Free Practice Questions for Databricks Certified Data Analyst Associate Certification

1.1: 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.

1.2: Understand catalogs, schemas, managed and external tables, access controls, views, certified tables, and lineage within the Catalog Explorer interface.

1.3: Describe the role and features of Databricks Marketplace.

2.1: Use Unity Catalog to discover, query, and manage certified datasets.

2.2: Use the Catalog Explorer to tag a data asset and view its lineage.

2.3: Perform data cleaning on Unity Catalog Tables in SQL, including removing invalid data or handling missing values.

3.1: 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.

3.2: Use the Databricks Workspace UI to upload a data file to the platform.

4.1: Utilize Databricks Assistant within a Notebook or SQL Editor to facilitate query writing and debugging.

4.2: Explain the role a SQL Warehouse plays in query execution.

4.3: Querying cross-system analytics by joining data from a Delta table and a federated data source.

4.4: Create a materialized view, including knowing when to use Streaming Tables and Materialized Views, and differentiate between dynamic and materialized views.

4.5: Perform aggregate operations such as count, approximate count distinct, mean, and summary statistics.

4.6: 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).

4.7: Perform sorting and filtering operations on a table.

4.8: 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.

4.9: Use Delta Lake's time travel to access and query historical data versions.

5.1: Understand the Features, Benefits, and Supported Workloads of Photon.

5.2: Identify poorly performing queries in the Databricks Intelligence platform, such as Query Insights, Query Profiler log, etc.

5.3: Utilize Delta Lake to audit and view history, validate results, and compare historical results or trends.

5.4: Utilize query history and caching to reduce development time and query latency

5.5: Apply Liquid Clustering to improve query speed when filtering large tables on specific columns.

5.6: Fix a query to achieve the desired results.

6.1: Build dashboards using AI/BI Dashboards, including multi-tabs/page layouts, multiple data sources/datasets, and widgets (visualizations, text, images).

6.2: Create visualizations in notebooks and the SQL editor.

6.3: Work with parameters in SQL queries and dashboards, including defining, configuring, and testing parameters.

6.4: Configure permissions through the UI to share dashboards with workspace users/groups, external users through shareable links, and embed dashboards in external apps.

6.5: Schedule an automatic dashboard refresh.

6.6: Configure an alert with a desired threshold and destination.

6.7: Identify the effective visualization type to communicate insights clearly.

7.1: Describe the purpose, key features, and components of AI/BI Genie spaces.

7.2: 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.

7.3: Assign permissions via the UI and distribute Genie spaces using embedded links and external app integrations.

7.4: 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.

8.1: Apply industry-standard data modeling techniques, such as star, snowflake, and data vault schemas, to analytical workloads.

8.2: Understand how industry-standard models align with the Medallion Architecture.

9.1: Use Unity Catalog roles and sharing settings to ensure workspace objects are secure.

9.2: Understand how the 3-level namespace(Catalog / Schema / Tables or Volumes) works in the Unity Catalog.

9.3: Apply best practices for storage and management to ensure data security, including table ownership and PII protection.