Databricks · DCDAA
Databricks Certified Data Analyst Associate Practice Tests
Validates the ability to perform data analysis tasks using Databricks SQL and the Data Intelligence Platform, covering data management with Unity Catalog, query development and optimization, dashboards and visualizations, AI/BI Genie spaces, and data modeling.
Exam Details
Practice Questions
627
≈ 13 practice exams
Duration
90 minutes
Passing Score
70%
Difficulty
AssociateLast Updated
Feb 2026
Databricks Certified Data Analyst Associate Practice Exam Preparation
Use this DCDAA practice exam to prepare for Databricks Certified Data Analyst Associate with realistic questions, detailed explanations, and focused study modes. The practice bank includes 627 questions for Databricks DCDAA, so you can review the exam steadily instead of relying on one long cram session.
As you practice, pay extra attention to patterns in your missed answers. Start with short sessions to identify weak areas, then move into timed quizzes once your accuracy is consistent.
The explanations are especially useful when you want to connect exam wording to the responsibilities and scenarios described in the official certification guidance. Use the free preview first, then unlock the full question bank when you are ready to build a complete study routine.
Exam Domain Breakdown
Databricks SQL22%
Data Management20%
SQL29%
Data Visualization and Dashboards18%
Analytics Applications11%
Exam Overview
The Databricks Certified Data Analyst Associate certification validates a candidate's ability to perform data analysis tasks using Databricks SQL and the broader Databricks Data Intelligence Platform. The exam assesses proficiency across five core domains: Databricks SQL (22%), Data Management (20%), SQL (29%), Data Visualization and Dashboards (18%), and Analytics Applications (11%). Candidates must demonstrate the ability to write and optimize ANSI SQL-compliant queries, manage data using Unity Catalog, ingest data through multiple methods including UI uploads, S3 ingestion, Delta Sharing, Auto Loader, and the Databricks Marketplace, and build production-grade dashboards with AI/BI Genie spaces.
The certification was updated in 2025 to reflect Databricks' evolution from a SQL analytics tool to a comprehensive Data Intelligence Platform. The updated exam places greater emphasis on Unity Catalog governance, AI/BI dashboard capabilities, query federation for cross-system analytics, and Attribute-Based Access Control (ABAC). Topics such as discrete/continuous statistics and third-party BI tool integrations (Tableau, Power BI, Looker specifics) were removed in the 2025 version. The credential remains valid for two years, after which recertification via the current exam version is required.
Who Should Take This Exam
This certification is designed for data analysts, business intelligence professionals, SQL practitioners, and business users who work with or plan to work with the Databricks Data Intelligence Platform. It is well-suited for individuals in roles such as Data Analyst, BI Analyst, Analytics Engineer, or SQL Developer who need to demonstrate hands-on proficiency with Databricks SQL for querying, visualization, and insight generation.
Candidates are expected to have approximately 6 months of hands-on experience performing data analysis tasks within the Databricks environment. The associate-level designation makes it an appropriate starting point for professionals transitioning into the Lakehouse ecosystem or those looking to formalize their existing Databricks SQL skills with a vendor-recognized credential.
Prerequisites
There are no mandatory formal prerequisites to register for this exam. However, Databricks recommends that candidates have at least 6 months of practical, hands-on experience working with Databricks SQL and the Data Intelligence Platform before attempting the exam. Familiarity with ANSI SQL standards is essential, as all SQL in the exam conforms to that specification.
Databricks also recommends completing the Lakehouse Fundamentals Accreditation as a foundational step before pursuing this certification. Prior experience with Unity Catalog for data governance, Delta Lake for data management, and the Databricks SQL editor will be highly beneficial. Candidates without Databricks-specific experience but with strong SQL backgrounds and data warehouse or analytics tool experience may still be competitive after targeted hands-on preparation.
Exam Format
The exam consists of 45 scored questions delivered in a 90-minute time window. Questions are multiple-choice and multi-select format. The exam may also include a small number of unscored survey or pilot items used for statistical calibration of future exams; these items are not identified and do not affect the final score, with additional time factored in to account for them.
The passing score is 70%. The exam costs USD $200 (plus applicable local taxes) and is delivered online through Databricks' exam delivery platform, which requires account creation or login prior to registration. All SQL tested on the exam adheres to ANSI SQL standards. Recertification is required every two years by retaking the current version of the exam.
Skills Measured
- 1.Databricks SQL (22%): Understanding the features, capabilities, and architecture of the Databricks SQL service, including workspace configuration, SQL warehouses, query editor functionality, alerts, and integration with the broader Data Intelligence Platform.
- 2.Data Management (20%): Managing data using Unity Catalog, including discovering, querying, cleaning, and governing certified datasets. Covers data ingestion methods such as UI-based uploads, S3 ingestion, Delta Sharing, API-driven intake, Auto Loader, and the Databricks Marketplace. Also includes Delta Lake table management, metadata handling, and data storage best practices.
- 3.SQL (29%): Writing and executing ANSI SQL-compliant queries for data exploration and analysis within the Lakehouse environment. Topics include JOIN operations, subqueries, aggregate functions, filtering, sorting, combining data from multiple sources, creating views, and auditing query performance using history logs and Liquid Clustering features.
- 4.Data Visualization and Dashboards (18%): Creating production-grade data visualizations and AI/BI dashboards using Databricks SQL. Includes selecting appropriate chart types, configuring dashboard parameters, setting up alerts, sharing dashboards with stakeholders, and developing AI/BI Genie spaces for natural-language-driven analytics.
- 5.Analytics Applications (11%): Applying data analysis techniques to solve common business problems using Databricks SQL. Covers building analytics workflows, performing data transformations, and leveraging platform features to deliver actionable insights.
Study Tips
- Download and study the official Databricks Certified Data Analyst Associate Exam Guide PDF from files.training.databricks.com — it lists all exam domains, topic breakdowns, and sample question styles. Use it as your primary study roadmap.
- Complete the official 'Data Analysis with Databricks' self-paced course on Databricks Academy (academy.databricks.com). It directly covers ingest, querying, dashboards, and alerting — the core exam content — and is explicitly designed to prepare candidates for this exam.
- Get hands-on with a live Databricks Community Edition account. Practice writing ANSI SQL queries in the SQL editor, create dashboards using AI/BI, configure alerts, and experiment with Unity Catalog to manage tables and permissions. Practical exposure is the most effective preparation.
- Focus significant study time on the SQL domain (29% weight), which is the largest section. Practice JOINs, subqueries, window functions, aggregations, and filtering on Databricks SQL. Pay attention to behaviors specific to Delta Lake tables such as DESCRIBE HISTORY and time travel queries.
- Study Unity Catalog governance concepts thoroughly for the Data Management domain (20%). Understand the three-level namespace (catalog.schema.table), data lineage, grants and privileges, ABAC, and the differences between managed and external tables in the Lakehouse.
- Review the AI/BI Genie spaces feature and the newer dashboard capabilities introduced in the 2025 exam update. This includes configuring parameters, sharing dashboards, embedding visualizations, and understanding when to use Genie spaces versus traditional dashboards.
- Take at least two full-length timed practice exams before the real test. Udemy offers several highly-rated practice exam sets specific to this certification. Review all incorrect answers against the official exam guide to close knowledge gaps before exam day.
Career Benefits
Earning the Databricks Certified Data Analyst Associate credential signals verified proficiency on one of the fastest-growing data platforms in the enterprise market. Databricks is widely adopted by companies building Lakehouse architectures, and certified analysts are well-positioned for roles such as Data Analyst, BI Analyst, Analytics Engineer, and SQL Developer at organizations using Databricks. The certification is particularly valuable for professionals looking to differentiate themselves as Databricks skills become a standard hiring requirement across data teams.
Data analysts with Databricks certification report average salaries in the range of $115,000–$148,000 annually in the United States, meaningfully above the general data analyst average. The certification is an associate-level entry point into the Databricks certification ecosystem, which also includes Data Engineer Associate/Professional and Machine Learning tracks, giving certified analysts a clear pathway for continued credential advancement. As enterprises continue to consolidate their data and AI workloads on unified Lakehouse platforms, demand for analysts with validated Databricks SQL and governance skills is expected to remain strong.
Sample Questions
5 sample questions with answers and explanations. The full bank has 627 questions, enough for 13 full-length practice exams.
Preview — answers shown1. A media streaming company maintains a 25 TB content_views table with liquid clustering on (content_id, user_id). The analytics team adds geographic analysis requirements and now frequently filters queries on user_country. After running ALTER TABLE content_views CLUSTER BY (content_id, user_id, user_country), which command must be run to apply the new clustering to existing data? (Select one!)
AOPTIMIZE content_views
BVACUUM content_views
CREFRESH TABLE content_views
DOPTIMIZE content_views FULL
Explanation
OPTIMIZE FULL forces reclustering of all records in the table with the new clustering key configuration. When changing clustering keys, existing data files are organized according to the old keys and must be rewritten to apply the new layout. Standard OPTIMIZE is incremental and only rewrites data that needs clustering based on current keys, not reclustering with changed keys. VACUUM removes old data files beyond the retention period but does not reorganize data layout or apply clustering changes. REFRESH TABLE reloads metadata from the file system and is used for external tables, not for applying clustering configuration changes.
2. A data analyst receives this error when querying a Delta table: 'Cannot time travel to version 45. Delta log retention has removed this version.' The table has DESCRIBE HISTORY showing 60 versions spanning 45 days. The analyst needs to implement a solution to enable time travel queries up to 90 days while minimizing storage costs. What combination of settings should they configure? (Select two!)
Multiple correct answersASet delta.logRetentionDuration to 90 days to preserve transaction log history
BSet delta.deletedFileRetentionDuration to 90 days to preserve data files referenced by old versions
CRun VACUUM with RETAIN 90 DAYS to preserve old data files
DEnable delta.autoOptimize.optimizeWrite to reduce the number of versions created
ESet delta.checkpointInterval to 1 to create more frequent checkpoints
Explanation
Setting delta.logRetentionDuration to 90 days preserves transaction log history including metadata about versions, while setting delta.deletedFileRetentionDuration to 90 days prevents VACUUM from removing data files needed for time travel. Both properties must be configured because log retention alone doesn't prevent file deletion. Running VACUUM with RETAIN 90 DAYS is a one-time operation that preserves files during that execution but doesn't change the ongoing retention policy, so files would be deleted in subsequent default VACUUM runs. Enabling autoOptimize.optimizeWrite reduces small files but doesn't affect time travel capability or retention duration. Changing checkpoint interval affects log checkpoint frequency but doesn't extend retention periods for time travel queries.
3. A data engineer creates a shallow clone of the production sales_data table in Unity Catalog for testing. The original table uses managed storage. After the data team runs VACUUM on the source sales_data table with default 7-day retention, what happens to the shallow clone? (Select one!)
AThe shallow clone automatically converts to a deep clone to preserve data integrity
BThe shallow clone breaks immediately with FileNotFoundException errors
CThe shallow clone enters read-only mode for 7 days before being deleted
DThe shallow clone continues to function normally because Unity Catalog tracks clone relationships and protects required data files
Explanation
Unity Catalog tracks relationships between all cloned tables and source data files. When VACUUM runs on a source table, the system expands the definition of valid files to include data files necessary for returning queries on any shallow cloned table as well as the source table. This protection mechanism prevents VACUUM from removing files that clones still need. Shallow clones do not automatically convert to deep clones under any circumstances. There is no read-only mode or automatic deletion behavior for shallow clones affected by maintenance operations.
4. A compliance team needs to audit all DELETE operations performed on the customer_transactions table over the past 90 days. The table has delta.logRetentionDuration set to 60 days and delta.deletedFileRetentionDuration set to 30 days. What will happen when the analyst runs DESCRIBE HISTORY customer_transactions to retrieve this information? (Select one!)
AThe command returns operations for only 60 days based on log retention duration
BThe command returns operations for 30 days based on deleted file retention duration
CThe command returns all operations for 90 days because DESCRIBE HISTORY ignores retention settings
DThe command fails with an error because deleted file retention is less than the query period
Explanation
DESCRIBE HISTORY returns operations for 60 days based on the logRetentionDuration setting of 60 days. Table history retention is controlled by delta.logRetentionDuration which defaults to 30 days but has been configured to 60 days for this table. DESCRIBE HISTORY does not ignore retention settings and is limited by the transaction log retention period. The deleted file retention duration controls VACUUM behavior and time travel query capabilities but does not affect DESCRIBE HISTORY output. The command does not fail because DESCRIBE HISTORY only reads transaction log metadata which is available for 60 days.
5. A data analyst optimizes query performance by analyzing execution plans using EXPLAIN. They notice a query performs a full table scan despite filtering on a clustered column. The table has liquid clustering enabled on the filter column and was optimized 2 weeks ago. What is the MOST likely cause? (Select one!)
ANew data inserted since the last OPTIMIZE is not properly clustered
BThe query uses a non-equality filter which cannot leverage clustering
CPhoton engine bypasses liquid clustering for vectorized execution
DEXPLAIN does not show clustering benefits, only physical execution plans
Explanation
Liquid clustering organizes data during OPTIMIZE operations, but new data written after OPTIMIZE may not be optimally clustered until the next OPTIMIZE run. For rapidly growing tables, Databricks recommends scheduling OPTIMIZE every 1-2 hours. The full table scan indicates poor data layout for the filter predicate. EXPLAIN does show data skipping and partition pruning benefits when present, though clustering effects may be visible in metrics rather than plan structure. Photon accelerates queries on clustered data and does not bypass clustering benefits. Liquid clustering supports data skipping for range filters, equality filters, and IN predicates, not just equality.
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