Exam Prep – Databricks Certified Associate Developer for Apache Spark
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Learning Track
Exam Prep
Delivery methods
On-Site, Virtual
Duration
1 day
This course is a practice-question–based exam preparation program for the Databricks Certified Associate Developer for Apache Spark certification. Each module is anchored by realistic exam-style questions aligned to the official domains. Instructors will review every answer option—explaining why each choice is correct or incorrect—and weave in targeted insights into Apache Spark architecture, DataFrame API, SQL, troubleshooting, structured streaming, Spark Connect, and Pandas API on Spark. Graduates will leave equipped with sharp exam-ready skills and expert-level command of Spark fundamentals on Databricks.
Learning Objectives
By the end of this course, learners will be able to:
- Instantly recognize and resolve common question pitfalls through option analysis.
- Confidently manipulate DataFrames and build DataFrame-based workflows using Python.
- Explain the core Spark architecture, execution modes, and performance mechanisms.
- Debug and optimize Spark jobs using best practices and tuning techniques.
- Develop and deploy structured streaming applications and use Spark Connect.
- Use the Pandas API on Spark for familiar workflows while avoiding common traps.
Audience
- Candidates preparing for the Databricks Certified Associate Developer for Apache Spark exam
- Data engineers, developers, and analytics practitioners with at least 6 months of hands-on Spark experience on Databricks
- Learners who favor scenario-based, question-driven learning over lecture-heavy formats
Prerequisites
- Familiarity with Python programming and basic DataFrame operations
- Hands-on experience with Apache Spark DataFrame API in Databricks environments
- General understanding of Spark processing tasks, query execution, and streaming
Course outline
- Certification format, domains, scoring, timing
- Strategies for analyzing multiple-choice questions
- Question types and common distractors
- Pacing strategies and flagging questions for review
- Execution and deployment modes
- Spark execution hierarchy: jobs, stages, tasks
- Fault tolerance, garbage collection, lazy evaluation
- Shuffling, actions, and broadcasting
- Writing SQL queries in Spark
- Functions, joins, aggregations, and grouping
- Query execution and optimization basics
- Selecting, renaming, and manipulating columns
- Filtering, dropping, sorting, and aggregating rows
- Handling missing data
- Combining, reading, writing, and partitioning DataFrames
- Using UDFs and built-in SQL functions
- Identifying and resolving common job failures
- Performance bottlenecks and tuning opportunities
- Best practices for memory, partitions, and caching
- Fundamentals of structured streaming
- Creating, managing, and monitoring streaming workflows
- Handling checkpoints, sinks, and failure recovery
- Overview of Spark Connect architecture
- Deploying Spark applications with Spark Connect
- Common use cases and limitations
- Overview of the Pandas API on Spark
- Syntax differences and compatibility with pandas
- Partitioning and performance considerations
- Best practices for avoiding pitfalls
