service. If you see ACID and low latency OLTP reads and writes, think Cloud SQL or possibly Spanner for global scale. If you see large scale analytical scans over many terabytes, think BigQuery.

The correct option is Configure a dead-letter topic on the subscription and inspect dead-lettered messages to identify processing failures.

Using a dead letter topic on the Pub/Sub subscription lets you capture messages that the subscriber cannot successfully process and acknowledge after the configured delivery attempts. Inspecting messages in the dead letter topic reveals the payload and useful attributes such as delivery attempt count, which you can correlate with subscriber and BigQuery logs to determine where the failure occurs in the ingestion and write path. This also preserves the problematic events so that after you resolve the issue you can reprocess them.

Reduce the subscription acknowledgment deadline to 5 seconds to speed up redelivery and processing is incorrect because shortening the acknowledgment deadline does not explain the missing records and can increase unnecessary redeliveries and timeouts if the subscriber needs more time to write to BigQuery.

Enable message ordering with an ordering key so events are delivered to the subscriber sequentially is incorrect because ordering does not help diagnose or trace failures and can introduce head of line blocking that delays newer messages without addressing the missing data.

Increase the Pub/Sub topic message retention to 168 hours and create a new subscription to replay recent data is incorrect because creating a new subscription does not retroactively receive past messages by default and retention settings help with replay only when used with seek and retained acknowledged messages. This does not show why the subscriber failed to land records in BigQuery.

When data is missing at the sink but publishers look healthy, think about the subscription path. Add a dead letter topic to surface failures, check delivery attempt attributes, and correlate with subscriber logs before attempting replays.

The correct option is Cloud Composer because it provides DAG based orchestration with task dependencies, flexible schedules such as every 30 minutes and nightly, and built in retry logic across workflows that call many Google Cloud services.

This managed Apache Airflow service lets you define Directed Acyclic Graphs where tasks depend on one another so you can express complex pipelines cleanly. You can schedule DAGs with cron expressions to run every 30 minutes or once nightly and you can configure retries and alerts. It also offers rich integrations through operators for many Google Cloud products which makes it well suited for cross service orchestration.

Workflows orchestrates services with a step based YAML or JSON definition and it supports retries and error handling, however it is not DAG based and it does not include built in scheduling so you would need an external trigger such as Cloud Scheduler.

Cloud Dataflow is a managed service for batch and streaming data processing pipelines rather than a general purpose orchestrator of multiple services, and it does not provide DAG based workflow scheduling or cross service dependency management.

Cloud Scheduler provides cron style triggers for single jobs, and while it can fire HTTP targets or Pub Sub it does not offer multi step DAGs, dependencies, or workflow level retries across tasks and services.

When a question mentions DAGs, task dependencies, and varied schedules, map it to the managed Airflow service and choose Cloud Composer.

The correct option is Stage the content in Cloud Storage and use Cloud Dataflow to derive metadata and apply basic transforms, then load structured results into BigQuery. This delivers a fully managed pipeline that scales to monthly volumes of unstructured files and feeds analytics-ready tables in BigQuery.

Cloud Storage is the right landing zone for PDFs, images, and text because it provides durable and cost effective object storage for large files. Cloud Dataflow is a serverless data processing service that can read from Cloud Storage, extract metadata, and perform simple transformations in batch, then write curated records to BigQuery. This approach reduces operational overhead, supports autoscaling, and integrates natively with BigQuery for downstream analysis.

With Dataflow you can build Apache Beam pipelines that parse object attributes like filenames and sizes and you can incorporate libraries to extract additional metadata from documents and images when needed. Writing the structured results to BigQuery produces well defined schemas that analysts can query efficiently.

Load the files directly into BigQuery using a custom loader that stores both the raw blobs and their metadata is not appropriate because BigQuery is an analytics warehouse rather than a binary object store. It does not ingest arbitrary files as raw blobs and forcing binaries into tables would be inefficient and would still not provide the managed extraction and transformation the team needs.

Keep the files in Cloud Storage and run Dataproc with Spark jobs to parse metadata and then write outputs to BigQuery can work but it is not the best fit for a fully managed pipeline. Dataproc involves cluster setup and job orchestration responsibilities, whereas Dataflow removes cluster management for this kind of batch ETL and simplifies operations.

Store the files in Cloud Storage and trigger Cloud Functions to parse metadata and save it to BigQuery is not ideal for 18 TB per month because functions are optimized for short lived event driven tasks and have execution and resource limits. Coordinating large scale parsing and batching with functions is complex and does not provide the robust, managed data pipeline capabilities that Dataflow offers.

When you see fully managed pipelines for large unstructured files, think Cloud Storage for staging, Cloud Dataflow for transforms, and BigQuery for analytics. Be cautious of options that store raw files in BigQuery or that rely on Cloud Functions for heavy batch processing.

The correct option is Authorized views in BigQuery.

This feature lets you define a SQL view that returns only the columns and rows you choose, then you grant the partner access to the view while keeping the underlying tables restricted. Each query reads current table data, so the subset is live and no copies are created.

Analytics Hub focuses on publishing and subscribing to dataset listings across organizations. It does not on its own enforce row or column filtering for a shared dataset, and you would still need a view or security controls to achieve selective exposure.

Row-level access policies restrict which rows a user can read on a table, yet they do not hide columns and they still require granting table access to the partner. The question asks for both row and column filtering as a live share without copying, which a single view solves more directly.

BigQuery policy tags provide column-level security by tagging sensitive columns and controlling access through Data Catalog, but they do not filter rows and they are not a sharing mechanism by themselves.

When a scenario mentions sharing a live filtered subset without copies, map it to a view that you grant access to rather than the underlying tables.

The correct option is Path analysis because it best represents an ordered flow of steps in multi‑step user journeys.

This visualization maps sequences of events such as moving from an ad click to a product view to a trial signup and then to a purchase. It highlights the most common routes and where users drop off between steps, and it shows the relative frequency of transitions so teams can understand how users progress through the funnel.

Bar chart compares categories or totals and does not show the ordered transitions between steps in a journey.

Pie chart shows parts of a whole at a single point in time and cannot represent sequences or branching paths.

Scatter plot displays relationships between two numeric variables and does not encode order or flow across multiple steps.

If a scenario emphasizes ordered steps, paths, or drop-offs then choose a path or flow style visualization rather than charts that compare categories or show proportions.

All questions are from my GCP Data Practitioner Udemy course and certificationexams.pro

The correct option is BigQuery ELT after ingest.

This approach streams raw telemetry directly into BigQuery to meet the under 60 second requirement, then performs transformations and enrichment inside BigQuery using SQL, scheduled queries, or Dataform when needed. It decouples ingestion from transformation which preserves the original data for flexible reprocessing and supports very high throughput using the Storage Write API or a Pub/Sub BigQuery subscription. This design minimizes end to end latency while enabling scalable and governed analytics later.

Batch loads to BigQuery on a schedule is not suitable because batch windows and job scheduling introduce minutes or longer of delay which fails the sub minute latency requirement.

Streaming ETL in Dataflow performs transformations before landing results which couples processing with ingestion and can add latency and operational complexity. It also does not naturally keep the raw data in BigQuery for later enrichment unless you build a parallel path.

Dataproc Spark ETL then load to BigQuery typically relies on batch or micro batch processing and cluster spin up which makes meeting an under 60 second end to end target unlikely and adds operational overhead.

When a question stresses very low latency and the need to transform data later, prefer streaming ingest into BigQuery and perform ELT with BigQuery SQL, scheduled queries, or Dataform. Batch pipelines rarely fit sub minute streaming requirements.

The correct option is Cloud Storage with a multi-region bucket.

This service is designed for storing and serving static assets such as customer profile photos and downloadable receipts. It automatically replicates objects across multiple regions within the selected multi-region and serves content from locations close to users which provides very low read latency for a worldwide audience. It scales transparently as data volume grows and offers high availability and durability that fit a global marketplace. It can also be paired with Cloud CDN to push content to edge locations for even faster delivery if needed.

Cloud Spanner is a globally distributed relational database for transactional workloads and it is not an object storage service. Using it to store and serve images and receipts would be inefficient and costly and it would not deliver the simplicity and throughput of an object store.

Cloud SQL is a managed relational database that is regionally deployed and scales primarily vertically. It is not appropriate for hosting and delivering static files worldwide with very low latency and high throughput.

Compute Engine with a Persistent Disk provides block storage attached to virtual machines that is zonal or regional and not globally distributed. It would require building and operating your own highly available file serving layer and would not provide effortless scaling for a global audience.

When the workload is static files read frequently across continents, prioritize Cloud Storage multi-region and add Cloud CDN when edge caching is needed for even lower latency.

The correct option is Create an Explore table calculation for unit_price times units_sold. This lets you compute gross revenue directly in the Explore without modifying LookML and it works even when you do not have Develop permission.

This method evaluates on the result set of your Explore and multiplies the two fields to produce a new column for gross revenue. It is created entirely in the analysis layer so it does not require any model changes and can be saved in a Look or dashboard for reuse.

Create a custom field in Explore is not the best choice for multiplying two base fields for gross revenue in this scenario because custom fields are constrained by available aggregation choices and modeling context. The question asks for a straightforward result set calculation that you can create ad hoc in the Explore, which this approach addresses more directly.

Add a revenue measure in the LookML view and commit is not possible because you do not have Develop permission, which is required to modify LookML and commit changes to the model.

Use SQL Runner to edit the SQL to multiply the fields is not appropriate because SQL Runner is a separate ad hoc SQL tool. It does not change the SQL generated by an Explore and it does not let you alter model generated SQL for your analysis.

If you cannot change LookML, first look for table calculations in Explore to create ad hoc formulas across fields without needing Develop permission.

The correct option is Dataprep by Trifacta because it provides an intuitive visual data preparation experience with real time previews for cleaning and standardizing mixed CSV and JSON files before publishing curated tables to BigQuery.

Dataprep by Trifacta offers a browser based no code interface that profiles data, suggests transformations, validates changes instantly, and lets you interactively fix inconsistent field formats. It connects to Cloud Storage and can write directly to BigQuery which matches the need to prepare files and load curated tables. Dataprep by Trifacta has undergone product transitions under the Trifacta and Alteryx brands so it may be less prominent on newer exams, yet it is the service historically designed for this visual wrangling workflow on Google Cloud.

Dataproc is a managed Spark and Hadoop service that is well suited for code driven ETL on clusters. It does not provide a point and click data wrangling UI with immediate previews, so it is not the most intuitive choice for this task.

Dataflow is a stream and batch processing service where you build pipelines in Java or Python or SQL. It is powerful and scalable, yet it is not a visual data preparation tool with interactive previews for fixing inconsistent fields.

BigQuery is a serverless data warehouse that excels at SQL analytics and can transform data with SQL, but it is not a dedicated visual data preparation interface for profiling and standardizing files before load.

Look for phrases like interactive and visual interface and real time preview and wrangling mixed file formats. These usually indicate a data preparation UI rather than code based pipelines or a warehouse.

The correct option is High availability configuration because it keeps a Cloud SQL database available during a zonal outage within a region through automatic failover to a standby in another zone.

With HA configured, Cloud SQL provisions a primary and a standby in different zones in the same region and continuously monitors health. If the zone hosting the primary becomes unavailable, the service performs automatic failover to the standby. Data is maintained on regional persistent storage so the promotion is fast and does not require a time consuming restore.

Automatic backups protect data and support point in time recovery, but they do not provide continuity during an outage. You must restore from a backup which takes time and results in downtime.

Read replicas are intended for scaling read workloads and use asynchronous replication. They do not provide automatic failover for availability during a zonal outage and promotion is a manual operation that can risk data loss.

Cross-region replica is designed for disaster recovery across regions and for read scaling. It does not deliver automatic failover for a zonal failure within the same region and is optimized for regional outages rather than zonal events.

If a question mentions a zonal outage in a region, map it to Cloud SQL high availability with automatic failover rather than backups or replicas.

The correct option is ARIMA_PLUS.

This model in BigQuery ML is purpose built for univariate time series forecasting. It can automatically detect and model seasonal patterns from several years of historical data and it supports multi step horizons such as a nine month forecast. With monthly ticket counts that show clear seasonal cycles, this model is a natural fit because it handles seasonality, trend, and holiday effects when configured, and it automates hyperparameter selection to produce robust forecasts.

AUTOML_REGRESSOR is a general tabular regression approach and does not natively model temporal autocorrelation or seasonality. You would need significant feature engineering to approximate time series behavior and it still would not match the purpose built capabilities of the time series model. This option also reflects the older AutoML Tables naming that has transitioned to Vertex AI and is less likely to be emphasized on newer exams.

LINEAR_REG fits a straight line to predict a continuous target and it does not account for lagged dependencies or seasonal cycles in a single time series. It is therefore not suitable for monthly forecasting with clear seasonality.

BOOSTED_TREE_REGRESSOR is a powerful general purpose regressor for tabular data, but it does not inherently capture time based autocorrelation or seasonal structure without extensive feature engineering. It is not the recommended choice when a dedicated time series forecasting model is available.

When you see seasonality and a clear forecasting horizon in BigQuery ML questions, favor the dedicated time series model rather than general regressors.

The correct option is Google Cloud Data Fusion.

Google Cloud Data Fusion is a fully managed data integration service that provides a visual interface to build reusable pipelines. It includes prebuilt transformations to standardize CSV and JSON formats, remove duplicates, and handle missing values. You can also schedule and monitor these pipelines with built in lineage and operational features so they prepare data before analysis.

Dataproc focuses on running Spark and Hadoop on managed clusters and it requires you to write and operate your own jobs rather than design reusable managed pipelines for data preparation.

BigQuery is a serverless data warehouse for analytics and while SQL can clean data it is not a managed pipeline designer for standardized ingestion and transformation.

Dataflow runs Apache Beam pipelines and offers powerful batch and streaming execution, yet it expects pipelines to be built in code or templates and it does not provide a visual low code pipeline authoring experience for wrangling and standardization.

Look for phrases like reusable managed pipelines and a visual designer for data preparation before analysis. These point to Data Fusion rather than compute engines or the analytics warehouse.

The correct option is Information Schema views.

These views expose per query job metadata that includes the submitting user and the job type along with detailed statistics such as total slot milliseconds and reservation information. You can query organization or region scoped job views to cover many projects in one place and you can stay within the last 90 days because job metadata is retained long enough to meet that window. You can also use the jobs timeline views when you need time series detail of slot usage for individual queries.

Export BigQuery audit logs to BigQuery does not provide precise slot consumption per query and typically lacks fields like total slot milliseconds, so it cannot deliver the most granular attribution to users and job types across projects.

Cloud Monitoring metrics aggregate slot usage at the reservation or project level and are not designed for per query analysis, and default retention often does not cover a full 90 day window for detailed breakdowns.

Query Execution Details is a console view for a single query and is not a programmatic or cross project solution for analyzing many queries over the last 90 days.

When a question asks for per queryacross projects and mentions a time window, think about querying BigQuery job metadata in Information Schema rather than logs or monitoring.

The correct option is System lag.

This metric measures how far event processing is behind the head of the stream and represents the time the pipeline would need to catch up if no new data arrived. It directly indicates backlog within the streaming pipeline rather than resource pressure and is the clearest signal that the job is falling behind incoming events.

Pub/Sub subscription backlog counts undelivered messages in the subscription and it reflects publisher and subscriber behavior outside the pipeline. A backlog can exist for reasons other than the pipeline falling behind and it does not precisely measure processing delay inside Dataflow.

Autoscaling worker count shows how many workers are running and it is a capacity signal rather than a timeliness metric. More or fewer workers do not tell you whether the job is behind on processing events.

CPU utilization shows resource usage on workers and high or low CPU can occur whether the pipeline is keeping up or lagging. It does not quantify end to end processing delay.

When distinguishing pipeline health, favor metrics that express time such as System lag and be cautious of resource or capacity indicators like CPU or worker count because they describe load rather than backlog.

The correct option is Storage Transfer Service because it provides scheduled recurring transfers and supports copying only new or modified objects from Amazon S3 into Cloud Storage.

This service natively connects to S3 as a source and Cloud Storage as a destination. You can create a daily job and enable synchronization so it transfers only files that have changed since the last run which meets the requirement to move only newly created or modified log files. It is fully managed and handles scheduling, incremental detection, and retries without custom code.

Transfer Appliance is designed for offline bulk data migration with a physical device and it is not intended for an automated daily sync from S3 to Cloud Storage.

Dataflow is a data processing service for building ETL pipelines and it does not provide a managed incremental file transfer from S3 to Cloud Storage on a simple schedule.

BigQuery Data Transfer Service moves data into BigQuery rather than into Cloud Storage and therefore it does not satisfy the requirement.

Look for the keywords scheduled, incremental, and cross cloud then match them to the fully managed transfer service that supports S3 to Cloud Storage with daily jobs.