Databricks-Data-Engineer-Associate
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Data Engineer Associate (Databricks®-Data-Engineer-Associate)
The Databricks Certified Data Engineer Associate exam validates expertise in building ELT pipelines with Apache Spark, leveraging Delta Lake for incremental processing, orchestrating production workflows, and applying Unity Catalog governance.
120
Minutes
45
Questions
70/100
Passing Score
$200
Exam Cost
1
Languages
Who Should Take This
It is intended for data engineers who have at least six months of hands‑on experience on Databricks, regularly design and maintain ELT workflows, and seek formal recognition of their ability to implement scalable, governed data solutions. They also aim to deepen their mastery of Delta Lake versioning, incremental processing patterns, and Unity Catalog security controls to advance their careers.
What's Covered
1
Extract, load, and transform data using Apache Spark DataFrames and SQL, including complex data types, joins, aggregations, and UDFs.
2
Create and manage Delta tables with ACID transactions, schema enforcement and evolution, time travel, optimization, and table maintenance.
3
Build incremental data pipelines using Structured Streaming, Auto Loader, and MERGE operations for CDC and slowly changing dimensions.
4
Develop production-grade pipelines using Delta Live Tables, medallion architecture, and Databricks Workflows with monitoring and orchestration.
5
Implement data governance using Unity Catalog including namespace hierarchy, access controls, dynamic views, data lineage, and audit capabilities.
Exam Structure
Question Types
- Multiple Choice
- Multiple Select
Scoring Method
Percentage-based scoring with a 70% minimum passing threshold
Delivery Method
Kryterion online proctored or testing center
Recertification
Recertify every 2 years by passing the current version of the exam.
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
59 learning goals
1
ELT with Apache Spark
4 topics
Spark DataFrame Fundamentals
- Identify the components of the Spark execution model including driver, executors, stages, and tasks and describe how lazy evaluation defers computation until an action is triggered
- Implement DataFrame creation from various sources including CSV, JSON, Parquet, and Delta formats using spark.read with appropriate schema inference and explicit schema definitions
- Implement DataFrame transformations using select, filter, withColumn, drop, and withColumnRenamed to reshape data for downstream consumption
- Describe the difference between transformations and actions in Spark and explain how the DAG scheduler optimizes execution plans across stages
Data Manipulation and Aggregation
- Implement groupBy aggregations with agg, count, sum, avg, min, and max functions to compute summary statistics across partitioned datasets
- Implement join operations including inner, left, right, outer, cross, and semi joins on DataFrames and explain how broadcast joins optimize performance for small-to-large table joins
- Implement window functions including row_number, rank, dense_rank, lead, and lag with partitionBy and orderBy to compute running calculations and rankings within groups
- Implement pivot and unpivot operations to reshape data between wide and long formats for analytical processing
Complex and Nested Data
- Implement extraction and flattening of nested JSON structures using explode, posexplode, and dot notation to transform semi-structured data into tabular format
- Implement operations on array and map column types using array_contains, element_at, transform, and map_keys to query and transform complex nested fields
- Analyze the trade-offs between storing data in nested structures versus flattened schemas and evaluate when each approach optimizes query performance and storage efficiency
SQL and Spark SQL
- Implement Spark SQL queries using spark.sql() and temporary views to perform transformations using standard SQL syntax within Databricks notebooks
- Implement Common Table Expressions (CTEs), subqueries, and higher-order functions in Spark SQL to build multi-step transformation logic
- Implement user-defined functions (UDFs) in Python and register them for use in Spark SQL and evaluate the performance overhead of Python UDFs versus native Spark functions
2
Delta Lake
3 topics
Delta Lake Fundamentals
- Describe the Delta Lake transaction log architecture including the _delta_log directory, JSON commit files, and checkpoint files and explain how they provide ACID guarantees
- Implement Delta table creation using CREATE TABLE, CTAS, and DataFrame write operations with the delta format and configure table properties for optimization
- Describe how Delta Lake enables time travel using VERSION AS OF and TIMESTAMP AS OF to query historical table states and explain transaction log retention policies
- Implement RESTORE TABLE operations to roll back Delta tables to previous versions and analyze scenarios where time travel recovery is appropriate versus point-in-time backups
Schema Management and Data Quality
- Describe how Delta Lake schema enforcement rejects writes that do not match the target table schema and explain the difference between schema enforcement and schema evolution
- Implement schema evolution using mergeSchema and overwriteSchema options to accommodate upstream schema changes in evolving data pipelines
- Implement Delta Lake constraints including NOT NULL and CHECK constraints to enforce data quality rules at the storage layer
- Analyze the impact of schema evolution strategies on downstream consumers and evaluate when to use additive evolution versus full schema replacement
Delta Table Optimization
- Implement OPTIMIZE and ZORDER BY commands to compact small files and co-locate related data for improved query performance on Delta tables
- Implement VACUUM operations to remove stale data files from Delta tables and explain how the retention threshold interacts with time travel capabilities
- Analyze Delta table storage metrics using DESCRIBE DETAIL and DESCRIBE HISTORY to identify tables that need optimization and diagnose performance degradation
3
Incremental Data Processing
3 topics
Structured Streaming
- Describe the Structured Streaming execution model including micro-batch processing, trigger intervals, and the concept of streaming DataFrames as unbounded tables
- Implement streaming reads from Delta tables and file sources using readStream and configure output modes including append, complete, and update for different use cases
- Implement writeStream with checkpoint locations and trigger configurations including trigger(availableNow=True) for incremental batch processing
- Analyze the role of checkpointing in exactly-once processing guarantees and evaluate how checkpoint corruption affects stream recovery
Auto Loader
- Describe how Auto Loader uses cloudFiles format to incrementally ingest new files from cloud storage and explain the difference between directory listing and file notification modes
- Implement Auto Loader ingestion pipelines with schema inference, schema evolution, and rescued data columns to handle evolving file-based data sources
- Analyze when to use Auto Loader versus COPY INTO for file ingestion and evaluate the scalability advantages of file notification mode for high-volume landing zones
Change Data Capture and MERGE
- Describe Change Data Capture (CDC) patterns for propagating inserts, updates, and deletes from source systems and explain how CDC feeds maintain data currency in lakehouse architectures
- Implement MERGE INTO operations with match and not-matched conditions to apply CDC changes including upserts and soft deletes to Delta tables
- Implement Type 1 and Type 2 slowly changing dimension patterns using MERGE operations on Delta tables to maintain historical dimension state
- Analyze the performance implications of MERGE operations on large Delta tables and evaluate strategies such as partition pruning and merge conditions to optimize write amplification
4
Production Pipelines
3 topics
Delta Live Tables
- Describe the Delta Live Tables (DLT) declarative pipeline framework and explain how it differs from imperative Spark pipeline development
- Implement DLT pipelines using @dlt.table and @dlt.view decorators to define streaming and batch tables with automatic dependency resolution
- Implement data quality expectations in DLT using @dlt.expect, @dlt.expect_or_drop, and @dlt.expect_or_fail to enforce data quality rules with configurable failure actions
- Implement CDC processing in DLT using apply_changes to automatically handle insert, update, and delete operations from CDC feeds into target tables
- Analyze DLT pipeline event logs to diagnose data quality violations, pipeline failures, and backfill scenarios using the event_log system table
Medallion Architecture
- Describe the medallion architecture pattern with bronze, silver, and gold layers and explain the data quality and transformation expectations at each layer
- Implement bronze layer ingestion that preserves raw source data with metadata columns including ingestion timestamp, source file path, and processing status
- Implement silver layer transformations that cleanse, deduplicate, and conform data with enforced schemas and quality constraints for analytical consumption
- Implement gold layer aggregation tables and business-level views that serve specific analytical use cases with pre-computed metrics and denormalized dimensions
- Analyze the trade-offs of medallion layer granularity and evaluate when to add intermediate layers or skip layers based on data volume, latency requirements, and consumer needs
Workflow Orchestration
- Implement Databricks Workflows with multi-task jobs including notebook tasks, DLT pipeline tasks, and SQL tasks with dependency configuration and retry policies
- Implement job scheduling with cron-based triggers, file arrival triggers, and continuous execution modes for production data pipeline orchestration
- Analyze job run diagnostics using the Workflows UI and job run metadata to identify bottlenecks, failed tasks, and cluster utilization patterns in production pipelines
5
Data Governance with Unity Catalog
3 topics
Unity Catalog Architecture
- Describe the Unity Catalog three-level namespace hierarchy of catalog, schema, and object and explain how it provides centralized governance across workspaces
- Describe the relationship between metastore, catalogs, and storage credentials in Unity Catalog and explain how external locations map to cloud storage paths
- Implement catalog, schema, and table creation within Unity Catalog and configure managed versus external table storage locations
Access Control and Permissions
- Implement GRANT and REVOKE statements to manage permissions on catalogs, schemas, tables, and views for users and groups in Unity Catalog
- Describe the privilege inheritance model in Unity Catalog and explain how permissions cascade from catalogs through schemas to tables and views
- Implement dynamic views with column-level and row-level security using current_user() and is_member() functions to restrict data access based on user identity
- Analyze access control strategies for multi-team data environments and evaluate the principle of least privilege applied to Unity Catalog permission design
Data Lineage and Audit
- Describe how Unity Catalog captures table-level and column-level data lineage automatically and explain how lineage visualization aids impact analysis
- Implement data discovery using Unity Catalog tags, comments, and search to enable self-service data exploration across organizational data assets
- Analyze audit logs and system tables to track data access patterns, permission changes, and compliance events across Unity Catalog-governed assets
Certification Benefits
Salary Impact
$140,000
Average Salary
Related Job Roles
Data Engineer
Analytics Engineer
Data Platform Engineer
ETL Developer
Lakehouse Engineer
Industry Recognition
The Databricks Data Engineer Associate certification validates practical data engineering skills on the Databricks Lakehouse Platform. As organizations increasingly adopt lakehouse architectures, this certification demonstrates proficiency with the leading platform for unified analytics and data engineering.
Scope
Included Topics
- All domains in the Databricks Certified Data Engineer Associate exam: ELT with Apache Spark and Delta Lake, incremental data processing with Structured Streaming and Auto Loader, production pipeline development with Delta Live Tables, data governance with Unity Catalog, and Databricks tooling including Repos, Workflows, and the Databricks CLI.
- Core Spark DataFrame API operations for data transformation, schema enforcement, schema evolution, and data quality constraints in Delta Lake.
- Medallion architecture patterns (bronze, silver, gold) for organizing data lakehouse layers.
- Change Data Capture (CDC) patterns, MERGE INTO operations, and incremental ETL strategies using Delta Lake.
- Unity Catalog concepts including metastore hierarchy, data access controls, data lineage, and audit logging.
Not Covered
- Advanced Spark internals such as custom partitioners, Catalyst optimizer deep-dive, and Tungsten memory management.
- Databricks Machine Learning runtime, MLflow, and Feature Store topics covered by the ML Associate exam.
- Cloud provider-specific infrastructure setup (AWS IAM, Azure AD, GCP IAM) beyond Databricks workspace configuration.
- Databricks SQL warehouse administration and BI-focused query optimization.
- Low-level JVM tuning, Spark on YARN/Mesos, and non-Databricks Spark deployment models.
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