Databricks Certified Data Analyst Associate Practice Exam

What the Databricks Certified Data Analyst Associate exam covers

• Understanding of Databricks Data Intelligence Platform84 questions
• Managing Data66 questions
• Importing Data50 questions
• Executing queries using Databricks SQL and Databricks SQL Warehouses152 questions
• Analyzing Queries108 questions
• Working with Dashboards and Visualizations in Databricks120 questions
• Developing, Sharing, and Maintaining AI/BI Genie spaces92 questions
• Data Modeling with Databricks SQL50 questions
• Securing Data66 questions

Free Databricks Certified Data Analyst Associate sample questions

A sample of 10 questions with answers and explanations. Sign up free to practice all 788.

1. Question 1Analyzing Queries

   A data analyst notices that a Databricks SQL query with heavy aggregations runs much faster on one SQL warehouse than on another warehouse of the same size. The faster warehouse has this engine enabled. What is it?

   • APhotonCorrect
   • BUnity Catalog lineage
   • CDelta Sharing
   • DAuto Loader
   ✓ Correct answer: A

   Photon executes query operators in native code rather than the JVM, processing data in a columnar, vectorized way. This speeds up common analytics operations like joins, aggregations, and scans with no query rewrite needed.

   Why the other options are wrong

   • BTracks data flow between assets; it does not change execution performance.
   • CAn open protocol for sharing data externally, unrelated to execution speed.
   • DIngests new files incrementally; it does not speed up query execution.
2. Question 2Analyzing Queries

   Which statement accurately describes how Liquid Clustering applies clustering to new data written to a table?

   • AClusters incrementally as OPTIMIZE runsCorrect
   • BRewrites every file on every insert
   • CClusters data only when queried
   • DApplies clustering only once, at creation
   ✓ Correct answer: A

   Rather than reorganizing the whole table on every write, Liquid Clustering incrementally applies clustering as OPTIMIZE is run, efficiently absorbing new data without the cost of a complete rewrite.

   Why the other options are wrong

   • BA full rewrite of every file on every insert would be extremely costly and is not how Liquid Clustering operates.
   • CClustering is applied through maintenance operations like OPTIMIZE, not triggered by running a SELECT.
   • DClustering can be reapplied over time as data grows; it is not limited to a single one-time action.
3. Question 3Working with Dashboards and Visualizations in DatabricksSelect all that apply

   An analyst tries to plot CPU utilization over 24 hours for 50 individual servers as 50 separate lines on one chart, but the result is an unreadable tangle of colors. Which TWO reasons make a heatmap (servers by hour, color as CPU percent) a better alternative? (Choose TWO)

   • AIt removes the need to collect hourly data
   • BColor lets patterns be scanned without overlapping linesCorrect
   • CIt always shows more precise values than a line chart
   • DA pie chart would organize 50 servers better
   • EIt encodes server, hour, and value in one gridCorrect
   ✓ Correct answer: B, E

   Replacing 50 overlapping lines with a server-by-hour grid colored by CPU percent makes hot spots and patterns visible at a glance, without any line-color confusion.

   Why the other options are wrong

   • AThe heatmap still needs hourly data to plot; it does not reduce data collection needs.
   • CHeatmaps are generally less precise for reading exact values than a table; their strength is pattern recognition, not precision.
   • DA pie chart cannot show two dimensions, server and hour, simultaneously, and 50 slices would be unreadable.
4. Question 4Developing, Sharing, and Maintaining AI/BI Genie spaces

   An insurer already has an AI/BI dashboard built on a curated set of claims tables. Which capability lets a team quickly stand up a related Genie space using those same datasets?

   • ACreating a Genie space directly from the existing dashboardCorrect
   • BExporting the dashboard as a PDF and re-uploading it as a table
   • CCloning the dashboard's SQL warehouse into a new catalog
   • DConverting the dashboard into a Delta Live Tables pipeline
   ✓ Correct answer: A

   This shortcut saves the team from re-selecting the same tables and views by hand, letting them quickly add conversational Q&A on top of data that is already curated and trusted for the dashboard.

   Why the other options are wrong

   • BA PDF export cannot be used to seed a Genie space's live, queryable datasets.
   • CCloning a warehouse alone does not carry over the dashboard's curated dataset selection.
   • DDelta Live Tables builds pipelines; it is unrelated to spinning up a Genie space.
5. Question 5Developing, Sharing, and Maintaining AI/BI Genie spacesSelect all that apply

   Before rolling a Genie space out to the entire company, which TWO practices help establish a trustworthy accuracy baseline? (Choose TWO)

   • ARunning a benchmark set of questions with known-correct answersCorrect
   • BAssuming accuracy is fine since setup was easy
   • CSkipping review since users will report problems
   • DHaving domain experts review a sample of generated answersCorrect
   • EHiding the space from all stakeholders indefinitely
   ✓ Correct answer: A, D

   Together they combine a repeatable, measurable check with qualitative human judgment on whether the answers actually make business sense.

   Why the other options are wrong

   • BEase of configuration says nothing about whether answers are actually correct.
   • CWaiting passively for user reports is reactive, not a way to establish an upfront baseline.
   • EHiding the space indefinitely prevents both feedback and adoption, not a valid rollout strategy.
6. Question 6Managing Data

   An analyst notices that a phone_number column has inconsistent leading and trailing whitespace that is breaking exact-match joins to another table. Which function directly removes this extra whitespace?

   • ALENGTH(phone_number)
   • BTRIM(phone_number)Correct
   • CCAST(phone_number AS INT)
   • DROUND(phone_number, 0)
   ✓ Correct answer: B

   TRIM(phone_number) strips extra spaces from the start and end of the value, which is a common cause of failed exact-match joins between tables that otherwise contain the same logical value. This is a lightweight, non-destructive cleaning step usable directly in a SELECT or JOIN condition.

   Why the other options are wrong

   • ALENGTH returns the character count of a string; it does not remove whitespace.
   • CCasting a phone number to an integer would strip formatting characters unpredictably and is not the correct fix for whitespace.
   • DROUND applies to numeric values and has no meaning for a text column like phone_number.
7. Question 7Data Modeling with Databricks SQL

   In a well-formed star schema for an airline's flight bookings, the fact table's foreign key columns should reference which kind of column in each dimension table?

   • AThe dimension surrogate keyCorrect
   • BDescriptive text attribute
   • CNatural/business source key
   • DThe dimension load timestamp
   ✓ Correct answer: A

   In a properly designed star schema, fact table foreign keys reference the dimension's surrogate key, which remains stable even if the source system's natural key changes, and which supports techniques like SCD Type 2.

   Why the other options are wrong

   • BDescriptive attributes are not used as join keys between fact and dimension tables.
   • CNatural or business keys from the source can change or be reused, making them unreliable as the join target.
   • DA load timestamp is metadata, not a join key.
8. Question 8Executing queries using Databricks SQL and Databricks SQL Warehouses

   Which clause restricts a query to only rows where status equals 'shipped'?

   • AHAVING status = 'shipped'
   • BORDER BY status = 'shipped'
   • CWHERE status = 'shipped'Correct
   • DGROUP BY status = 'shipped'
   ✓ Correct answer: C

   WHERE evaluates the condition against each row before any grouping or aggregation, keeping only rows where status is 'shipped'.

   Why the other options are wrong

   • AHAVING filters aggregated groups, it is not the right clause for a simple row-level condition with no aggregation involved.
   • BORDER BY only controls sort order, it cannot filter rows.
   • DGROUP BY defines grouping columns for aggregation, it does not accept a filter condition like this.
9. Question 9Executing queries using Databricks SQL and Databricks SQL WarehousesSelect all that apply

   Which two statements about Delta Lake time travel retention are correct? (Choose TWO)

   • ADelta tables retain every historical version forever with no possibility of losing access
   • BHow far back time travel can go depends on log and data file retention settingsCorrect
   • CTime travel retention is controlled exclusively by the SQL warehouse's auto-stop setting
   • DRunning VACUUM can remove data files needed for older versions, limiting time travel rangeCorrect
   • ETime travel is unlimited only for external tables, never for managed tables
   ✓ Correct answer: B, D

   Because Delta Lake's retention is configurable rather than infinite, teams need to balance storage cost against how far back they may need to time travel, and be careful about running VACUUM too aggressively.

   Why the other options are wrong

   • ARetention is not indefinite by default; it is governed by configurable retention thresholds and can be affected by VACUUM.
   • CSQL warehouse auto-stop controls compute idling; it has no bearing on Delta version retention.
   • ETime travel availability depends on log/data retention settings, not on whether a table is managed or external.
10. Question 10Understanding of Databricks Data Intelligence Platform

    A data engineering team wants to define a multi-stage ETL pipeline declaratively, with built-in data quality checks and automatic handling of pipeline errors. Which component is designed for this?

    • ALakeflow Jobs
    • BDelta Live TablesCorrect
    • CMosaic AI
    • DAI/BI Dashboards
    ✓ Correct answer: B

    Instead of writing imperative orchestration code, a team declares the desired tables and the quality expectations, and the framework manages dependency ordering, retries, and monitoring.

    Why the other options are wrong

    • ALakeflow Jobs schedules and orchestrates tasks, including DLT pipelines, but is not itself the declarative pipeline framework.
    • CMosaic AI is for building and serving ML models, not ETL pipelines.
    • DAI/BI Dashboards visualize query results; they do not build ETL pipelines.

Who this Databricks Certified Data Analyst Associate practice exam is for

This practice set is for anyone preparing for the Databricks Certified Data Analyst Associate exam at the intermediate level - from first-time candidates building a foundation to experienced Data 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 Databricks Certified Data Analyst Associate 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 Databricks Certified Data Analyst Associate exam.

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