Databricks · DCASD
Databricks Certified Associate Developer for Apache Spark Practice Tests
Validates the ability to use Apache Spark DataFrame API and Spark SQL for data manipulation tasks, covering Spark architecture and execution model, DataFrame transformations and actions, Structured Streaming, Spark Connect, and performance tuning.
Exam Details
Practice Questions
604
≈ 13 practice exams
Duration
90 minutes
Passing Score
70%
Difficulty
AssociateLast Updated
Feb 2026
Databricks Certified Associate Developer For Apache Spark
Prepare for the Databricks Certified Associate Developer for Apache Spark exam with questions focused on Spark DataFrame operations, Spark SQL, data transformations, execution behavior, and common development patterns. This practice set is useful for developers who need to prove they can use Apache Spark correctly inside real data-processing workflows.
Use practice mode while learning the API details, then switch to timed sessions when you are ready to test speed and accuracy. The explanations are designed to reinforce why a Spark operation behaves a certain way, which is often the difference between recognizing an answer and applying it under exam pressure.
Exam Overview
The Databricks Certified Associate Developer for Apache Spark validates a candidate's ability to use the Apache Spark DataFrame API and core Spark concepts to perform essential data manipulation tasks within a Spark session. The exam was significantly updated in April 2025 (replacing the legacy Spark 3.0 version) and now covers the Spark DataFrame API for selecting, renaming, and manipulating columns; filtering, dropping, sorting, and aggregating rows; handling missing data; combining, reading, writing, and partitioning DataFrames with schemas; and working with user-defined functions (UDFs) and Spark SQL functions. Code in the exam is presented exclusively in Python.
Beyond the DataFrame API, the certification assesses foundational knowledge of the Spark architecture and execution model, including execution and deployment modes, the execution hierarchy (jobs, stages, tasks), fault tolerance, garbage collection, lazy evaluation, shuffling, actions, and broadcasting. The updated exam also includes coverage of Structured Streaming fundamentals, Spark Connect, the Pandas API on Apache Spark, and common performance tuning and troubleshooting techniques. This breadth makes it a comprehensive entry-level credential for working with Apache Spark in production data environments.
Who Should Take This Exam
This certification is designed for early-to-mid-career data practitioners who work with Apache Spark in Python on a regular basis. Ideal candidates include data engineers, data analysts, and software developers who build or maintain Spark-based data pipelines and need to demonstrate foundational proficiency. Databricks recommends at least six months of hands-on experience performing the tasks covered in the exam guide before attempting the exam.
The certification is well-suited for professionals transitioning into big data engineering roles, data platform engineers working on Databricks-based lakehouse architectures, and developers at organizations that use Apache Spark at scale. Because Apache Spark is used across industries—from finance and retail to healthcare and technology—this credential is valuable beyond Databricks-specific roles.
Prerequisites
There are no formal prerequisites required to register for this exam. However, Databricks strongly recommends that candidates have at least six months of practical, hands-on experience with Apache Spark before attempting the certification. Candidates should be comfortable writing PySpark code using the DataFrame API and have a working understanding of Spark's execution model.
Recommended preparatory knowledge includes familiarity with Python programming, basic SQL, and core big data concepts such as distributed computing and partitioning. Databricks' official training courses—particularly 'Apache Spark™ Programming with Databricks' and 'Developing Applications with Apache Spark™'—are strongly recommended as preparation. Prior completion of an introductory Databricks or Spark course, combined with hands-on practice in a Databricks workspace, will substantially improve a candidate's readiness.
Exam Format
The exam consists of 45 scored multiple-choice questions and must be completed within 90 minutes, delivered in a proctored format either online or at an authorized testing center. All code snippets and questions are presented in Python. The exam costs $200 USD per attempt, and the certification is valid for two years, after which recertification is required. The passing score is 70%.
As with many proctored certification exams, the exam may include a small number of unscored pilot items used to gather statistical data for future exam development; these items are not identified and do not impact the final score, and additional time is factored in to account for them. Questions are scenario-based and frequently require candidates to evaluate PySpark code snippets, making hands-on coding experience essential for success.
Skills Measured
- 1.Spark DataFrame API – Core Operations (approximately 70% of exam): Selecting, renaming, and manipulating columns; filtering, dropping, sorting, and aggregating rows; handling missing and null data; combining DataFrames via joins and unions; reading and writing DataFrames in various formats (Parquet, CSV, JSON, Delta); managing schemas and partitioning; applying user-defined functions (UDFs); and using Spark SQL functions.
- 2.Spark Architecture and Execution Model: Understanding Spark's execution and deployment modes (client vs. cluster, local vs. distributed); the execution hierarchy (applications, jobs, stages, tasks); lazy evaluation and the DAG scheduler; fault tolerance and lineage; garbage collection behavior; shuffling and its performance implications; actions vs. transformations; and broadcasting variables and accumulators.
- 3.Structured Streaming: Fundamentals of Spark Structured Streaming, including reading from and writing to streaming sources and sinks, output modes (append, complete, update), triggers, and watermarking for handling late data.
- 4.Spark Connect: Understanding the Spark Connect architecture and its role in enabling a decoupled client-server model for submitting Spark workloads, including how it differs from the traditional Spark driver model.
- 5.Pandas API on Apache Spark: Using the Pandas API on Spark (formerly Koalas) to write Pandas-compatible code that executes on a distributed Spark cluster, including awareness of its behavioral differences from native Pandas.
- 6.Performance Tuning and Troubleshooting: Common optimization techniques including caching and persistence strategies, partition management, avoiding data skew, understanding the Spark UI for diagnosing bottlenecks, and applying best practices for efficient Spark job execution.
Study Tips
- Work through the official Databricks training course 'Apache Spark™ Programming with Databricks' (available on the Databricks Academy platform), as its curriculum is directly aligned with the exam objectives and includes hands-on labs in a real Databricks environment.
- Practice writing PySpark DataFrame transformations from scratch in a free Databricks Community Edition workspace. Focus heavily on chaining transformations, reading/writing various file formats, handling nulls, and writing UDFs—these topics represent the bulk of exam questions.
- Study the official Databricks exam guide and topic outline published on the certification page to ensure you cover every listed objective, paying special attention to new topics in the 2025 version: Spark Connect, Structured Streaming, and the Pandas API on Spark.
- Read the relevant chapters of 'Spark: The Definitive Guide' by Bill Chambers and Matei Zaharia (O'Reilly) to build a solid understanding of Spark's execution model, lazy evaluation, shuffling, and fault tolerance—conceptual questions on these topics appear alongside code-based questions.
- Take at least one full-length timed practice exam under real conditions (45 questions, 90 minutes) to build familiarity with the question format and identify weak areas. Review every incorrect answer by testing the corresponding code in a Databricks notebook rather than just reading explanations.
- Review the Spark documentation and Databricks documentation pages for the specific functions and classes that appear in the exam—particularly `pyspark.sql.functions`, `DataFrame` methods, streaming API (`readStream`/`writeStream`), and `SparkSession` configuration options.
- For Structured Streaming and Spark Connect, focus on conceptual understanding rather than memorizing syntax—these topics tend to appear as scenario or concept questions rather than code-evaluation questions in the exam.
Career Benefits
Earning the Databricks Certified Associate Developer for Apache Spark credential signals verified, entry-level proficiency in one of the most widely deployed distributed data processing frameworks in the industry. Apache Spark is used at scale by organizations across virtually every sector, meaning this certification is relevant beyond Databricks-specific roles. Common target positions for certified professionals include Data Engineer, Big Data Developer, Analytics Engineer, and Data Platform Engineer. With experience, certified professionals move into senior data engineering, data architecture, and principal engineering roles.
In terms of compensation, mid-level data engineers with Spark proficiency in the United States commonly earn in the range of $130,000–$180,000 annually, with senior roles exceeding $200,000 at major technology firms. Industry surveys suggest that certified professionals can command 10–20% salary premiums over non-certified peers with equivalent experience. The certification is valid for two years and pairs well with other Databricks credentials—such as the Databricks Certified Data Engineer Associate or the Databricks Certified Machine Learning Associate—for professionals building a broader Databricks certification portfolio.
Sample Questions
5 sample questions with answers and explanations. The full bank has 604 questions, enough for 13 full-length practice exams.
Preview — answers shown1. A data scientist uses the following code: df.select('scores').show(). The scores column has type ArrayType(IntegerType()). Some rows contain null arrays and some contain arrays with null elements like [1, null, 3]. Which function correctly computes the sum of non-null elements in each array? (Select one!)
Adf.select(expr('aggregate(scores, 0, (acc, x) -> acc + coalesce(x, 0))'))
Bdf.select(aggregate('scores', 0, lambda acc, x: acc + x))
Cdf.select(array_sum('scores'))
Ddf.select(sum('scores'))
Explanation
The aggregate higher-order function with a lambda that uses coalesce correctly handles null elements by replacing them with 0 before adding to the accumulator. This approach sums only the non-null values. Using aggregate without coalesce would propagate nulls through the calculation, resulting in null if any element is null. There is no built-in array_sum function in Spark SQL. The sum aggregate function works on DataFrame rows, not on array elements within a single row.
2. A Spark application processes a large dataset using df.groupBy('category').agg(sum('amount').alias('total')). The explain plan shows that Adaptive Query Execution dynamically coalesced 200 shuffle partitions into 8 partitions. What triggered this optimization? (Select one!)
AThe groupBy operation resulted in only 8 distinct category values requiring 8 output partitions
BAQE detected that post-shuffle data size was small enough to process efficiently in fewer partitions
CThe sum aggregation produces minimal output data triggering automatic partition consolidation
DAQE automatically reduces partition count to match the number of available executor cores
Explanation
Adaptive Query Execution dynamically coalesces shuffle partitions when runtime statistics show that the actual post-shuffle data size is much smaller than expected. AQE monitors partition sizes after shuffle operations complete and combines small partitions to reduce scheduling overhead and improve performance. This optimization occurs when many partitions contain small amounts of data. The number of distinct grouping keys does not directly determine partition coalescing; even with 8 categories, data could be spread across 200 partitions initially. AQE does not simply match partition count to executor cores; it bases decisions on actual data statistics. The aggregation output size does not automatically trigger coalescing; AQE evaluates partition size distribution across the shuffle.
3. A Spark application running on YARN with 10 executors experiences frequent out-of-memory errors during shuffle operations. The executor memory is set to 8 GB. Which configuration change is most likely to resolve the issue? (Select one!)
AEnable spark.memory.offHeap.enabled to move shuffle data off the JVM heap
BDecrease spark.sql.shuffle.partitions to reduce the number of shuffle tasks
CIncrease spark.executor.memoryOverhead to allocate more off-heap memory for shuffle operations
DIncrease spark.memory.storageFraction to allocate more memory for execution
Explanation
Shuffle operations use off-heap memory allocated by spark.executor.memoryOverhead for network buffers, temporary files, and native libraries. The default overhead is max(384 MB, 10 percent of executor memory), which may be insufficient for shuffle-heavy workloads. Increasing memoryOverhead provides more off-heap memory for shuffle operations without affecting JVM heap. Increasing storageFraction would allocate more of the unified memory region to storage (caching) at the expense of execution memory, which is counterproductive. Decreasing shuffle partitions might help if partitions are too small, but the issue is memory availability, not partition count. While enabling off-heap memory storage is possible, it requires explicit configuration of spark.memory.offHeap.size and does not automatically resolve shuffle OOM issues.
4. A data pipeline writes output with df.write.bucketBy(50, 'user_id').sortBy('timestamp').saveAsTable('user_events'). What are the primary benefits of this bucketing strategy for subsequent queries that filter by user_id? (Select one!)
ABucketing automatically creates indexes on user_id for faster lookups
BBucketing ensures data for each user_id is in a single file, avoiding shuffle during joins and aggregations on user_id
CBucketing compresses data more efficiently than non-bucketed tables
DBucketing eliminates the need for partition pruning during query execution
Explanation
Bucketing pre-partitions data by hashing the bucket column values into a fixed number of buckets, ensuring that all rows with the same user_id are in the same bucket file. This eliminates shuffle operations during joins and aggregations on user_id because matching keys are already co-located. Partition pruning applies to partitionBy, not bucketBy. Bucketing does not create indexes; Spark does not support traditional database indexes. Bucketing does not affect compression ratios, which are determined by the file format and compression codec settings.
5. A data engineer needs to write a DataFrame to JDBC with parallel writes to avoid overwhelming the target database. The id column ranges from 1 to 10 million. Which configuration enables parallel writes with 10 connections? (Select one!)
ASet numPartitions to 10 without additional parameters
BCall repartition(10) on the DataFrame before writing
CSet partitionColumn to id, numPartitions to 10, lowerBound to 1, and upperBound to 10000000
DSet partitionColumn to id and numPartitions to 10 only
Explanation
JDBC parallel writes require all four parameters: partitionColumn (the column to partition on), numPartitions (number of parallel connections), lowerBound and upperBound (range boundaries for partition calculation). Spark divides the range evenly across partitions for parallel writing. Setting only numPartitions does not enable parallel JDBC writes because Spark needs the partition column and bounds to distribute writes. partitionColumn alone is insufficient without bounds. Calling repartition affects the DataFrame's internal partitioning but does not configure JDBC-specific parallel write behavior.
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