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 engineer creates a global temporary view using df.createOrReplaceGlobalTempView('sales_summary'). Another team member tries to query it using spark.sql('SELECT * FROM sales_summary') and receives an error stating the table does not exist. What is the correct way to query this global temporary view? (Select one!)
Aspark.catalog.setCurrentDatabase('global_temp') then spark.sql('SELECT * FROM sales_summary')
Bspark.sql('SELECT * FROM temp.sales_summary')
Cspark.sql('SELECT * FROM GLOBAL.sales_summary')
Dspark.sql('SELECT * FROM global_temp.sales_summary')
Explanation
Global temporary views must be accessed using the global_temp database prefix: spark.sql('SELECT * FROM global_temp.sales_summary'). This is a critical requirement because global temporary views are stored in the special global_temp database to distinguish them from session-scoped temporary views. Using just the view name without the prefix fails because Spark looks in the current database. The GLOBAL, temp prefixes, and setting current database approaches do not work for global temporary views.
2. A data engineering team needs to write a DataFrame partitioned by year and month columns to optimize query performance. The output should have separate directories for each year-month combination. Which code correctly implements this? (Select one!)
Adf.repartition("year", "month").write.parquet("/output/path")
Bdf.write.partitionBy("year", "month").parquet("/output/path")
Cdf.write.partition("year", "month").saveAsTable("table_name")
Ddf.write.bucketBy(10, "year", "month").parquet("/output/path")
Explanation
The partitionBy method on DataFrameWriter creates directory-based partitioning with separate subdirectories for each unique combination of partition column values (e.g., year=2024/month=01/). This enables partition pruning for faster queries. bucketBy is used for bucketing tables, not directory partitioning, and requires saveAsTable. repartition redistributes data across partitions but does not create directory-based partitions on write. There is no partition method on DataFrameWriter (the correct method is partitionBy).
3. A Spark application running on a 10-node cluster with 4 cores per executor processes a DataFrame with 50 partitions. After a wide transformation that creates a new stage, how many tasks will execute in parallel at maximum during that stage? (Select one!)
A50 tasks (one per partition)
B10 tasks (one per node)
C40 tasks (total cores across cluster)
D200 tasks (default shuffle partitions)
Explanation
The maximum parallelism is determined by the total number of executor cores available across the cluster, which is 10 executors × 4 cores = 40 cores. Even though there are 50 partitions requiring 50 tasks, only 40 can execute simultaneously, with the remaining 10 tasks scheduled after the first wave completes. The number of nodes does not directly determine parallelism since multiple executors with multiple cores run tasks. The 50 partitions determine total task count, not concurrent parallelism. The default shuffle partitions of 200 would apply to shuffle operations but the question asks about maximum parallel execution based on available resources.
4. A Spark application reads a large CSV file using spark.read.option('inferSchema', 'true').option('header', 'true').csv('/data/large.csv'). The data engineer notices the read operation takes significantly longer than expected. What is the primary cause of the performance issue? (Select one!)
AThe header option forces sequential processing of all rows
BinferSchema requires scanning the data to determine column types, adding overhead
CCSV format does not support parallel reading across multiple executors
DReading the header row requires a full scan of the entire dataset
Explanation
The inferSchema option requires Spark to scan the data to infer column types, which adds significant overhead especially for large files. This requires reading through the data before the actual DataFrame is created. Providing an explicit schema avoids this scan and dramatically improves read performance. Reading the header row is a minimal operation that does not require full dataset scanning. CSV files support parallel reading through file splitting when not compressed. The header option does not force sequential processing, Spark can parallelize CSV reads across partitions. Best practice for production workloads is always providing explicit schemas rather than using inferSchema.
5. A real-time analytics application uses Structured Streaming to process IoT sensor data with event-time-based windows. The team configures watermarking with a 10-minute delay and uses Trigger.ProcessingTime('30 seconds'). Which statements accurately describe the behavior of this configuration? (Select two!)
Multiple correct answersAThe watermark is calculated as (max event time seen - 10 minutes) at the start of each 30-second trigger
BState data for windows older than the watermark is immediately deleted at the end of each trigger
CIn append mode, windowed aggregates are only output after the window end time plus watermark delay has passed
DThe 30-second processing time trigger guarantees that each micro-batch completes within 30 seconds
EWatermarking is only effective when using Trigger.Once or Trigger.AvailableNow, not with ProcessingTime triggers
Explanation
The watermark is computed at the beginning of each trigger as the maximum event time observed minus the watermark delay (10 minutes in this case), and this calculation occurs every 30 seconds with ProcessingTime trigger. In append mode, Spark only outputs completed windows after the watermark has passed the window end time, ensuring late data within the watermark delay is still processed. State is not immediately deleted but marked for cleanup. The ProcessingTime trigger specifies the interval between micro-batches, not a completion guarantee. Watermarking works with all trigger types including ProcessingTime, not just Once or AvailableNow.
Databricks Dumps Are Not Worth the 14-Day Retake Lockout
Databricks has not published as detailed a public security policy as Microsoft or AWS, but its enforcement is real: candidates on the Databricks Community forums have reported exam suspensions tied to irregular activity, and Databricks introduced a mandatory 14-day wait between retake attempts specifically to tighten exam security. Getting flagged does not just cost the exam fee, it costs weeks of eligibility while a suspension is under review.
The Apache Spark Developer exam rewards people who can actually reason through Spark's execution model, which is exactly what memorized answers cannot fake. CertCompanion's bank has 604 practice questions, 30 free, with explanations built around how Spark actually executes a query.
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