Thesius Code

Posted on Mar 23 • Originally published at datanest-stores.pages.dev

Databricks DE Associate Prep: Databricks Data Engineer Associate Exam Guide

#cloud #certification #aws #devops

Databricks Data Engineer Associate Exam Guide

Accelerate your data engineering career with this comprehensive study guide for the Databricks Certified Data Engineer Associate exam. This guide covers Apache Spark fundamentals, Delta Lake architecture, Delta Live Tables pipelines, Unity Catalog governance, and workflow orchestration — the five pillars of the Databricks Lakehouse Platform. Each section includes PySpark code examples, SQL patterns, and practice questions designed to match the exam's emphasis on practical, production-ready data engineering. Whether you are coming from a traditional ETL background or already working with Spark, this guide provides the focused preparation you need.

Key Features

Lakehouse architecture concepts with medallion pattern (bronze/silver/gold) implementation
Apache Spark deep dives covering DataFrames, transformations, actions, and query optimization
Delta Lake features including ACID transactions, time travel, MERGE operations, and schema evolution
Delta Live Tables pipeline development with expectations, quality constraints, and monitoring
Unity Catalog governance covering namespaces, grants, data lineage, and access control
Workflow orchestration with Databricks Jobs, task dependencies, and scheduling patterns
Production data engineering best practices for testing, monitoring, and incremental processing

Study Plan

Week 1-2: Databricks Lakehouse Platform (24% of exam)

Lakehouse architecture: combining data lake and data warehouse benefits
Medallion architecture: bronze (raw), silver (cleansed), gold (aggregated)
Delta Lake fundamentals: ACID transactions, unified batch and streaming
Databricks workspace navigation: notebooks, repos, clusters, SQL warehouses

Week 3-4: ELT with Spark SQL and PySpark (29% of exam)

DataFrame operations: select, filter, groupBy, join, window functions
Spark SQL: CTEs, subqueries, higher-order functions
Reading/writing data: CSV, JSON, Parquet, Delta with schema inference
Incremental data processing with Auto Loader and COPY INTO

Week 5-6: Delta Live Tables (16% of exam)

DLT pipeline development: live tables vs. streaming live tables
Data quality with expectations: constraints, quarantine patterns
Pipeline monitoring and event log analysis
Materialized views vs. streaming tables decision framework

Week 7-8: Unity Catalog and Governance (11% of exam)

Three-level namespace: catalog.schema.table
Managing permissions with GRANT/REVOKE statements
Data lineage tracking and impact analysis
External locations and storage credentials

Week 9-10: Workflow Orchestration and Production (20% of exam)

Databricks Jobs: tasks, dependencies, clusters, retry policies
Multi-task workflows with linear and fan-out/fan-in patterns
Alerting and monitoring with job notifications
CI/CD for notebooks using Databricks Repos

Key Topics

Domain	Weight	Focus Areas
Lakehouse Platform	24%	Architecture, Delta Lake, workspace
ELT with Spark	29%	DataFrames, SQL, Auto Loader
Delta Live Tables	16%	Pipelines, expectations, monitoring
Unity Catalog	11%	Governance, permissions, lineage
Workflow Orchestration	20%	Jobs, scheduling, production ops

Practice Questions

Q1: A data engineer needs to ingest new CSV files as they arrive in cloud storage. The schema may change over time with new columns added. Which approach handles this with the least operational overhead?

A1: Use Auto Loader (cloudFiles format) with cloudFiles.schemaLocation set to a checkpoint directory and mergeSchema option enabled. Auto Loader automatically detects new files, infers schema changes, and evolves the target Delta table schema. Example: spark.readStream.format("cloudFiles").option("cloudFiles.format", "csv").option("cloudFiles.schemaLocation", "/checkpoint/schema").load("/data/input/").

Q2: A Delta table contains incorrect records that were loaded 3 hours ago. The engineer needs to restore the table to its state before the bad load. What is the most efficient approach?

A2: Use Delta Lake time travel to restore the table: RESTORE TABLE my_catalog.my_schema.my_table TO VERSION AS OF <version_before_bad_load>. First, find the correct version using DESCRIBE HISTORY my_catalog.my_schema.my_table to identify the version number before the bad load. This is an atomic operation that creates a new version pointing to the old data files.

Q3: In a Delta Live Tables pipeline, a data quality rule requires that the email column must not be null and the amount column must be positive. Records failing either rule should be quarantined, not dropped. How should this be implemented?

A3: Use DLT expectations with the @dlt.expect_all_or_drop decorator for critical rules and route failed records to a quarantine table. However, for quarantine (not drop), use @dlt.expect("valid_email", "email IS NOT NULL") and @dlt.expect("positive_amount", "amount > 0") — then create a separate streaming live table that reads from the event log to capture violated records into a quarantine table for review.

Q4: A Unity Catalog administrator needs to grant a data analyst read-only access to all tables in a specific schema but prevent them from creating new tables. What GRANT statement should be used?

A4: GRANT USE CATALOG ON CATALOG my_catalog TO analyst_group; GRANT USE SCHEMA ON SCHEMA my_catalog.my_schema TO analyst_group; GRANT SELECT ON SCHEMA my_catalog.my_schema TO analyst_group; The USE CATALOG and USE SCHEMA grants allow navigation, while SELECT grants read access to all current and future tables in that schema without CREATE TABLE permissions.

Lab Exercises

Lab 1: Delta Lake Operations

-- Create a Delta table with the medallion pattern
CREATE TABLE IF NOT EXISTS catalog.bronze.raw_events (
  event_id STRING,
  event_type STRING,
  user_id STRING,
  event_data STRING,
  ingestion_time TIMESTAMP DEFAULT current_timestamp()
);

-- MERGE operation for upsert pattern
MERGE INTO catalog.silver.dim_users AS target
USING catalog.bronze.raw_users AS source
ON target.user_id = source.user_id
WHEN MATCHED THEN UPDATE SET *
WHEN NOT MATCHED THEN INSERT *;

-- Time travel query
SELECT * FROM catalog.silver.dim_users VERSION AS OF 5;

-- View table history
DESCRIBE HISTORY catalog.silver.dim_users;

Lab 2: Auto Loader Ingestion

# Stream new JSON files from cloud storage into a Delta table
(spark.readStream
    .format("cloudFiles")
    .option("cloudFiles.format", "json")
    .option("cloudFiles.schemaLocation", "/checkpoints/events/schema")
    .option("cloudFiles.inferColumnTypes", "true")
    .load("/data/raw/events/")
    .writeStream
    .option("checkpointLocation", "/checkpoints/events/data")
    .option("mergeSchema", "true")
    .trigger(availableNow=True)
    .toTable("catalog.bronze.events"))

Lab 3: Delta Live Tables Pipeline

import dlt
from pyspark.sql.functions import col

@dlt.table(comment="Raw ingestion of sales data")
def bronze_sales():
    return (spark.readStream
        .format("cloudFiles")
        .option("cloudFiles.format", "csv")
        .option("header", "true")
        .load("/data/raw/sales/"))

@dlt.table(comment="Cleansed sales with quality checks")
@dlt.expect_or_drop("valid_amount", "amount > 0")
@dlt.expect_or_drop("valid_date", "sale_date IS NOT NULL")
def silver_sales():
    return dlt.read_stream("bronze_sales").select(
        col("sale_id").cast("long"),
        col("amount").cast("double"),
        col("sale_date").cast("date"),
        col("product_id")
    )

Exam Tips

ELT with Spark is the biggest domain at 29% — practice DataFrame transformations and SQL queries extensively
Auto Loader vs. COPY INTO — Auto Loader is for streaming incremental loads; COPY INTO is for batch idempotent loads
Delta Lake features — know MERGE syntax, time travel, OPTIMIZE, ZORDER, VACUUM, and schema evolution
Structured Streaming — understand triggers: availableNow=True for batch-like streaming vs. continuous processing
Unity Catalog hierarchy — catalog > schema > table/view/function; understand GRANT inheritance
The exam is practical — expect code snippets where you identify the correct output or fix a bug

Resources

This is 1 of 11 resources in the Certification Prep Pro toolkit. Get the complete [Databricks DE Associate Prep] with all files, templates, and documentation for $69.

Get the Full Kit →

Or grab the entire Certification Prep Pro bundle (11 products) for $249 — save 30%.

Get the Complete Bundle →

DEV Community