Google Cloud Associate Data Practitioner Practice Exam

What the Google Cloud Associate Data Practitioner exam covers

• Data Preparation and Ingestion226 questions
• Data Analysis and Presentation202 questions
• Data Pipeline Orchestration135 questions
• Data Management189 questions

Free Google Cloud Associate Data Practitioner practice test questions

A sample of 10 questions with answers and explanations. Sign up free to practice all 752.

1. Question 1Data Preparation and IngestionSelect all that apply

   A CSV load job fails because incoming files contain an extra trailing column that is not defined in the destination table's schema. Which two changes would allow the load job to succeed? (Choose two.)

   • AEnable allow jagged rows so BigQuery accepts rows with missing trailing values
   • BReduce the number of files loaded per job so fewer rows are scanned
   • CSwitch the write disposition from WRITE_APPEND to WRITE_TRUNCATE
   • DAdd the extra column to the destination table's schema before loadingCorrect
   • ESet the option that accepts and discards values not present in the schemaCorrect
   ✓ Correct answer: D, E

   Enabling the option that ignores values not present in the schema lets BigQuery drop the extra column while loading the rest of the row, and updating the destination schema to include the new column captures it instead. Both directly address the mismatch, unlike the other listed changes.

   Why the other options are wrong

   • AAllow jagged rows addresses rows with fewer values than expected, not an extra unexpected column.
   • BLoading fewer files per job does not change whether the schema mismatch occurs.
   • CChanging the write disposition affects how rows are written, not whether extra columns are tolerated.
2. Question 2Data Preparation and Ingestion

   A set of Parquet files in Cloud Storage is queried through a BigQuery external table. New files are later written with one additional column appended after the original set of columns. What is the most likely effect on queries against the external table?

   • AThe table schema must be updated to expose the new columnCorrect
   • BEvery query against the table fails until files are rewritten
   • CBigQuery instantly drops the older files from the table
   • DThe additional column silently replaces an existing column
   ✓ Correct answer: A

   BigQuery external tables use the schema that was defined, or inferred, at table creation time. Parquet embeds its own schema per file, so files with an extra column can still be read for the columns already defined, but the new column is not exposed to queries until the table definition or schema auto detection incorporates it. Older files are unaffected and continue to be queried normally alongside newer ones.

   Why the other options are wrong

   • BQueries against the columns that already exist in the table schema continue to work; the table does not fail wholesale because of an extra column in some files.
   • COlder files remain part of the table and are still queried; adding a column to new files does not remove earlier files.
   • DA newly appended column does not overwrite or replace any existing column; it is simply additional data that the current table schema is unaware of.
3. Question 3Data Preparation and Ingestion

   A recurring load job appends new rows to an existing table, and the latest export from the source system includes one new column that is not yet part of the table's schema. Which setting lets the job add that column automatically while still appending data?

   • AEnabling the schema update option that allows new fields to be added on appendCorrect
   • BSetting the write disposition to WRITE_TRUNCATE so the schema resets on each run
   • CRenaming the new column in the source file so it matches an existing column
   • DDeleting the destination table so BigQuery recreates it from scratch every run
   ✓ Correct answer: A

   BigQuery load jobs support a schema update setting that, when enabled alongside an append write disposition, lets the destination table gain new columns found in the source data instead of failing on the mismatch. This preserves the existing rows while extending the schema.

   Why the other options are wrong

   • BWRITE_TRUNCATE would discard the existing rows entirely rather than simply appending with an updated schema.
   • CRenaming the column would incorrectly merge unrelated data rather than adding a genuinely new field.
   • DRecreating the table from scratch loses the existing historical rows, which the job needs to keep.
4. Question 4Data Analysis and Presentation

   Before running an expensive query, you want to estimate how many bytes it would process without paying for it. Which BigQuery feature provides this?

   • AA dry run, which fully executes the query but automatically refunds the on-demand charge afterward
   • BA dry run, which estimates bytes processed without executing the query or incurring the chargeCorrect
   • CA materialized view, which precomputes the exact bytes each query will scan
   • DThe query cache, which returns the byte estimate from a previous execution
   ✓ Correct answer: B

   A dry run parses and validates the SQL and returns an estimate of the bytes the query would process, but it does not execute the query against the data or incur the on-demand charge for bytes scanned. This makes it a safe way to preview cost and validate a query before running it for real.

   Why the other options are wrong

   • AA dry run does not execute the query at all, so there is no charge to refund.
   • CMaterialized views precompute results; they do not provide a pre-run byte estimate.
   • DThe cache returns prior results, not a cost estimate for a new query.
5. Question 5Data Analysis and Presentation

   A marketing manager wants a live, interactive dashboard that non-technical stakeholders can filter and refresh themselves. Which tool best fits this need?

   • AA Cloud Shell terminal session
   • BA Vertex AI Workbench notebook
   • CA BigQuery command-line script
   • DA Looker Studio dashboardCorrect
   ✓ Correct answer: D

   Looker Studio is designed for building interactive, shareable dashboards that business users can filter and refresh without writing code, unlike a notebook, a terminal session, or a command-line script, all of which require technical interaction. This makes it the right fit for a recurring, stakeholder-facing report.

   Why the other options are wrong

   • AA Cloud Shell terminal session requires command-line interaction, not a no-code filtering experience.
   • BA notebook requires reading or writing Python code, which does not suit a no-code audience.
   • CA BigQuery command-line script requires technical query knowledge, not a self-serve dashboard.
6. Question 6Data Analysis and Presentation

   A finance analyst wants a running total of daily revenue ordered by date, without collapsing rows via GROUP BY. Which construct produces a running total?

   • ACOUNT(revenue) OVER (ORDER BY date), a running count
   • BSUM(revenue) OVER (ORDER BY date), a running totalCorrect
   • CSUM(revenue) with GROUP BY date, a daily total only
   • DAVG(revenue) OVER (ORDER BY date), a running average
   ✓ Correct answer: B

   SUM(revenue) OVER (ORDER BY date) keeps every row and adds each day's revenue to the sum of all prior days, producing a running total without collapsing the result. GROUP BY would instead collapse rows into one total per date, losing the running behavior.

   Why the other options are wrong

   • ACOUNT OVER produces a running count of rows, not a running revenue total.
   • CGROUP BY collapses rows into one total per date and removes the row-by-row detail.
   • DAVG OVER produces a running average, not a running sum of revenue.
7. Question 7Data Pipeline Orchestration

   A pipeline has just two steps: a Cloud Storage upload triggers a Cloud Run function that then loads data into BigQuery. Is a dedicated workflow orchestrator like Cloud Composer necessary for this pipeline?

   • ANo, a direct Eventarc-triggered function is enough for this chainCorrect
   • BNo, but only if the pipeline never runs more than once a year
   • CYes, any multi-service pipeline always requires Cloud Composer setup
   • DYes, Cloud Composer is required whenever Cloud Storage is used
   ✓ Correct answer: A

   For a straightforward sequence like file upload leading to a function that loads BigQuery, wiring the steps together with an Eventarc trigger is sufficient and avoids the added operational cost of running an orchestrator such as Cloud Composer. Orchestrators earn their keep when pipelines have complex branching, retries, or many interdependent steps across systems.

   Why the other options are wrong

   • BRun frequency is not the deciding factor for whether an orchestrator is needed; pipeline complexity is.
   • CUsing more than one Google Cloud service does not by itself require a dedicated orchestrator for a simple chain.
   • DCloud Storage involvement alone does not mandate Cloud Composer; it depends on the pipeline's overall complexity.
8. Question 8Data Pipeline Orchestration

   Which statement correctly describes a Dataflow batch job?

   • AIt runs continuously and never reaches a completed state
   • BIt requires manually defined windows for each batch run
   • CIt processes a bounded dataset and then completesCorrect
   • DIt only reads streaming data from a Pub/Sub subscription
   ✓ Correct answer: C

   Batch jobs read from a bounded source, such as a set of files in Cloud Storage or a BigQuery table snapshot, apply the pipeline's transforms, write the results, and then reach a completed state. This differs from streaming jobs, which continue running to handle an ongoing flow of data.

   Why the other options are wrong

   • ARunning continuously and never completing describes a streaming job, not a batch job, which is expected to finish.
   • BWindowing is primarily relevant to streaming aggregations over unbounded data, not a requirement for every batch run.
   • DBatch jobs commonly read from bounded sources like Cloud Storage or BigQuery, not exclusively streaming data from Pub/Sub.
9. Question 9Data Management

   A nightly ETL job in error overwrites a critical reference file in a Cloud Storage bucket with an empty file. The bucket had object versioning enabled beforehand. How can the practitioner recover the original content?

   • AWait for Autoclass to automatically revert the object
   • BContact support to reverse the bucket write operation
   • CRestore the prior noncurrent version of the objectCorrect
   • DEnable soft delete now to recover the prior content
   ✓ Correct answer: C

   Because versioning was already enabled, the overwrite did not erase the original bytes; instead the prior content became a noncurrent version identified by its own generation number, which can be listed and copied back over the current object. No external support intervention is needed for this self-service recovery.

   Why the other options are wrong

   • AAutoclass changes storage class based on access patterns and has no ability to revert object content.
   • BSupport intervention is unnecessary since versioning already preserves the prior content for self-service restoration.
   • DEnabling soft delete after the fact would not retroactively protect an overwrite that already occurred before it was turned on.
10. Question 10Data Management

    A data team is deciding whether to keep three-year-old clickstream data queryable in BigQuery or export it to Cloud Storage. The data is queried a few times per quarter for trend analysis. What is the main cost trade-off they are weighing?

    • AHigher compute cost from exporting versus lower egress from BigQuery
    • BLower backup cost from exporting versus faster load times in BigQuery
    • CLower IAM overhead from exporting versus stronger encryption in BigQuery
    • DCheaper export storage versus BigQuery's direct query convenienceCorrect
    ✓ Correct answer: D

    Because the clickstream data is only queried a few times per quarter, the team must weigh BigQuery's per-GB storage rate, even at the long-term discount, against Cloud Storage's cheaper classes, factoring in the extra step and time needed to reload exported data before each analysis. This is a classic storage-cost-versus-query-convenience trade-off, not a compute, IAM, or backup concern.

    Why the other options are wrong

    • AExporting reduces stored bytes rather than increasing compute, and egress is not the primary cost driver in this comparison.
    • BBackup cost and load-time speed are not the central factors driving this particular storage-location choice.
    • CIAM overhead and encryption strength do not materially differ between the two storage locations for this decision.

Who this Google Cloud Associate Data Practitioner practice exam is for

This practice set is for anyone preparing for the Google Cloud Associate Data Practitioner exam at the intermediate level - from first-time candidates building a foundation to experienced Google practitioners doing a final review before test day. If you learn best by working through realistic questions and reading why each answer is right or wrong, it is built for you.

How to use this Google Cloud Associate Data Practitioner practice exam

1. Start with the free sample questions above to gauge your current baseline.
2. Read the full explanation on every question, including why each wrong option is wrong.
3. Track your weak domains and focus your study where you are losing the most marks.
4. Once you are scoring consistently well, take a timed, full-length mock exam.
5. Use your readiness score to decide when you are ready to book the real Google Cloud Associate Data Practitioner exam.

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