Databricks Certified Data Engineer Associate Complete Study Guide 2025
Master the Associate Level Exam with Our Comprehensive Study Plan
Databricks Certified Data Engineer Associate Certification Syllabus
Exam Overview
- Certification Level: Associate
- Format: Multiple-choice questions
- Duration: 90 minutes
- Number of Questions: 45 scored questions
- Passing Score: ~80%
- Recommended Experience: 6+ months hands-on data engineering on Databricks
- Delivery: Online proctored
- Validity: 2 years
- Cost: $200 USD
Important Note
The Databricks Certified Data Engineer Associate exam assesses your ability to use the Databricks Data Intelligence Platform to complete introductory data engineering tasks. This includes understanding the platform architecture, performing ETL tasks using Spark SQL and PySpark, and deploying basic data pipelines :cite[1]. The exam was updated in July 2025 with a revised syllabus and increased difficulty :cite[5].
Exam Domains and Weightings
The Databricks Certified Data Engineer Associate exam covers five main domains with the following weightings :cite[1]:
- Databricks Intelligence Platform (10%) - Platform architecture, workspace, tools, and capabilities
- Development and Ingestion (30%) - ETL tasks using Spark SQL and PySpark, data extraction, complex data handling
- Data Processing & Transformations (31%) - Data processing, transformations, and data engineering patterns
- Productionizing Data Pipelines (18%) - Workflow configuration, job scheduling, task orchestration
- Data Governance & Quality (11%) - Unity Catalog, data security, access control, data quality
Key Topics Covered
Databricks Intelligence Platform:
- Lakehouse Platform architecture and components
- Workspace navigation and management
- Data Science and Engineering workspace features
- Cluster configuration and management
- Notebook development and collaboration
Development & Ingestion (Spark SQL & Python):
- ETL pipeline development with Spark SQL and PySpark
- Relational entities (databases, tables, views)
- Data extraction and complex data handling
- User Defined Functions (UDFs)
- Data ingestion methods and patterns
Data Processing & Transformations:
- Structured Streaming concepts and implementation
- Auto Loader for incremental data processing
- Multi-hop architecture (bronze-silver-gold)
- Delta Live Tables (DLT) for pipeline orchestration
- Data transformation and cleansing techniques
Production Pipelines & Orchestration:
- Workflows configuration and management
- Job scheduling and task orchestration
- Dashboard creation and alert configuration
- Pipeline monitoring and maintenance
- Error handling and retry mechanisms
Data Governance & Quality:
- Unity Catalog for centralized governance
- Entity permissions and access control
- Data security best practices
- Data quality checks and validation
- Metadata management and data lineage
Primary Study Resources
Free Practice Test
Databricks Certified Data Engineer Associate - Practice Test
Free practice questions covering all exam domains
Take Free Practice TestOfficial Databricks Resources:
Recommended Training & Practice:
14-Day Intensive Study Plan
Follow this accelerated timeline to prepare for your Databricks Data Engineer Associate certification in two weeks:
Study Progress Tracker
Progress: 0% Complete
Day 1: Platform Fundamentals (4 hours)
Objectives:
- Understand Databricks Intelligence Platform architecture
- Learn workspace navigation and management
- Review exam structure and domains
- Set up Databricks workspace environment
Resources:
- Databricks Platform Documentation
- Official Exam Guide
Practice Examples:
-- Unity Catalog basic operations
CREATE CATALOG IF NOT EXISTS learning;
USE CATALOG learning;
CREATE SCHEMA IF NOT EXISTS bronze_data;
USE SCHEMA bronze_data;
-- Create managed table in Unity Catalog
CREATE TABLE sales_raw (
id INT,
transaction_date TIMESTAMP,
amount DECIMAL(10,2),
customer_id INT,
region STRING
) USING DELTA;
-- Basic permissions
GRANT USAGE ON CATALOG learning TO `engineers@company.com`;
Day 2: Spark SQL Fundamentals (5 hours)
Objectives:
- Master Spark SQL syntax and functions
- Learn table creation and management
- Practice data extraction techniques
- Understand basic query optimization
Resources:
- Spark SQL Documentation
- Databricks SQL Guide
Practice Examples:
-- Create tables and views
CREATE TABLE silver_data.customers (
customer_id INT,
customer_name STRING,
email STRING,
region STRING,
created_date TIMESTAMP
) USING DELTA;
-- Data extraction and transformation
CREATE OR REPLACE VIEW active_customers AS
SELECT
customer_id,
customer_name,
email,
region
FROM silver_data.customers
WHERE created_date >= current_date() - INTERVAL 30 DAYS;
-- Aggregate queries
SELECT
region,
COUNT(*) as customer_count,
AVG(amount) as avg_transaction
FROM silver_data.sales
GROUP BY region;
Day 3: PySpark Basics (5 hours)
Objectives:
- Learn PySpark DataFrame API
- Practice data transformations
- Understand UDF creation
- Master data type handling
Resources:
- PySpark Documentation
- Databricks Notebook Examples
Practice Examples:
# PySpark DataFrame operations
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import *
# Create Spark session
spark = SparkSession.builder.appName("DataProcessing").getOrCreate()
# Read and transform data
df = spark.read.table("bronze_data.sales_raw")
# Basic transformations
processed_df = (df
.filter(col("amount") > 0)
.withColumn("transaction_year", year("transaction_date"))
.withColumn("transaction_month", month("transaction_date"))
.groupBy("region", "transaction_year", "transaction_month")
.agg(
sum("amount").alias("total_sales"),
count("*").alias("transaction_count")
)
)
# UDF example
@udf(returnType=StringType())
def region_category(region):
if region in ['north', 'south']:
return "primary"
else:
return "secondary"
df_with_category = df.withColumn("region_type", region_category(col("region")))
Day 4: Data Ingestion Patterns (4 hours)
Objectives:
- Learn batch ingestion methods
- Understand Auto Loader basics
- Practice COPY INTO command
- Master incremental ingestion
Resources:
- Auto Loader Documentation
- Data Ingestion Guide
Practice Examples:
-- COPY INTO for idempotent batch loads
COPY INTO bronze_data.sales_data
FROM '/mnt/raw/sales/'
FILEFORMAT = PARQUET
PATTERN = '*.parquet'
COPY_OPTIONS ('mergeSchema' = 'true');
-- Auto Loader for streaming ingestion
CREATE OR REFRESH STREAMING TABLE bronze_data.sales_stream
AS SELECT * FROM cloud_files(
"/mnt/raw/sales/streaming/",
"json",
map("cloudFiles.inferColumnTypes", "true")
);
-- Check loaded data
SELECT COUNT(*) as record_count FROM bronze_data.sales_data;
DESCRIBE HISTORY bronze_data.sales_data;
Day 5: Delta Lake Essentials (5 hours)
Objectives:
- Understand Delta Lake ACID properties
- Learn basic Delta operations
- Practice time travel
- Master table maintenance
Resources:
- Delta Lake Documentation
- Delta Table Guide
Practice Examples:
-- Delta Lake basic operations
-- Create Delta table
CREATE TABLE silver_data.sales_cleansed
USING DELTA
AS SELECT * FROM bronze_data.sales_raw WHERE amount IS NOT NULL;
-- Time travel queries
SELECT * FROM silver_data.sales_cleansed TIMESTAMP AS OF '2025-10-01';
SELECT * FROM silver_data.sales_cleansed VERSION AS OF 5;
-- Table maintenance
OPTIMIZE silver_data.sales_cleansed;
-- Check table details
DESCRIBE DETAIL silver_data.sales_cleansed;
DESCRIBE HISTORY silver_data.sales_cleansed;
-- Basic MERGE operation
MERGE INTO silver_data.customers target
USING bronze_data.customers_updates source
ON target.customer_id = source.customer_id
WHEN MATCHED THEN UPDATE SET *
WHEN NOT MATCHED THEN INSERT *;
Day 6: Structured Streaming (5 hours)
Objectives:
- Learn streaming concepts
- Practice streaming DataFrame operations
- Understand watermarking
- Master checkpointing
Resources:
- Structured Streaming Guide
- Streaming Examples
Practice Examples:
# Structured Streaming basics
from pyspark.sql.streaming import DataStreamReader
# Read from Delta table as stream
streaming_df = (spark.readStream
.format("delta")
.table("bronze_data.sales_stream")
)
# Basic streaming operations with watermark
processed_stream = (streaming_df
.withWatermark("transaction_date", "10 minutes")
.groupBy(
window("transaction_date", "5 minutes"),
"region"
)
.agg(sum("amount").alias("total_sales"))
)
# Write streaming results
(processed_stream.writeStream
.format("delta")
.outputMode("update")
.option("checkpointLocation", "/mnt/checkpoints/sales_agg")
.table("silver_data.sales_aggregates")
)
# Monitor streams
SHOW STREAMS;
DESCRIBE STREAM silver_data.sales_aggregates;
Day 7: Multi-hop Architecture (4 hours)
Objectives:
- Understand bronze-silver-gold architecture
- Practice data quality checks
- Learn incremental processing
- Master data validation
Resources:
- Medallion Architecture Guide
- Data Quality Documentation
Practice Examples:
-- Bronze layer (raw ingestion)
CREATE TABLE bronze_data.sales_raw
USING DELTA
AS SELECT * FROM read_files('/mnt/raw/sales/*.json');
-- Silver layer (cleansed data)
CREATE TABLE silver_data.sales_cleansed
USING DELTA
AS SELECT
id,
customer_id,
product_id,
CAST(amount AS DECIMAL(10,2)) as amount,
CAST(transaction_date AS TIMESTAMP) as transaction_date,
region,
CURRENT_TIMESTAMP() as processed_at
FROM bronze_data.sales_raw
WHERE amount IS NOT NULL
AND transaction_date IS NOT NULL;
-- Gold layer (business aggregates)
CREATE TABLE gold_data.sales_aggregates
USING DELTA
AS SELECT
region,
DATE_TRUNC('month', transaction_date) as year_month,
COUNT(*) as transaction_count,
SUM(amount) as total_sales,
AVG(amount) as avg_sale_amount
FROM silver_data.sales_cleansed
GROUP BY region, DATE_TRUNC('month', transaction_date);
-- Data quality checks
SELECT
COUNT(*) as total_records,
COUNT(CASE WHEN amount IS NULL THEN 1 END) as null_amounts,
COUNT(CASE WHEN transaction_date IS NULL THEN 1 END) as null_dates
FROM bronze_data.sales_raw;
Day 8: Delta Live Tables (DLT) (5 hours)
Objectives:
- Learn DLT pipeline development
- Practice expectations and data quality
- Understand incremental processing
- Master pipeline deployment
Resources:
- DLT Documentation
- DLT Examples
Practice Examples:
# Delta Live Tables pipeline
import dlt
from pyspark.sql.functions import *
@dlt.table(
comment="Raw sales data from source system"
)
def sales_bronze():
return (
spark.readStream
.format("cloudFiles")
.option("cloudFiles.format", "json")
.option("cloudFiles.schemaLocation", "/tmp/schema/sales_bronze")
.load("/mnt/raw/sales/")
)
@dlt.table(
comment="Cleansed and validated sales data"
)
@dlt.expect("valid_amount", "amount > 0")
@dlt.expect_or_drop("valid_date", "transaction_date IS NOT NULL")
def sales_silver():
return (
dlt.read_stream("sales_bronze")
.select(
col("id").cast("integer"),
col("customer_id").cast("integer"),
col("amount").cast("decimal(10,2)"),
col("transaction_date").cast("timestamp"),
col("region")
)
)
@dlt.table(
comment="Aggregated sales data"
)
def sales_gold():
return (
dlt.read("sales_silver")
.groupBy("region")
.agg(
sum("amount").alias("total_sales"),
avg("amount").alias("avg_sale_amount")
)
)
Day 9: Workflows & Job Orchestration (4 hours)
Objectives:
- Learn Workflows configuration
- Practice job scheduling
- Understand task dependencies
- Master job monitoring
Resources:
- Workflows Documentation
- Job Scheduling Guide
Practice Examples:
# Workflow configuration example
from databricks.sdk import WorkspaceClient
from databricks.sdk.service import jobs
w = WorkspaceClient()
# Create a simple workflow
pipeline_job = w.jobs.create(
name="Sales Data Pipeline",
tasks=[
jobs.Task(
task_key="ingest_bronze",
description="Ingest raw sales data",
notebook_task=jobs.NotebookTask(
notebook_path="/Workspace/Users/engineer/bronze_ingestion"
)
),
jobs.Task(
task_key="transform_silver",
description="Transform to silver layer",
notebook_task=jobs.NotebookTask(
notebook_path="/Workspace/Users/engineer/silver_transformation"
),
depends_on=[jobs.TaskDependency(task_key="ingest_bronze")]
)
],
schedule=jobs.CronSchedule(
quartz_cron_expression="0 0 6 * * ?", # Daily at 6 AM
timezone_id="America/New_York"
)
)
-- Monitor job runs
SELECT * FROM system.operational_data.jobs WHERE name = 'Sales Data Pipeline';
DESCRIBE HISTORY silver_data.sales_cleansed;
Day 10: Unity Catalog & Security (5 hours)
Objectives:
- Master Unity Catalog security
- Learn permission management
- Practice data governance
- Understand access control
Resources:
- Unity Catalog Documentation
- Security Best Practices
Practice Examples:
-- Unity Catalog security implementation
-- Create roles
CREATE ROLE data_engineer;
CREATE ROLE data_analyst;
-- Grant permissions
GRANT USE CATALOG ON CATALOG learning TO data_engineer;
GRANT USE SCHEMA ON SCHEMA silver_data TO data_engineer;
GRANT SELECT ON TABLE silver_data.sales_cleansed TO data_analyst;
-- Row level security with views
CREATE VIEW silver_data.regional_sales AS
SELECT *
FROM silver_data.sales_cleansed
WHERE region IN (
SELECT region
FROM user_regions
WHERE user_email = current_user()
);
-- Check permissions
SHOW GRANTS ON TABLE silver_data.sales_cleansed;
SHOW CURRENT ROLES;
-- Data governance
CREATE VOLUME learning.bronze_data.raw_files;
CREATE EXTERNAL LOCATION learning.raw_data
URL 's3://company-raw-data/'
WITH (CREDENTIAL learning.aws_cred);
Day 11: Performance Optimization (4 hours)
Objectives:
- Learn basic performance tuning
- Practice ZORDER optimization
- Understand partitioning strategies
- Master query optimization
Resources:
- Performance Tuning Guide
- Query Optimization Documentation
Practice Examples:
-- Performance optimization techniques
-- Create optimized table
CREATE TABLE gold_data.optimized_sales
USING DELTA
PARTITIONED BY (sale_year)
AS SELECT
*,
YEAR(transaction_date) as sale_year
FROM silver_data.sales_cleansed;
-- Optimize with ZORDER
OPTIMIZE gold_data.optimized_sales
ZORDER BY (customer_id, region);
-- Check optimization results
DESCRIBE DETAIL gold_data.optimized_sales;
SELECT * FROM delta_file_snapshot('gold_data.optimized_sales');
-- Analyze query performance
EXPLAIN SELECT * FROM gold_data.optimized_sales WHERE customer_id = 123;
# PySpark performance configurations
# Enable basic optimizations
spark.conf.set("spark.sql.adaptive.enabled", "true")
spark.conf.set("spark.sql.adaptive.coalescePartitions.enabled", "true")
# Configure shuffle partitions
spark.conf.set("spark.sql.shuffle.partitions", "200")
Day 12: Data Quality & Monitoring (4 hours)
Objectives:
- Implement data quality checks
- Learn monitoring techniques
- Practice alert configuration
- Understand observability
Resources:
- Data Quality Documentation
- Monitoring Guide
Practice Examples:
-- Data quality monitoring
-- Check data freshness
SELECT
MAX(transaction_date) as latest_transaction,
DATEDIFF(CURRENT_TIMESTAMP(), MAX(transaction_date)) as days_behind
FROM silver_data.sales_cleansed;
-- Data quality metrics
SELECT
COUNT(*) as total_records,
COUNT(DISTINCT customer_id) as unique_customers,
SUM(CASE WHEN amount IS NULL THEN 1 ELSE 0 END) as null_amounts,
SUM(CASE WHEN amount < 0 THEN 1 ELSE 0 END) as negative_amounts
FROM silver_data.sales_cleansed;
-- Monitor table history
DESCRIBE HISTORY silver_data.sales_cleansed;
-- Check streaming progress
SELECT * FROM stream_reads('silver_data.sales_stream');
-- Set up basic alerts (pseudo-code)
-- Monitor for data freshness issues
-- Alert if no new data in 24 hours
Day 13: Comprehensive Review (6 hours)
Objectives:
- Review all exam domains
- Practice key concepts
- Identify weak areas
- Reinforce learning
Resources:
- Official Exam Guide
- All previous exercises
- Practice tests
Key Concepts Quick Review:
-- Essential patterns for exam
-- 1. Basic table creation
CREATE TABLE catalog.schema.table_name (
id INT,
name STRING,
created_date TIMESTAMP
) USING DELTA;
-- 2. Data ingestion with COPY INTO
COPY INTO bronze_data.sales
FROM '/mnt/raw/data/'
FILEFORMAT = PARQUET;
-- 3. Stream ingestion with Auto Loader
CREATE STREAMING TABLE bronze_data.streaming_data
AS SELECT * FROM cloud_files("/mnt/raw/stream/", "json");
-- 4. Data transformation
CREATE TABLE silver_data.cleansed_data AS
SELECT
id,
customer_id,
CAST(amount AS DECIMAL(10,2)) as amount,
CAST(transaction_date AS TIMESTAMP) as transaction_date
FROM bronze_data.raw_data
WHERE amount > 0 AND transaction_date IS NOT NULL;
-- 5. Basic aggregation
CREATE TABLE gold_data.aggregates AS
SELECT
region,
COUNT(*) as count,
SUM(amount) as total
FROM silver_data.cleansed_data
GROUP BY region;
-- 6. Delta Lake operations
OPTIMIZE silver_data.cleansed_data ZORDER BY (customer_id);
DESCRIBE HISTORY silver_data.cleansed_data;
VACUUM silver_data.cleansed_data RETAIN 168 HOURS;
Day 14: Practice Exams & Final Prep (6 hours)
Objectives:
- Complete practice exams
- Review weak areas
- Final documentation review
- Schedule certification exam
Resources:
- Free Practice Test (provided link)
- Official Exam Guide
- Domain quick references
Final Checklist:
- ✓ Completed all 5 exam domains
- ✓ Scored 80%+ on practice tests
- ✓ Hands-on practice with key features
- ✓ Reviewed weak areas thoroughly
- ✓ Scheduled certification exam
Exam Day Strategy:
-- Key SQL patterns to remember
-- 1. Unity Catalog object creation
CREATE CATALOG IF NOT EXISTS my_catalog;
USE CATALOG my_catalog;
CREATE SCHEMA IF NOT EXISTS my_schema;
-- 2. Managed vs external tables
CREATE TABLE managed_table (id INT, name STRING); -- Managed
CREATE TABLE external_table (id INT, name STRING)
LOCATION 's3://bucket/path/'; -- External
-- 3. Streaming table creation
CREATE OR REFRESH STREAMING TABLE streaming_table
AS SELECT * FROM cloud_files("/path/", "format");
-- 4. Data quality in DLT
@dlt.expect("valid_id", "id IS NOT NULL")
@dlt.expect_or_drop("positive_amount", "amount > 0")
-- 5. Basic permissions
GRANT USE CATALOG ON CATALOG my_catalog TO `user@company.com`;
GRANT SELECT ON TABLE my_schema.my_table TO data_analysts;
-- 6. Table maintenance
OPTIMIZE my_table ZORDER BY (id);
VACUUM my_table;
DESCRIBE HISTORY my_table;
Success Tips & Best Practices
Study Strategies:
- Hands-on Practice: Use Databricks workspace extensively - the exam tests practical skills
- Focus on Fundamentals: Associate exam tests basic data engineering tasks, not advanced patterns
- Master Both Languages: Be comfortable with both Spark SQL and PySpark syntax
- Understand Platform Features: Know when to use which Databricks service
- Practice Time Management: 90 minutes for 45 questions requires good pacing
Exam Day Preparation:
- Review Unity Catalog permissions and security patterns
- Practice reading basic Spark query plans
- Understand the difference between batch and streaming processing
- Be familiar with basic data ingestion patterns
- Get adequate rest - the exam requires focused attention for 90 minutes
During the Exam:
- Read questions carefully - look for keywords about specific services or features
- Eliminate obviously wrong answers first in multiple-choice questions
- Manage your time - flag difficult questions and return to them
- Trust your hands-on experience - the exam tests practical knowledge
- Focus on the Associate level scope - don't overthink with Professional level solutions
Associate Level Focus
The Associate exam assesses your ability to complete introductory data engineering tasks using the Databricks Data Intelligence Platform. Focus on understanding how to use platform features correctly for basic data engineering scenarios rather than designing complex architectures :cite[1]. The July 2025 update increased the difficulty and passing score, so thorough preparation is essential :cite[5].
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