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Google Associate Data Practitioner Exam Practice Questions, practice question pack
GOOGLE · Associate Data Practitioner

Google Associate Data Practitioner Exam Practice Questions

99% pass rateUpdated 1 Nov 2026103 questions2 PDFs by email
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Every question with the correct answer and why the other options are wrong.

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TRY BEFORE YOU BUY

Three of the 103 questions in this pack

Question 1

Your organization uses Dataflow pipelines to process real-time financial transactions. You discover that one of your Dataflow jobs has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

  1. Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.
  2. Create a custom script to periodically poll the Dataflow API for job status updates, and send email alerts if any errors are identified.
  3. Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.
  4. Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.
Show answer and explanation

Correct answer: C. Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.

When a Dataflow job fails, the fastest way to troubleshoot is to directly examine the job logs and worker logs available in the Google Cloud console's Dataflow Jobs page. This provides immediate visibility into error messages, stack traces, and failure reasons without requiring additional setup or custom tooling. The Dataflow Jobs page displays detailed logs that pinpoint exactly what went wrong, allowing rapid identification and resolution of the issue.

Why the other options are wrong

  • A. Setting up a monitoring dashboard is useful for ongoing observability but does not directly troubleshoot an existing failure and requires time to configure.
  • B. Creating a custom polling script is inefficient and unnecessary when native logging and monitoring tools already exist in the console.
  • D. While the gcloud CLI can retrieve metrics and logs, accessing the console directly is faster for immediate troubleshooting of a known failure.

Question 2

You work for a financial organization that stores transaction data in BigQuery. Your organization has a regulatory requirement to retain data for a minimum of seven years for auditing purposes. You need to ensure that the data is retained for seven years using an efficient and cost-optimized approach. What should you do?

  1. Create a partition by transaction date, and set the partition expiration policy to seven years.
  2. Set the table-level retention policy in BigQuery to seven years.
  3. Set the dataset-level retention policy in BigQuery to seven years.
  4. Export the BigQuery tables to Cloud Storage daily, and enforce a lifecycle management policy that has a seven-year retention rule.
Show answer and explanation

Correct answer: A. Create a partition by transaction date, and set the partition expiration policy to seven years.

Partition expiration policies in BigQuery allow you to automatically delete data older than a specified period, providing both cost optimization and automated compliance. This is more efficient than table-level or dataset-level retention policies because it operates at the partition granularity, deleting only expired partitions rather than entire tables. Option A directly addresses the regulatory requirement while maintaining cost efficiency through automatic deletion of old data.

Why the other options are wrong

  • B. Table-level retention policies delete the entire table after the retention period, which is inefficient and doesn't allow for archival of historical data.
  • C. Dataset-level retention policies apply to all tables in a dataset uniformly and lack the granularity needed for efficient data management.
  • D. Exporting to Cloud Storage and managing lifecycle policies adds unnecessary operational complexity and management overhead compared to built-in BigQuery partition expiration.

Question 3

You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report. What should you do?

  1. Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
  2. Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
  3. Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
  4. Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
Show answer and explanation

Correct answer: B. Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.

Colab Enterprise with bigframes.pandas library provides a Python-native environment optimized for data analysis at scale, with built-in integration to BigQuery and pandas-compatible APIs. The ability to schedule Colab notebooks through Vertex AI makes this the most efficient Python-based solution for weekly aggregation reports. This approach minimizes setup complexity while leveraging Google's managed environment.

Why the other options are wrong

  • A. Cloud Run functions are better suited for event-driven workloads rather than scheduled data aggregation tasks, and NumPy alone isn't optimized for BigQuery operations.
  • C. Cloud Data Fusion is a visual ETL tool better suited for complex data integration scenarios rather than Python-based aggregation logic.
  • D. Dataflow is designed for streaming or large-scale batch processing and introduces unnecessary complexity for simple weekly aggregation tasks.

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REVIEWS

1 review for Google Associate Data Practitioner Exam Practice Questions

  1. Myra –

    Google Associate-Data-Practitioner dumps gave me confidence, and I passed. The dumps are also 100% valid.

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How much of the Google Associate Data Practitioner bank is free?

103 questions in the bank

Each step includes the one before, so buying only adds new questions.

  • You are here10questionsFree sampleThree on this page, all ten as a PDF.Download the sample
  • 2625% of the bankPractice pageFree on the site, every answer explained.Go to the practice page
  • 50the first half of the packHalf PackFree by email, as one PDF, every answer explained.Get the Half Pack
  • 103100%, the whole bankFull pack, US$39Plus the questions-only PDF, monthly updates, refund if you fail.Get the full pack
ABOUT THIS PACK

What’s in the Google Associate Data Practitioner pack

  • 103 questions mapped to the Associate Data Practitioner exam guide
  • Answers and explanations for every question, including the wrong options
  • A questions-only PDF for timed practice runs
  • Delivered by email shortly after checkout
  • Free monthly updates for as long as the exam is live
  • Pass or your money back

An Associate Data Practitioner attempt costs US$125. This pack is US$39, paid once.

What makes the Associate Data Practitioner exam hard

Associate Data Practitioner is the entry point to Google's data track, sitting below the Professional Data Engineer, and it is a product-selection exam. Almost every question describes a data need and asks which Google Cloud service fits, so the marks go to people who know the difference between Dataflow and Data Fusion, BigQuery and Bigtable, Composer and scheduled queries, not to people who can write the pipeline.

Data Preparation and Ingestion is about 30% of the exam: ETL versus ELT versus ETLT, choosing between Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, Spanner and AlloyDB, and the right regional, dual-regional or multi-regional location. Data Analysis and Presentation at about 27% covers BigQuery SQL, Looker versus Looker Studio, and BigQuery ML.

Data Management at about 25% covers IAM roles, storage classes and lifecycle rules, backup and replication, and CMEK, CSEK and GMEK with Cloud KMS. Data Pipeline Orchestration at about 18% covers choosing between Dataproc, Dataflow, Data Fusion, Composer and Dataform, plus event-driven pipelines with Pub/Sub and Eventarc.

About the exam

The Google Cloud Associate Data Practitioner certification covers data preparation and ingestion, data analysis and presentation with BigQuery, Looker and BigQuery ML, data pipeline orchestration, and data management including access control, lifecycle, high availability and encryption. There are no prerequisites; Google recommends six or more months of experience with Google Cloud data services.

Exam sections

  • Data Preparation and Ingestion: about 30%
  • Data Analysis and Presentation: about 27%
  • Data Pipeline Orchestration: about 18%
  • Data Management: about 25%

50 to 60 multiple choice and multiple select questions, 120 minutes, US$125 per attempt, online proctored or at a test centre, certification valid for three years. Google does not publish a fixed passing score.

Questions before you buy

How quickly do I receive it?

Your PDF is prepared and sent to your email address after checkout, and you get a confirmation as soon as it is on its way.

Are updates included?

Yes. The pack is updated every month for as long as the exam is live, and updates are free for everyone who has bought it.

What if I fail the exam?

We refund the pack, or swap it for any other pack if you would rather keep studying. Sit the exam 7 to 30 days after buying, then send your official score report within 7 days of the exam date, as set out in the refund policy.

What is your pass rate?

99% of customers who prepare with a CertStash pack pass. If you do not, we refund the pack, or swap it for another pack.

Can I share it with colleagues?

Each purchase is licensed to one person. For a team, school or training organisation, email support@certstash.com for a licence that fits.

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