DATABRICKS-CERTIFIED-ASSOCIATE-DEVELOPER-FOR-APACHE-SPARK Real Exam Questions
Databricks Certified Associate Developer for Apache Spark. Everything you need to prepare, practice, and pass.
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Certification Overview
This exam is intensely focused on Spark DataFrame operations: transformations, column manipulation, and data manipulation patterns. You'll be tested on understanding how to structure data transformations using the DataFrame API, optimize query execution through lazy evaluation, and implement common ETL patterns. PySpark syntax and methods dominate the question distribution.
What This Certification Proves
This certification validates your ability to develop distributed data processing applications using Apache Spark's DataFrame API and PySpark. It demonstrates practical competency in building data transformations and pipelines, making you a credible candidate for data engineering and analytics roles working with big data platforms like Databricks.
Who Should Take This Exam
Python developers with basic SQL/data querying knowledge transitioning into data engineering; junior data engineers looking to formalize Spark skills; data scientists wanting to strengthen their distributed computing fundamentals. You should have 1-2 years of data work experience or equivalent self-study.
Topic Breakdown
115 domains covering 171 questions
| Domain | Questions | Weight |
|---|---|---|
| Working With Spark Dataframes | 8 | 5% |
| Spark Dataframe Operations | 5 | 3% |
| Spark Architecture And Execution | 5 | 3% |
| Spark Dataframe Transformations | 5 | 3% |
| Perform Dataframe Transformations | 5 | 3% |
| Transforming Data With Spark Dataframes | 5 | 3% |
| Optimizing Spark Applications | 4 | 2% |
| Performing Data Transformations With Spark Dataframes | 4 | 2% |
| Performing Dataframe Transformations | 4 | 2% |
| Working With Dataframes | 4 | 2% |
| Dataframe Transformations | 4 | 2% |
| Spark Sql And Dataframes | 3 | 2% |
| Performance Tuning And Optimization | 3 | 2% |
| Working With Spark Dataframes And Transformations | 2 | 1% |
| Spark Sql And Dataframe Operations | 2 | 1% |
| Spark Core Concepts And Architecture | 2 | 1% |
| Spark Dataframe Api Operations | 2 | 1% |
| Working With Spark Sql And Dataframes | 2 | 1% |
| Data Transformation And Manipulation | 2 | 1% |
| Implementing User-Defined Functions (Udfs) In Pyspark | 2 | 1% |
| Spark Execution Model | 2 | 1% |
| Transforming Dataframes | 2 | 1% |
| Spark Dataframe Api | 2 | 1% |
| Dataframe Transformations And Operations | 1 | 1% |
| Dataframe Write Operations And Data Persistence | 1 | 1% |
| Handling Missing Data In Spark Dataframes | 1 | 1% |
| Implementing User-Defined Functions (Udfs) In Spark Sql | 1 | 1% |
| Loading Data Into Dataframes | 1 | 1% |
| Manipulate And Clean Data Using Spark Dataframes | 1 | 1% |
| Manipulate Dataframe Columns And Apply Transformations | 1 | 1% |
| Manipulate Dataframes Using The Spark Api | 1 | 1% |
| Manipulating Data With Spark Dataframes | 1 | 1% |
| Manipulating Dataframes In Apache Spark | 1 | 1% |
| Optimizing And Troubleshooting Spark Applications | 1 | 1% |
| Optimizing Spark Application Performance | 1 | 1% |
| Perform Data Aggregation Operations On Spark Dataframes | 1 | 1% |
| Perform Data Transformations Using Spark Dataframe Api | 1 | 1% |
| Perform Dataframe Api Operations (Transformations) | 1 | 1% |
| Perform Dataframe Column Transformations | 1 | 1% |
| Performance Tuning | 1 | 1% |
| Performing Aggregations On Dataframes | 1 | 1% |
| Performing Basic Dataframe Operations | 1 | 1% |
| Performing Basic Dataframe Transformations | 1 | 1% |
| Performing Data Aggregations With Spark Dataframes | 1 | 1% |
| Performing Data Aggregations With Spark Dataframes And Sql | 1 | 1% |
| Performing Data Analysis With Spark Dataframes | 1 | 1% |
| Performing Data Input/Output (I/O) With Spark Dataframes | 1 | 1% |
| Performing Data Output Operations | 1 | 1% |
| Performing Data Transformations Using Spark Dataframes | 1 | 1% |
| Performing Join Operations On Spark Dataframes | 1 | 1% |
| Performing Spark Dataframe Actions | 1 | 1% |
| Pyspark Dataframe Operations | 1 | 1% |
| Reading And Writing Data With Spark Dataframes | 1 | 1% |
| Spark Application Architecture And Execution | 1 | 1% |
| Spark Application Execution And Optimization | 1 | 1% |
| Spark Architecture | 1 | 1% |
| Spark Architecture And Components | 1 | 1% |
| Spark Architecture And Core Concepts | 1 | 1% |
| Spark Architecture And Deployment | 1 | 1% |
| Spark Architecture And Execution Modes | 1 | 1% |
| Spark Architecture And Fault Tolerance Mechanisms | 1 | 1% |
| Spark Cluster Architecture And Execution Model | 1 | 1% |
| Spark Cluster Management And Performance | 1 | 1% |
| Spark Configuration Management And Performance Tuning | 1 | 1% |
| Spark Core Architecture And Execution Model | 1 | 1% |
| Spark Core Concepts | 1 | 1% |
| Spark Data Management And Optimization | 1 | 1% |
| Spark Data Persistence | 1 | 1% |
| Spark Dataframe And Dataset Operations - Api Usage | 1 | 1% |
| Spark Dataframe Operations And Data Distribution | 1 | 1% |
| Spark Dataframe Operations And Execution | 1 | 1% |
| Spark Dataframe Operations And Optimizations | 1 | 1% |
| Spark Dataframe Operations: Data Selection And Filtering | 1 | 1% |
| Spark Dataframe Partitioning And Shuffling | 1 | 1% |
| Spark Dataframe Persistence | 1 | 1% |
| Spark Dataframe Transformation Types | 1 | 1% |
| Spark Dataframe Transformations And Performance | 1 | 1% |
| Spark Execution And Performance Optimization | 1 | 1% |
| Spark I/O Operations | 1 | 1% |
| Spark Performance Optimization | 1 | 1% |
| Spark Performance Tuning | 1 | 1% |
| Spark Sql Query Optimization | 1 | 1% |
| Spark Structured Apis | 1 | 1% |
| Transforming Data With Dataframes | 1 | 1% |
| Understand Dataframe Join Syntax And Parameter Requirements | 1 | 1% |
| Understand Spark Dataframe Structure And Capabilities | 1 | 1% |
| Understanding Spark Task Execution Model And Scheduling | 1 | 1% |
| User-Defined Functions (Udfs) | 1 | 1% |
| Work With Spark Dataframes | 1 | 1% |
| Working With Apache Spark Dataframes | 1 | 1% |
| Working With Dataframes And Spark Sql | 1 | 1% |
| Accessing And Manipulating Dataframe Data | 1 | 1% |
| Writing Dataframes To Storage | 1 | 1% |
| Core Spark Dataframe Api - Data Retrieval Fundamentals | 1 | 1% |
| Data Ingestion | 1 | 1% |
| Data Ingestion And Output With Apache Spark Dataframes | 1 | 1% |
| Data Ingestion And Transformations | 1 | 1% |
| Data Loading And I/O Operations With Apache Spark | 1 | 1% |
| Data Loading And Persistence | 1 | 1% |
| Data Manipulation With Spark Dataframes | 1 | 1% |
| Data Manipulation With Spark Sql | 1 | 1% |
| Data Manipulation With Spark Sql And Dataframes | 1 | 1% |
| Data Persistence And Caching | 1 | 1% |
| Data Preparation And Transformation | 1 | 1% |
| Data Transformation And Aggregation | 1 | 1% |
| Data Transformation And Manipulation Using Spark Dataframes | 1 | 1% |
| Data Transformation With Spark Dataframes | 1 | 1% |
| Data Transformation With Type-Aware Spark Sql Functions | 1 | 1% |
| Dataframe Data Writing And Serialization | 1 | 1% |
| Dataframe Join Operations And Api Methods | 1 | 1% |
| Dataframe Join Syntax And Parameters In Apache Spark | 1 | 1% |
| Dataframe Manipulation And Querying | 1 | 1% |
| Dataframe Operations And Sql Joins In Apache Spark | 1 | 1% |
| Dataframe Partitioning And Performance Optimization | 1 | 1% |
| Dataframe Transformation Methods - Join Operations | 1 | 1% |
Study Plans
Choose a study plan that matches your schedule and experience level
30 Days
Intensive Sprint
Week 1-2
- Master fundamentals: Working With Spark Dataframes
- Read Databricks official documentation
- Complete 7 questions daily
Week 3
- Deep dive: Spark Dataframe Operations
- Review weak areas from results
- Take 2 full-length exams
Week 4
- Review all flagged questions
- Timed exams to build stamina
- Final revision of key concepts
60 Days
Balanced Approach
Week 1-2
- Survey all exam domains
- Set up study environment
- Begin with foundational topics
Week 3-4
- Focus: Working With Spark Dataframes
- Focus: Spark Dataframe Operations
- 4 questions daily
Week 5-6
- Focus: Spark Architecture And Execution
- Hands-on labs if applicable
- Review explanations for wrong answers
Week 7-8
- Complete all 181 questions
- Identify and eliminate weak areas
- Take 3 full-length timed tests
90 Days
Comprehensive Study
Month 1
- Learn all exam domains at a comfortable pace
- Build strong foundational knowledge
- 3 questions daily
Month 2
- Deep dive into each domain
- Hands-on practice and labs
- Take weekly timed exams
Month 3
- Work through all 181 questions
- Identify and eliminate weak areas
- Take 3 full-length timed exams
DATABRICKS-CERTIFIED-ASSOCIATE-DEVELOPER-FOR-APACHE-SPARK-Specific Tips
- Practice DataFrame transformations hands-on in a Databricks notebook or Spark shell—focus on map/filter/flatMap, groupBy, join, and window operations until they're muscle memory
- Master the distinction between lazy evaluation (transformations) and eager evaluation (actions); this is tested heavily and critical for writing efficient Spark code
- Drill common column operations: select, withColumn, cast, when/otherwise, and aggregate functions—these appear in most exam questions
- Work through optimization patterns: understand broadcast joins, repartitioning, and when to cache() vs persist() to avoid premature optimization traps
- Use the Spark API documentation as your reference while practicing; the exam tests your ability to use methods correctly, not memorize signatures
- Focus on PySpark-specific behavior (vs Scala or SQL) since topics lean heavily toward DataFrame API idioms in Python
- Take all 141 practice questions available and review misses deeply—with 1.9 difficulty, most failures are conceptual, not hard—identify patterns in your gaps
Relevant Career Roles
Sample Questions
Try 5 free questions from the DATABRICKS-CERTIFIED-ASSOCIATE-DEVELOPER-FOR-APACHE-SPARK question bank
Which of the following code blocks returns a DataFrame containing only the rows from DataFrame storesDF where the value in column sqft is less than or equal to 25,000?
Which of the following sets of DataFrame methods will both return a new DataFrame only containing rows that meet a specified logical condition?
The default value of spark.sql.shuffle.partitions is 200. Which of the following describes what that means?
The code block shown below contains an error. The code block intended to create a single- column DataFrame from Scala List years which is made up of integers. Identify the error. Code block: spark.createDataset(years)
The code block shown contains an error. The code block is intended to return a new DataFrame where column sqft from DataFrame storesDF has had its missing values replaced with the value 30,000. Identify the error. A sample of DataFrame storesDF is displayed below: Code block: storesDF.na.fill(30000, col("sqft"))
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