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Databricks DE Associate Prep
Study guide for Databricks Data Engineer Associate covering Spark, Delta Lake, DLT, Unity Catalog, and workflow orchestration.
YAMLJSONMarkdownPythonSQLDatabricksPySparkSparkDelta LakeRedis
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databricks-de-associate/
├── LICENSE
├── README.md
├── config.example.yaml
├── examples/
│ ├── delta-lake-operations.py
│ ├── dlt-pipeline-example.py
│ ├── pyspark-examples.py
│ ├── spark-sql-examples.sql
│ └── structured-streaming-example.py
├── free-sample.zip
├── guide/
│ ├── 01-overview.md
│ ├── 02-lakehouse-platform-architecture.md
│ └── 03-elt-with-spark-sql-and-pyspark.md
├── index.html
├── practice-questions/
│ ├── answer-key.md
│ └── questions.md
├── quick-reference/
│ └── cheatsheet.md
└── study-guide/
├── 01-lakehouse-platform.md
├── 02-elt-spark-sql-pyspark.md
├── 03-incremental-data-processing.md
├── 04-production-pipelines.md
└── 05-data-governance.md
📖 Documentation Preview README excerpt
Databricks Certified Data Engineer Associate — Exam Prep Kit
Complete study guide, 45 practice questions with explained answers, PySpark & SQL code examples, and an exam-day cheatsheet for the Databricks Certified Data Engineer Associate certification.
Exam Facts at a Glance
| Detail | Value |
|---|---|
| Exam Code | Databricks Certified Data Engineer Associate |
| Number of Questions | 45 multiple-choice |
| Duration | 90 minutes |
| Passing Score | 70% (approximately 32/45 correct) |
| Cost | $200 USD |
| Format | Proctored online or at a test center |
| Prerequisites | None required (6+ months hands-on recommended) |
| Validity | 2 years |
| Languages | English |
Who This Is For
- Data engineers with 3–12 months of experience on the Lakehouse platform who want to validate their skills
- SQL analysts transitioning into data engineering roles who need structured Spark/Delta Lake knowledge
- Career changers targeting data engineering roles who want a recognized credential
- Experienced engineers who use the platform daily but want to fill knowledge gaps before sitting the exam
What's Inside
Study Guide (5 Domain Files)
| File | Domain | Weight |
|---|---|---|
study-guide/01-lakehouse-platform.md | Databricks Lakehouse Platform | ~24% |
study-guide/02-elt-spark-sql-pyspark.md | ELT with Spark SQL and PySpark | ~29% |
study-guide/03-incremental-data-processing.md | Incremental Data Processing | ~22% |
study-guide/04-production-pipelines.md | Production Pipelines | ~16% |
study-guide/05-data-governance.md | Data Governance | ~9% |
Each domain file includes:
- Concept explanations with real-world context
- Key terminology tables
- "Exam Tip" callouts for high-probability question topics
- Code examples (PySpark and SQL) you can run in a notebook
Practice Questions
| File | Description |
|---|---|
practice-questions/questions.md | 45 realistic multiple-choice questions across all 5 domains |
practice-questions/answer-key.md | Full answer key with detailed explanations for every option |
Code Examples
| File | Description |
|---|---|
examples/pyspark-examples.py | 20+ PySpark patterns: reads, writes, transforms, joins, window functions |
examples/spark-sql-examples.sql | 25+ Spark SQL statements: DDL, DML, CTAS, MERGE, CDF queries |
examples/delta-lake-operations.py | Delta-specific operations: time travel, OPTIMIZE, VACUUM, constraints |
examples/structured-streaming-example.py | Streaming patterns: Auto Loader, watermarks, triggers, output modes |
examples/dlt-pipeline-example.py | Declarative pipeline definitions with expectations and quality rules |
... continues with setup instructions, usage examples, and more.
📄 Code Sample .py preview
examples/delta-lake-operations.py
# ============================================================================
# Delta Lake Operations — Databricks DE Associate Exam Prep
# ============================================================================
# Key Delta Lake operations: time travel, OPTIMIZE, VACUUM, constraints,
# Change Data Feed (CDF), and table maintenance.
# ============================================================================
from pyspark.sql import functions as F
from delta.tables import DeltaTable
CATALOG = "catalog_gold_analytics"
SCHEMA = "sales_data"
TABLE = f"{CATALOG}.{SCHEMA}.orders"
# ============================================================================
# 1. TIME TRAVEL — Query Previous Versions
# ============================================================================
# Query a specific version of the table
df_v3 = (spark.read
.format("delta")
.option("versionAsOf", 3)
.table(TABLE)
)
print(f"Version 3 had {df_v3.count()} rows")
# Query by timestamp
df_yesterday = (spark.read
.format("delta")
.option("timestampAsOf", "2025-06-15T00:00:00Z")
.table(TABLE)
)
# View table history (all versions and their operations)
# In SQL: DESCRIBE HISTORY catalog_gold_analytics.sales_data.orders
delta_table = DeltaTable.forName(spark, TABLE)
history = delta_table.history()
history.select("version", "timestamp", "operation", "operationMetrics").show(truncate=False)
# ============================================================================
# 2. RESTORE — Roll Back to a Previous Version
# ============================================================================
# SQL: RESTORE TABLE catalog_gold_analytics.sales_data.orders TO VERSION AS OF 3
# PySpark equivalent
delta_table = DeltaTable.forName(spark, TABLE)
delta_table.restoreToVersion(3)
# Restore by timestamp
# delta_table.restoreToTimestamp("2025-06-15T00:00:00Z")
# ... 179 more lines ...