Free Practice Questions for Databricks Certified Data Analyst Associate Certification
Study with 390 exam-style practice questions designed to help you prepare for the Databricks Certified Data Analyst Associate. All questions are aligned with the latest exam guide and include detailed explanations to help you master the material.
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Exam Details
Key information about Databricks Certified Data Analyst Associate
- Multiple choice
None is required; related course attendance and six months of hands-on experience as a Data Analyst are highly recommended.
Online proctored or test center proctored
Recertification is required every two years to maintain your certified status. To recertify, you must take the full exam that is currently live.
90 minutes
45 scored multiple-choice questions
2 years
Exam Topics & Skills Assessed
Skills measured (from the official study guide)
Domain 1: Understanding of Databricks Data Intelligence Platform
Subdomain 1.1: Core components of the Databricks Intelligence Platform
Describe the core components of the Databricks Intelligence Platform, including Mosaic AI, DeltaLive tables, Lakeflow Jobs, Data Intelligence Engine, Delta Lake, Unity Catalog, and Databricks SQL.
Subdomain 1.2: Catalog Explorer interface
Understand catalogs, schemas, managed and external tables, access controls, views, certified tables, and lineage within the Catalog Explorer interface.
Subdomain 1.3: Databricks Marketplace
Describe the role and features of Databricks Marketplace.
Domain 2: Managing Data
Subdomain 2.1: Using Unity Catalog for datasets
Use Unity Catalog to discover, query, and manage certified datasets.
Subdomain 2.2: Catalog Explorer for tagging and lineage
Use the Catalog Explorer to tag a data asset and view its lineage.
Subdomain 2.3: Data cleaning in SQL
Perform data cleaning on Unity Catalog Tables in SQL, including removing invalid data or handling missing values.
Domain 3: Importing Data
Subdomain 3.1: Approaches for bringing data into Databricks
Explain the approaches for bringing data into Databricks, covering ingestion from S3, data sharing with external systems via Delta Sharing, API-driven data intake, the Auto Loader feature, and Marketplace.
Subdomain 3.2: Uploading data via Workspace UI
Use the Databricks Workspace UI to upload a data file to the platform.
Domain 4: Executing queries using Databricks SQL and Databricks SQL Warehouses
Subdomain 4.1: Using Databricks Assistant
Utilize Databricks Assistant within a Notebook or SQL Editor to facilitate query writing and debugging.
Subdomain 4.2: Role of SQL Warehouse
Explain the role a SQL Warehouse plays in query execution.
Subdomain 4.3: Cross-system analytics with federated data
Querying cross-system analytics by joining data from a Delta table and a federated data source.
Subdomain 4.4: Materialized views and streaming tables
Create a materialized view, including knowing when to use Streaming Tables and Materialized Views, and differentiate between dynamic and materialized views.
Subdomain 4.5: Aggregate operations
Perform aggregate operations such as count, approximate count distinct, mean, and summary statistics.
Subdomain 4.6: Join and set operations
Write queries to combine tables using various join operations (inner, left, right, and so on) with single or multiple keys, as well as set operations like union and union all, including the differences between the joins (inner, left, right, and so on).
Subdomain 4.7: Sorting and filtering
Perform sorting and filtering operations on a table.
Subdomain 4.8: Creating managed and external tables
Create managed tables and external tables, including creating tables by joining data from multiple sources (e.g., CSV, Parquet, Delta tables) to create unified datasets, including Unity Catalog.
Subdomain 4.9: Delta Lake time travel
Use Delta Lake's time travel to access and query historical data versions.
Domain 5: Analyzing Queries
Subdomain 5.1: Photon features and benefits
Understand the Features, Benefits, and Supported Workloads of Photon.
Subdomain 5.2: Identifying poorly performing queries
Identify poorly performing queries in the Databricks Intelligence platform, such as Query Insights, Query Profiler log, etc.
Subdomain 5.3: Auditing and history with Delta Lake
Utilize Delta Lake to audit and view history, validate results, and compare historical results or trends.
Subdomain 5.4: Query history and caching
Utilize query history and caching to reduce development time and query latency.
Subdomain 5.5: Liquid Clustering
Apply Liquid Clustering to improve query speed when filtering large tables on specific columns.
Subdomain 5.6: Fixing queries
Fix a query to achieve the desired results.
Domain 6: Working with Dashboards and Visualizations in Databricks
Subdomain 6.1: Building dashboards with AI/BI Dashboards
Build dashboards using AI/BI Dashboards, including multi-tabs/page layouts, multiple data sources/datasets, and widgets (visualizations, text, images).
Subdomain 6.2: Creating visualizations
Create visualizations in notebooks and the SQL editor.
Subdomain 6.3: Working with parameters
Work with parameters in SQL queries and dashboards, including defining, configuring, and testing parameters.
Subdomain 6.4: Sharing dashboards
Configure permissions through the UI to share dashboards with workspace users/groups, external users through shareable links, and embed dashboards in external apps.
Subdomain 6.5: Scheduling dashboard refresh
Schedule an automatic dashboard refresh.
Subdomain 6.6: Configuring alerts
Configure an alert with a desired threshold and destination.
Subdomain 6.7: Effective visualization types
Identify the effective visualization type to communicate insights clearly.
Domain 7: Developing, Sharing, and Maintaining AI/BI Genie spaces
Subdomain 7.1: Purpose and features of AI/BI Genie spaces
Describe the purpose, key features, and components of AI/BI Genie spaces.
Subdomain 7.2: Creating Genie spaces
Create Genie spaces by defining reasonable sample questions and domain-specific instructions, choosing SQL warehouses, curating Unity Catalog datasets (tables, views...), and vetting queries as Trusted Assets.
Subdomain 7.3: Assigning permissions and distributing Genie spaces
Assign permissions via the UI and distribute Genie spaces using embedded links and external app integrations.
Subdomain 7.4: Optimizing AI/BI Genie spaces
Optimize AI/BI Genie spaces by tracking user questions, response accuracy, and feedback; updating instructions and trusted assets based on stakeholder input; validating accuracy with benchmarks; refreshing Unity Catalog metadata.
Domain 8: Data Modeling with Databricks SQL
Subdomain 8.1: Industry-standard data modeling techniques
Apply industry-standard data modeling techniques, such as star, snowflake, and data vault schemas, to analytical workloads.
Subdomain 8.2: Alignment with Medallion Architecture
Understand how industry-standard models align with the Medallion Architecture.
Domain 9: Securing Data
Subdomain 9.1: Unity Catalog roles and sharing settings
Use Unity Catalog roles and sharing settings to ensure workspace objects are secure.
Subdomain 9.2: 3-level namespace in Unity Catalog
Understand how the 3-level namespace (Catalog / Schema / Tables or Volumes) works in the Unity Catalog.
Subdomain 9.3: Best practices for data security
Apply best practices for storage and management to ensure data security, including table ownership and PII protection.
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