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Databricks Certified Data Engineer Associate Syllabus

Databricks Intelligence Platform

Core platform architecture and choosing compute for workloads.

6%
Weight
3
Questions
6
Marks

Core Components

  • Platform architecture (control and compute planes)
  • Delta Lake fundamentals
  • Unity Catalog object model
  • Workspace and catalog relationships

Compute Selection

  • Serverless vs. classic compute
  • SQL warehouses vs. job and all-purpose compute
  • Compute cost models
  • Matching compute to workload needs

Data Ingestion and Loading

Batch, streaming and incremental ingestion into Unity Catalog tables.

21%
Weight
9
Questions
21
Marks

File Ingestion

  • Auto Loader incremental ingestion
  • COPY INTO for idempotent loads
  • Schema enforcement and schema evolution
  • Ingesting semi-structured and unstructured data

Connectors and Sources

  • Lakeflow Connect standard and managed connectors
  • JDBC/ODBC and REST ingestion orchestrated by Lakeflow Jobs
  • Partner connector options
  • Choosing the right ingestion method for a scenario

Data Transformation and Modeling

Cleaning, joining and modeling data across the medallion layers.

22%
Weight
10
Questions
22
Marks

Cleaning and Shaping

  • Bronze-to-silver cleaning (nulls, types)
  • Splitting, renaming and filtering columns and rows
  • Exploding arrays
  • Deduplication

Joins and Aggregations

  • Inner, left, broadcast, multi-key and cross joins
  • UNION vs. UNION ALL
  • Aggregations
  • Basic Spark tuning and re-measuring performance

Gold Layer and Quality

  • Materialized views vs. streaming tables vs. views
  • Lakeflow Declarative Pipelines datasets
  • Data quality expectations
  • Validation rules

Working with Lakeflow Jobs

Orchestrating tasks with dependencies, control flow and triggers.

16%
Weight
7
Questions
16
Marks

Tasks and Control Flow

  • Notebook, SQL, dashboard and pipeline task types
  • DAG-based task dependencies
  • Retries and conditional (If/Else) tasks
  • For Each looping

Schedules and Triggers

  • Scheduled (time-based) triggers
  • File arrival triggers
  • Table update triggers
  • Choosing time-based vs. data-driven triggers

Implementing CI/CD

Version control and promotion of assets with Git folders and bundles.

10%
Weight
5
Questions
10
Marks

Git Folders

  • Branching and commits
  • Pushing and pulling changes
  • Pull request workflows
  • Resolving merge conflicts in Git folders

Declarative Automation Bundles

  • Bundle variables and environment overrides
  • Promoting jobs and pipelines across dev, test and prod
  • Databricks CLI bundle validate and deploy
  • Managing deployed bundle resources

Troubleshooting, Monitoring, and Optimization

Monitoring job health and resolving performance and compute issues.

10%
Weight
4
Questions
10
Marks

Monitoring

  • Job run history and performance trends
  • Task graphs, statuses and failure rates
  • Spark UI stage metrics for skew, shuffle and spill
  • Job notifications on failure

Optimization and Fixes

  • Liquid Clustering
  • Predictive optimization
  • Cluster start-up failures and library conflicts
  • Out-of-memory errors

Governance and Security

Securing data with Unity Catalog privileges, masks and ABAC.

15%
Weight
7
Questions
15
Marks

Tables and Privileges

  • Managed vs. external tables and conversion
  • GRANT, REVOKE and DENY
  • Privilege inheritance across the hierarchy
  • Object ownership in Unity Catalog

Fine-grained Access

  • Row filters by user group
  • Column masks
  • ABAC policies with governed tags
  • Dynamic views for access control

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Learning Progress

Databricks Intelligence Platform 85%
Data Ingestion and Loading 92%

Practice Test Scores

95%
Latest Score
Above passing threshold

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