Databricks · DB-DAA · Associate

Databricks Data Analyst Associate — Practice Questions and Mock Exam

Name: Pass-IT
Author: Pass-IT

Prepare for DB-DAA with original practice questions and clear answer explanations. Ask Alex, your AI tutor, when you need more detail, use your results to identify topics to review, and practice your pacing with timed mock exams.

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45Mock exam questions

90minTime limit

Checked against Databricks · August 2026 · Current exam version

Overview

About the exam

The Databricks Certified Data Analyst Associate exam validates the ability to work with data using Databricks SQL and related tools. It covers executing and analyzing SQL queries, building dashboards and visualizations, developing AI/BI Genie spaces for natural language data exploration, and managing and securing data through Unity Catalog. Candidates must demonstrate proficiency in Databricks SQL Warehouses, data modeling, and interpreting query performance.

This certification is designed for data analysts with six or more months of experience using Databricks SQL for querying, visualization, and analysis. It demonstrates the ability to turn raw data into dashboards and shareable insights within the Databricks platform.

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Try five DB-DAA questions

Try five practice questions from the app’s current Databricks Certified Data Analyst Associate question bank, with answers and explanations.

Executing queries using Databricks SQL and Databricks SQL Warehouses1 / 5

How does an analyst use CTAS (CREATE TABLE AS SELECT) to materialize query results?

AlexFull explanation from Alex

CREATE TABLE AS SELECT (CTAS) executes the SELECT query and writes results into a new managed Delta table in one atomic statement. The schema is inferred from the SELECT—you cannot specify columns explicitly in a CTAS. Common patterns: materializing expensive aggregations for dashboards, creating date-partitioned snapshots, or building cleaned/transformed tables. Distractor analysis: The option “CTAS executes the SELECT as a streaming job…” is wrong—CTAS is batch, not streaming, and needs no COMMIT. The option “CTAS registers the SELECT query as a view…” is wrong—CTAS creates a physical table, not a materialized view. The option “CTAS creates a schema-only table structure using the SELECT…” is wrong—CTAS both creates the schema and populates data in one step. Ref: docs.databricks.com/en/delta/tutorial.html; docs.databricks.com/en/sql/language-manual/sql-ref-syntax-ddl-create-table-using.html

Sourcedocs.databricks.com

Managing Data2 / 5

How does partition pruning improve query performance on large Delta tables?

AlexFull explanation from Alex

Partition pruning skips entire partition directories in cloud storage when the WHERE clause filters on the partition column. For example, a table partitioned by date with WHERE date = '2024-01-01' reads only that partition's files. Best partition columns have low cardinality with clear query patterns (date, region). Delta Lake handles partition metadata automatically—no MSCK REPAIR needed. For non-partition columns, Delta uses data skipping (min/max file stats) and dynamic file pruning. Distractor analysis: The option “Partition pruning indexes the column statistics within each…” describes data skipping/predicate pushdown, not partition pruning. The option “Partition pruning redistributes query processing so…” describes data locality, not partition pruning. The option “Partition pruning rewrites the query execution plan…” describes query plan optimization, not partition elimination. Ref: docs.databricks.com/en/tables/partitions.html; docs.databricks.com/en/delta/best-practices.html

Sourcedatabricks.com

Developing, Sharing, and Maintaining AI/BI Genie spaces3 / 5

How does a data analyst set up a Genie Space for a sales analytics use case?

AlexFull explanation from Alex

Genie Space setup follows a structured workflow: (1) Select 3-30 well-curated Unity Catalog tables/views. (2) Write clear instructions defining business metrics and rules (e.g., 'Revenue excludes cancelled orders'). (3) Add table and column descriptions for semantic context. (4) Test with sample questions and mark verified SQL as trusted assets. The knowledge store—instructions, descriptions, and example queries—helps Genie generate accurate SQL. Distractor analysis: The option “Import existing BI dashboard definitions…” is wrong—BI dashboard imports are not how Genie spaces are configured. The option “Create a curated Gold-layer Delta table…” is wrong—while Gold-layer tables help, the key step is instructions and descriptions. The option “Connect the sales database using a Unity Catalog service…” is wrong—no YAML configuration is needed. Ref: docs.databricks.com/en/genie/set-up.html; docs.databricks.com/en/genie/best-practices.html

Sourcedocs.databricks.com

Analyzing Queries4 / 5

What is the difference between RANK, DENSE_RANK, and ROW_NUMBER window functions?

AlexFull explanation from Alex

Choosing the right function: RANK: competition-style (like sports rankings). 'Two people tied for 2nd, nobody is 3rd.' Use for: leaderboards, competition results. DENSE_RANK: sequential ranking with shared positions. 'Two people tied for 2nd, next person is 3rd.' Use for: top-N filtering where gaps cause problems (WHERE rnk <= 3 with RANK might return 2 rows if there are ties at rank 2, but DENSE_RANK guarantees at least 3 ranks). ROW_NUMBER: guaranteed unique numbers. Use for: deduplication (keep first per group), pagination (OFFSET/FETCH alternative), unique row IDs.

Sourcespark.apache.org

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Question pool

310 practice questions

Use the Pass-IT question pool to practice for DB-DAA. Mock exams are set to 45 questions in 90 minutes.

Pool details: DB-DAA

Objectives in the guide39 objectives listed in the official guide

across 9 domains in the official exam guide

Pool size310 questions

= The pool size is equivalent to 6 sets of 45 questions; this does not mean that each mock exam uses a separate set.

Recorded as checked against sources310 of 310

questions recorded as having their answer, options, and explanation checked against official Databricks documentation

How questions are made →

Exam domains

What's on the exam

Executing queries with Databricks SQL is the single largest domain at 20%; together with analyzing queries (15%) and dashboards and visualizations (16%), querying and presenting data make up just over half the exam. AI/BI Genie spaces carry 12%, close to platform fundamentals at 11%, testing natural-language exploration built on curated, trusted datasets.

The remaining domains sit in the 5–8% range: managing and securing data through Unity Catalog, importing data through Auto Loader or Delta Sharing, and modeling analytical workloads with star and snowflake schemas. These smaller sections test specific, narrow skills rather than the day-to-day SQL work that dominates the rest of the exam.

Exam blueprint: DB-DAA

Understanding of Databricks Data Intelligence Platform11%

Tour the platform's building blocks - from Delta Lake and Unity Catalog to Lakeflow Jobs and Databricks SQL - navigate catalogs and permissions through Catalog Explorer, and see what the Databricks Marketplace adds on top.

≈ 7 h

Managing Data8%

Find, query, and govern trusted datasets through Unity Catalog, tag and trace them in Catalog Explorer, and clean up bad or missing values before they reach downstream tables.

≈ 5 h

Importing Data5%

Bring external data into Databricks through cloud-storage ingestion, Delta Sharing, Auto Loader, or the Marketplace, or simply drag a file in through the Workspace UI.

≈ 3 h

Executing queries using Databricks SQL and Databricks SQL Warehouses20%

Write and debug SQL queries using Databricks Assistant and SQL Warehouses, including joins, aggregations, sorting, and filtering across managed, external, and federated data sources. Also covers creating materialized and streaming views, and using Delta Lake time travel to query historical data versions.

≈ 12 h

Analyzing Queries15%

Spot slow-running queries using tools like Query Profiler, lean on Photon for the workloads it accelerates, audit Delta Lake's history to compare results over time, and speed things up further with caching and Liquid Clustering on frequently filtered columns.

≈ 9 h

Working with Dashboards and Visualizations in Databricks16%

Assemble multi-tab dashboards from several data sources with AI/BI Dashboards, or chart data straight from a notebook or the SQL editor, wire up parameters, control who can view or embed the result, schedule refreshes and alerts, and choose the chart type that actually communicates the insight.

≈ 10 h

Developing, Sharing, and Maintaining AI/BI Genie spaces12%

Understand what a Genie space is built from, stand one up with curated datasets and guiding instructions, control who can reach it and how it gets shared, and refine it over time using accuracy benchmarks and user feedback.

≈ 7 h

Data Modeling with Databricks SQL5%

Model analytical workloads with dimensional (star and snowflake) and data-vault schema patterns, then connect those structures to the Medallion Architecture's bronze/silver/gold layering.

≈ 3 h

Securing Data8%

Lock down workspace objects using roles and sharing settings inside Unity Catalog, work within its three-tier catalog, schema, and table structure, and follow storage practices that keep ownership clear and personal data protected.

≈ 5 h

Format

Exam format and question types

The exam draws 45 scored, multiple-choice questions from a 90-minute session that may also include unscored pilot items. Questions are scenario-based, covering writing and debugging Databricks SQL queries, building AI/BI dashboards, configuring Genie spaces, and diagnosing slow queries in the Query Profiler rather than pure syntax recall.

Question types: DB-DAA

Multiple Choice100%

Select the single answer that best meets the question’s requirements.

See Databricks for official question-format information. The shares shown describe the Pass-IT practice pool; they do not establish the proportions on the official exam.

Planning

Preparing for DB-DAA

The exam is delivered online with a remote proctor or at a test center, in English. The credential holds for two years, and recertifying means passing the current version of the exam.

Preparation and logistics: DB-DAA

Preparation

Illustrative study time35–90 h

illustrative planning range: 35 h with relevant experience to 90 h when starting out; your needs may fall outside this range

LevelAssociate

Recommended backgroundNone required. 6+ months hands-on Databricks SQL experience recommended.

Taking and maintaining the certification

DeliveryOnline proctored or test center

Retake policyNo mandatory waiting period. Retake fee applies.

Certification validity2 years

Recertification required every 2 years by taking the current exam version.

Official guideView on provider site

Study notes

Common pitfalls

Topics to review: DB-DAA

013-Level Namespace
Confusing the catalog/schema/table hierarchy with a flat table reference, and table ownership with row- or column-level grants, leads to wrong answers on Unity Catalog security questions.
02Schema Design
Mapping a star or snowflake design, or a data vault model, onto the wrong Medallion layer causes mistakes on data-modeling questions that expect Bronze/Silver/Gold alignment.
03Delta Time Travel
Not knowing how to query a table's historical state with Delta Lake time travel, or confusing it with a full audit-history export, leads to wrong answers on versioning questions.
04Ingestion Paths
Confusing S3-based file ingestion, Delta Sharing from external systems, and Marketplace data with each other causes mistakes on import-method questions expecting the right source-to-Databricks path.
05Join and Set Ops
Mixing up inner, left, and right joins with union and union all, especially when combining tables from multiple sources, leads to wrong answers on query-writing questions.

Practice with questions and explanations in Pass-IT →

Questions

Frequently asked questions

How long is the Databricks Certified Data Analyst Associate exam?

The Databricks Certified Data Analyst Associate exam has 45 questions and a 90-minute time limit.

Which pitfalls should I review when preparing for Databricks Certified Data Analyst Associate?

Topics to review include 3-Level Namespace, Schema Design, Delta Time Travel, Ingestion Paths, Join and Set Ops. Work through examples to check that you understand the distinctions and can explain your answer.

Which Data Analyst Associate topics carry the most marks?

Running queries in Databricks SQL and SQL warehouses is the largest single area at 20%, with dashboards and visualizations at 16% and query analysis at 15%. AI/BI Genie spaces take 12% and platform understanding 11%, and the remaining five areas are 8% or less each. The exam is split across nine narrow objectives, so it rewards familiarity with the whole SQL workspace rather than depth in one corner.

How long does the Databricks Data Analyst Associate stay valid?

Two years from the date you pass. Databricks has no continuing-education route, so recertifying means sitting the current version of the exam again. The two-year clock is shorter than the three years AWS and CompTIA use, which catches people out.

What do you need before taking the Data Analyst Associate exam?

No certification is required. Databricks recommends six or more months of hands-on Databricks SQL work, and the catalog budget is around 60 hours. Most of that time goes on the workspace itself rather than on SQL, which the exam assumes you already write.

Data Analyst Associate or Data Engineer Associate?

The analyst exam is about querying, dashboards and Genie spaces inside the SQL workspace. The engineer exam is about ingestion, transformation and Lakeflow jobs that produce the tables you would then query. If you consume tables, take the analyst exam; if you build them, take the engineer one.

How soon can you retake the Data Analyst Associate exam?

Databricks sets no mandatory waiting period between attempts. You rebook when you are ready rather than when a policy allows it, which makes the pacing entirely your own decision.

One certification, 12 months

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