← Back to all products

Databricks DE Associate Prep

$69

Study guide for Databricks Data Engineer Associate covering Spark, Delta Lake, DLT, Unity Catalog, and workflow orchestration.

🏷 v1.0.0
Production-ready
✓ Instant download✓ Lifetime updates✓ MIT licensed✓ MIT license✓ Secure checkout (Stripe)

📁 File Structure 22 files

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
├── interactive.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

DetailValue
**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)
    FileDomainWeight
    `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
    FileDescription
    `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
    FileDescription
    `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

    ... preview truncated, see full README in product download.

    📄 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) # ============================================================================

    📅 Changelog

    v1.0.0 — Initial release.

    Purchases include lifetime updates. Check the product page for the latest version.

    📄 Product Preview

    Try the interactive reader and demo tools below, or get the full product with all content unlocked.

    📖 Interactive Reader (Free Preview)⚙ Try Demo Tools📦 Download Free Sample

    ❓ Frequently Asked Questions

    What license is this under?

    How do I download after purchase?

    Do I get updates?

    What if it doesn't work for me?

    Can I get a refund?

    Is there support?

    Buy Now — $69 Back to Products