Databricks Certified Data Engineer Associate Study Guide

ABSTRACT
This study guide prepares candidates for the Databricks
Data Engineer Associate certification through concise
explanations and hands-on assignments. It covers key
concepts including the Databricks Lakehouse
architecture, data ingestion using Auto Loader and COPY
INTO, Spark DataFrame transformations, and Delta Lake
features such as schema enforcement, time travel, and
optimizations. Performance tuning techniques like
partitioning, caching, and bucketing are also introduced.
The guide includes five practical assignments using the
NYC Taxi dataset, guiding learners through the medallion
architecture: Bronze (raw ingestion), Silver (cleansed
data), and Gold (aggregated analytics). It concludes with
SQL-based analysis and dashboard creation using

DATABRICKS Databricks SQL.

Meenhorst, Lauren

ASSOCIATE
Study guide

,CONTENTS
Databricks Data Engineer Associate – Study Guide and Assignments .................................................................. 2

1. Databricks Lakehouse Architecture and Components ................................................................................. 2

2. Data Ingestion Techniques – Auto Loader and COPY INTO .......................................................................... 3

3. Transformations using Spark DataFrames API ............................................................................................. 4

4. Delta Lake Fundamentals (Schema Enforcement, Time Travel, Optimizations) .......................................... 5

5. Performance Tuning Basics (Partitioning, Caching, Bucketing) .................................................................... 7

6. Databricks SQL Fundamentals (Queries, Aggregations, Visualization) ........................................................ 8

7. Hands-On Practice Assignments................................................................................................................... 9

Assignment 1: Ingesting Data with Auto Loader (Bronze Layer Ingestion) ............................................... 10

Assignment 2: Batch Loading with COPY INTO (Alternative Ingestion Method) ....................................... 11

Assignment 3: Data Transformation and Cleansing (Silver Layer with Spark DataFrames) ...................... 12

Assignment 4: Delta Lake Time Travel and Optimization (Gold Layer + Data Optimization) .................... 13

Assignment 5: Analysis and Visualization with Databricks SQL ................................................................. 16

, DATABRICKS DATA ENGINEER ASSOCIATE – STUDY
GUIDE AND ASSIGNMENTS
This study guide covers all key topics for the Databricks Data Engineer Associate certification. Each
section highlights core concepts, provides examples or sample questions, and includes hands-on
exercises that simulate real-world data engineering tasks. The guide spans the Databricks Lakehouse
architecture, data ingestion, Spark DataFrame transformations, Delta Lake fundamentals, performance
tuning, and Databricks SQL for analytics and visualization.




1. Databricks Lakehouse Architecture and Components
The Databricks Lakehouse is a unified data platform that combines the capabilities of data lakes
(scalable storage) and data warehouses (performance, ACID transactions). Key components of the
Lakehouse architecture include:

1. Apache Spark Engine – Databricks is built on Apache Spark, a distributed processing engine for big
data. Compute is decoupled from storage for scalability.
2. Delta Lake – An optimized storage layer built on open-source Delta Lake that adds ACID transactions,
schema enforcement, and versioning on top of data lake files. Delta Lake ensures reliability and data
quality in the Lakehouse.
3. Unity Catalog – A unified governance and catalog service. It provides fine-grained access control,
data lineage, and a central metadata store across all data assets.
4. Databricks Workspace & Tools – Interactive notebooks, jobs for scheduling, and other workspace
components that allow collaboration and production workflows on data.

Medallion Architecture (Bronze/Silver/Gold): Databricks recommends structuring data pipelines in
layered tiers for incremental quality improvement.

• Bronze layer: Raw ingested data (often landing from source systems in raw form).

• Silver layer: Cleaned and refined data (after transformations, validations, joins).

• Gold layer: Aggregated or business-level data ready for analytics or data science.

This medallion design is an application of the Lakehouse, where data flows from raw to refined stages,
improving quality at each step.