Databricks Certified Data Engineer Associate

Enable features that simplify data layout decisions and optimize query performance. Explain the value of the Data Intelligence Platform. Identify the applicable compute to use for a specific use case.

Use Databricks Connect in a data engineering workflow. Determine the capabilities of the Notebooks functionality. Classify valid Auto Loader sources and use cases. Demonstrate knowledge of Auto Loader syntax. Use Databricks built-in debugging tools to troubleshoot a given issue.

Describe the three layers of the Medallion Architecture and explain the purpose of each layer in a data processing pipeline. Classify the type of cluster and configuration for optimal performance based on the scenario in which the cluster is used. Emphasize the advantages of Lakeflow Spark Declarative Pipelines for ETL in Databricks. Implement data pipelines using Lakeflow Spark Declarative Pipelines. Identify DDL (Data Definition Language) and DML features. Compute complex aggregations and metrics with PySpark DataFrames.

Identify the difference between DAB and traditional deployment methods. Identify the structure of Asset Bundles. Deploy a workflow, repair, and rerun a task in case of failure. Use serverless for hands-off, auto-optimized compute managed by Databricks. Analyze the Spark UI to optimize the query.

Explain the difference between managed and external tables. Identify the grant of permissions to users and groups within Unity Catalog. Identify key roles in Unity Catalog. Identify how audit logs are stored. Use lineage features in Unity Catalog. Use the Delta Sharing feature available with Unity Catalog to share data. Identify the advantages and limitations of Delta Sharing. Identify the types of Delta Sharing: Databricks vs external systems. Analyze the cost considerations of data sharing across clouds. Identify use cases of Lakehouse Federation when connected to external sources.