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 data analyst writes a query using the INTERSECT operator to find products sold in both 2024 and 2025. The query returns fewer results than expected. After investigation, the analyst discovers that product names have inconsistent capitalization across years. Which modification will correctly identify matching products regardless of case? (Select one!)
AAdd COLLATE NOCASE to the INTERSECT operation
BUse INTERSECT ALL instead of INTERSECT to include duplicates
CApply UPPER() function to product names in both queries before INTERSECT
DReplace INTERSECT with INNER JOIN and use case-insensitive comparison
Explanation
Applying UPPER() or LOWER() to normalize product names before the INTERSECT operation ensures case-insensitive matching. This transformation makes 'Widget' and 'WIDGET' identical for comparison purposes. While INNER JOIN with case-insensitive comparison would work, it requires more complex query restructuring. COLLATE NOCASE is not standard SQL syntax in Databricks. INTERSECT ALL includes duplicates but does not address case sensitivity.
2. A data analyst configures a new Genie space for the marketing department. The space must allow users to query customer engagement data stored in Unity Catalog tables. Which two requirements must be met before creating the Genie space? (Select two!)
Multiple correct answersAThe data analyst must have SELECT privileges on the Unity Catalog tables to be included
BThe marketing users must have CREATE TABLE permissions on the catalog
CA Pro or Serverless SQL warehouse must be available with CAN USE permission
DThe Genie space must include at least 10 sample questions
EAll tables must use liquid clustering for optimal performance
Explanation
Creating a Genie space requires SELECT privileges on the Unity Catalog data being included and access to a Pro or Serverless SQL warehouse with CAN USE permission. These are fundamental technical requirements. Marketing end users need SELECT privileges to query data but not CREATE TABLE permissions. Sample questions are optional configuration elements that improve user experience but are not required for space creation. Liquid clustering is a performance optimization technique unrelated to Genie space requirements.
3. A data analyst creates a Delta table with liquid clustering: CREATE TABLE events (event_id BIGINT, event_type STRING, user_id BIGINT, event_date DATE) CLUSTER BY (event_type, event_date). After six months, query patterns change and most queries now filter by user_id instead of event_type. What should the analyst do to optimize for the new query pattern? (Select one!)
ADrop the table and recreate it with the new clustering keys because liquid clustering is immutable
BRun OPTIMIZE ZORDER BY (user_id, event_date) to add secondary clustering
CRun ALTER TABLE events CLUSTER BY (user_id, event_date) followed by OPTIMIZE
DRun VACUUM to remove old clustering metadata and recreate the table with new clustering keys
Explanation
Liquid clustering allows redefining clustering keys without rewriting existing data using ALTER TABLE CLUSTER BY followed by OPTIMIZE. The OPTIMIZE command incrementally reclusters data using the new keys. VACUUM removes old data files based on retention periods, not clustering metadata, and recreating the table is unnecessary. Liquid clustering keys can be changed which is one of its primary advantages over traditional partitioning. Liquid clustering is not compatible with ZORDER and does not support secondary clustering.
4. Litware Corporation is migrating CSV files from an S3 bucket to a Delta table. They need an idempotent loading solution that automatically skips files that have already been processed, preventing duplicate data. New files arrive daily. Which approach should they use? (Select one!)
AUse INSERT INTO with a SELECT statement reading from S3
BUse COPY INTO command pointing to the S3 location
CUse MERGE INTO with file metadata tracking
DUse CREATE TABLE AS SELECT from the S3 path
Explanation
COPY INTO is specifically designed for idempotent batch loading from cloud storage. It automatically tracks which files have been processed and skips them on subsequent runs, preventing duplicates without manual intervention. INSERT INTO always appends data and does not track processed files. CREATE TABLE AS SELECT is a one-time operation and cannot handle incremental loads. MERGE INTO requires custom logic to track file metadata and is more complex than necessary for this use case.
5. A retail analytics team creates a dashboard showing sales performance with filters for region, product category, and date range. Different pages need different default filter values. How should filters be configured? (Select one!)
ACreate page-level filters with page-specific defaults
BCreate separate query parameters for each page
CCreate global filters and override default values per page
DUse widget-level filters for each visualization
Explanation
AI/BI dashboards support page-level filters that allow different default values per page while maintaining filter consistency within each page. This provides the flexibility needed for different pages to show different default views. Global filters apply workspace-wide with the same defaults across all pages. Separate query parameters per page creates maintenance overhead and does not provide the integrated filtering experience. Widget-level filters apply to individual visualizations but require more configuration than page-level filters for the described scenario.
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