Databricks Certified Data Engineer Associate Preparation Details
The Databricks Certified Data Engineer Associate exam tests your ability to build ETL pipelines and manage Delta Lake tables on the Data Intelligence Platform. This guide covers every exam domain, from ingestion and transformation to CI/CD and governance, with official documentation for each objective. You can also explore more Databricks certification study guides on the Databricks category to keep building your skills.
Databricks Certified Data Engineer Associate Materials
| Coursera | Databricks Lakehouse Fundamentals |
| Udemy | Databricks Data Engineer Associate – Preparation |
| Whizlabs | Databricks Data Engineer Associate Certification |
Section 1: Databricks Intelligence Platform (6%)
Topics Covered
Understand the core components of the Databricks Data Intelligence Platform, such as its architecture, Delta Lake, and Unity Catalog.
The scope of the Databricks platform
What is Delta Lake in Databricks?
Understand Databricks Data Intelligence Platform’s compute services, including their characteristics, limitations, and cost models, and select the most suitable option for each workload use case.
Set up serverless SQL warehouses
Section 2: Data Ingestion and Loading (21%)
Topics Covered
Enable and detail data ingestion patterns, including batch, streaming, and incremental loading, and import data from sources such as local files, Lakeflow Connect standard connectors, and Lakeflow Connect managed connectors.
Standard connectors in Lakeflow Connect
Managed connectors in Lakeflow Connect
Create or modify a table using file upload
Use the COPY INTO command to incrementally load files from cloud object storage (ADLS/S3/GCS) into Unity-Catalog-governed tables.
Get started using COPY INTO to load data
Load data using COPY INTO with an instance profile
Tutorial: COPY INTO with Spark SQL
Use Auto Loader with schema enforcement and schema evolution in batch modes (for example, directory listing or file notification) to land data into Unity Catalog-governed tables.
Configure schema inference and evolution in Auto Loader
Configure Auto Loader streams in directory listing mode
Configure Auto Loader streams in file notification mode
Configure Lakeflow Connect to reliably ingest data from diverse enterprise sources into Unity Catalog-governed tables.
Salesforce ingestion connector
Microsoft SQL Server connector concepts
Database connectors in Lakeflow Connect
Managed connectors in Lakeflow Connect
Use JDBC/ODBC or REST clients in notebooks to land data into cloud storage or directly into Unity Catalog-governed tables, usually orchestrated and scheduled with Lakeflow Jobs.
Databricks ODBC and JDBC Drivers
Statement Execution API: Run SQL on warehouses
Prioritize between Auto Loader, Lakeflow Connect (standard and managed connectors), partner connectors, and other ingestion methods based on technical requirements such as data volume, ingestion frequency, data types, and governance needs with Unity Catalog.
Connect to ingestion partners using Partner Connect
Standard connectors in Lakeflow Connect
Ingest semi-structured and unstructured data (for example, JSON and nested data) via Lakeflow Connect and other managed connectors into Unity Catalog-governed Delta tables.
Section 3: Data Transformation and Modeling (22%)
Topics Covered
Implement data cleaning by reading bronze tables with PySpark/SQL, cleaning nulls, standardizing data types, and writing to new silver tables.
What is the medallion lakehouse architecture?
Combine DataFrames with operations such as Inner join, left join, broadcast join, multiple keys, cross join, union, and union all.
Manipulate columns, rows, and table structures by adding, dropping, splitting, renaming column names, applying filters, and exploding arrays.
Perform data deduplication operations and aggregate operations on DataFrames, such as count, approximate count distinct, and mean, summary.
Understand the basic tuning parameters (spark.sql.shuffle.partitions:, spark.default.parallelism, spark.executor/driver.memory, spark.sql.autoBroadcastJoinThreshold) and re-measure the performance.
Set Spark configuration properties on Databricks
Compute configuration reference
Understand the difference between, and how to build, Gold layer objects such as materialized views, views, streaming tables, and tables for BI and analytics teams in Unity Catalog.
Apply data quality checks and validation rules to ensure reliable Silver and Gold datasets.
Manage data quality with pipeline expectations
Expectation recommendations and advanced patterns
Section 4: Working with Lakeflow Jobs (16%)
Topics Covered
Implement control flows (retries and conditional tasks such as branching and looping) using Lakeflow Jobs for pipeline orchestration
Control the flow of tasks within Lakeflow Jobs
Add branching logic to a job with the If/else task
Use a For each task to run another task in a loop
Configure common tasks (notebook, SQL query, dashboard, and pipeline tasks) and their dependencies using Lakeflow Jobs and its DAG-based task graph
Implement job schedules using Lakeflow Jobs with an understanding of trigger types (scheduled, file arrival, and table update)
Automating jobs with schedules and triggers
Trigger jobs when new files arrive
Trigger jobs when source tables are updated
Choose between time-based and data-driven triggers based on data availability and pipeline dependencies.
Automating jobs with schedules and triggers
Trigger jobs when new files arrive
Trigger jobs when source tables are updated
Section 5: Implementing CI/CD (10%)
Topics Covered
Manage your code development workflow within the Databricks workspace UI, including creating and switching between branches in Databricks Git Folders (formerly Databricks Repos), committing and pushing changes, and creating pull requests using Databricks Git integration.
Databricks Git folders concepts
CI/CD with Databricks Git folders
Understand environment-specific configuration using Automation Bundle (formerly Databricks Asset Bundles) variables and overrides while promoting the same codebase across dev, test, and prod targets.
What are Declarative Automation Bundles?
Substitutions and variables in Declarative Automation Bundles
Declarative Automation Bundles configuration
Declarative Automation Bundles deployment modes
Deploy Declarative Automation Bundles (formerly Databricks Asset Bundles) to package, configure, and promote Lakeflow Jobs, Lakeflow Spark Declarative Pipelines, and other workspace assets across dev, test, and prod environments.
Develop Declarative Automation Bundles
Deploy bundles and run workflows from the workspace
Declarative Automation Bundles deployment modes
Understand the Databricks CLI to validate, deploy, and manage Declarative Automation Bundles (formerly Databricks Asset Bundles) and other workspace assets in automated CI/CD workflows.
Install or update the Databricks CLI
Develop Declarative Automation Bundles
Section 6: Troubleshooting, Monitoring, and Optimization (10%)
Topics Covered
Identify trends in job performance using the Lakeflow Jobs run history view to compare current execution times against historical baselines.
Monitoring and observability for Lakeflow Jobs
Use the Lakeflow Jobs UI to monitor pipeline health by interpreting job statuses, viewing DAG-based task graphs to spot upstream blockers, and tracking pipeline run times and failure rates.
Monitoring and observability for Lakeflow Jobs
Identify common performance bottlenecks such as data skew, shuffling, and disk spilling by interpreting stage-level metrics in the Spark UI.
Diagnose cost and performance issues using the Spark UI
Diagnosing a long job in Spark
Understand the features of Liquid Clustering and predictive optimization.
Use liquid clustering for tables
Predictive optimization for Unity Catalog managed tables
Diagnose cluster startup failures, library conflicts, and out-of-memory issues.
Classic compute termination error codes
Notebook-scoped Python libraries
Section 7: Governance and Security (15%)
Topics Covered
Differentiate between managed and external tables in Unity Catalog and perform basic operations (create, modify, delete, and convert between managed and external tables) on them.
Managed versus external assets in Unity Catalog
Unity Catalog managed tables in Databricks for Delta Lake and Apache Iceberg
Convert an external table to a managed Unity Catalog table
Configure access controls using the UI and SQL by applying GRANT, REVOKE, and DENY privileges to principals (users, groups, and service principals) at appropriate levels of the security hierarchy.
Manage privileges in Unity Catalog
Privileges and securable objects in Unity Catalog
Understand column-level masking and row-level security to restrict data visibility based on user groups.
Manually apply row filters and column masks
When to use ABAC vs table-level row filters and column masks
Understand Unity Catalog ABAC policies to centrally control row-level filtering and column masking for sensitive data.
Attribute-based access control in Unity Catalog
Core concepts for attribute-based access control (ABAC)
Wrapping Up Databricks Certified Data Engineer Associate
This guide walked through all seven domains of the Databricks Certified Data Engineer Associate exam guide, covering the Data Intelligence Platform, ingestion, transformation, Lakeflow Jobs, CI/CD, and governance with Unity Catalog. With official documentation mapped to every objective, you now have a clear path to prepare for the DE Associate exam with confidence. You can also explore more Databricks certification study guides on the Databricks category to keep building your skills. Have a question or tip? Leave a comment below.
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