Databricks · DCASD
Databricks Certified Associate Developer for Apache Spark Practice Test
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
Questions
604
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. Start a practice session to test yourself across all 604 questions.
Preview — answers shown1. A data engineering team uses Spark Connect to execute jobs on a remote cluster from thin clients. Which deployment mode describes this architecture? (Select one!)
ALocal mode with embedded driver
BCluster mode with driver on remote cluster
CRemote mode with decoupled client-server architecture
DClient mode with driver on local machine
Explanation
Spark Connect introduces a decoupled client-server architecture where thin clients without full Spark installation can submit jobs to remote Spark clusters. This differs from traditional client and cluster modes which require Spark libraries on the driver machine. Client mode runs the driver locally but requires full Spark installation. Cluster mode runs the driver on the cluster but uses traditional Spark submit mechanisms. Local mode runs everything on a single machine.
2. A data engineer needs to read a partitioned Parquet dataset with the following directory structure: /data/year=2024/month=01/, /data/year=2024/month=02/, etc. They want to read only January and February data. Which approach reads only the required partitions? (Select one!)
AUse spark.read.parquet(/data).where(month IN (01, 02))
BUse spark.read.parquet(/data).filter(col(month).isin([01, 02]))
CRead with spark.read.parquet(/data/year=2024/month=01, /data/year=2024/month=02)
DUse spark.read.option(partitionFilter, month=01 OR month=02).parquet(/data)
Explanation
Reading specific paths like spark.read.parquet(/data/year=2024/month=01, /data/year=2024/month=02) only scans the specified partitions, minimizing I/O. Using filter() after reading applies partition pruning in Spark, but explicitly specifying paths is more direct and guaranteed to read only those partitions. Using where() has the same behavior as filter(). The partitionFilter option does not exist; partition pruning is handled through filter conditions or explicit path specification.
3. A team uses broadcast joins for dimension tables. After updating spark.conf.set('spark.sql.autoBroadcastJoinThreshold', 52428800), they observe some small tables are still not broadcast. Which two factors could prevent broadcasting? (Select two!)
Multiple correct answersAThe table statistics are stale or unavailable, causing Spark to estimate the table as larger than the threshold
BBroadcast joins are disabled when using left outer joins
CThe table is compressed, and Spark cannot determine the uncompressed size
DAdaptive Query Execution dynamically decided a sort-merge join is more efficient
EThe table contains more than 1 million rows, which exceeds the row count limit for broadcasting
Explanation
Spark relies on table statistics to determine if a table fits within the broadcast threshold. If statistics are missing, outdated, or inaccurate, Spark may overestimate the table size and choose not to broadcast it. Running ANALYZE TABLE updates statistics. Adaptive Query Execution can override initial join strategy decisions based on runtime statistics, potentially switching from broadcast to sort-merge if actual data characteristics suggest better performance. Broadcast joins work with all join types including left outer, right outer, and full outer joins. Compression is handled by Spark when reading data, and size estimates account for the in-memory representation. There is no row count limit for broadcasting, only a size threshold in bytes.
4. A data pipeline uses explode to flatten an array column containing product tags. The DataFrame has 1 million rows, with array sizes ranging from 0 to 100 elements. Some arrays are null or empty. The team needs to preserve all original rows even when arrays are empty or null. Which function should they use? (Select one!)
Aexplode(col('tags'))
Bexplode_outer(col('tags'))
Cflatten(col('tags'))
Dposexplode(col('tags'))
Explanation
explode_outer preserves rows with null or empty arrays by creating a single row with null values in the exploded columns, ensuring no original rows are lost. explode drops rows where the array is null or empty. posexplode adds position information but still drops null/empty arrays. flatten is for nested arrays (array of arrays) and does not handle the requirement to preserve rows with null/empty arrays.
5. A Spark application uses the following code: schema = 'user_id: int, name: string, email: string'. df = spark.createDataFrame(data, schema). What schema format is being used? (Select one!)
AHive schema format
BDDL-formatted string schema
CStructType schema definition
DJSON schema string
Explanation
The code uses DDL (Data Definition Language) formatted string schema, which is a concise string representation of schema using SQL-like syntax. The format is 'column_name: data_type, column_name: data_type'. This is an alternative to defining schemas using StructType and StructField objects. JSON schema would use JSON format with braces and quotes. StructType schema definition uses Python objects like StructType([StructField(...)]) rather than strings. While DDL format is similar to Hive table definitions, it is specifically called DDL string format in PySpark documentation.
More Databricks Practice Exams
Databricks Certified Machine Learning Associate
DCMLEA · 630 questions
Databricks Certified Data Engineer Associate
DCDEA · 628 questions
Databricks Certified Data Engineer Professional
DCDEP · 628 questions
Databricks Certified Data Analyst Associate
DCDAA · 627 questions
Databricks Certified Machine Learning Professional
DCMLEP · 622 questions
Databricks Certified Generative AI Engineer Associate
DCGAE · 620 questions
$17.99
One-time access to this exam
Full access to all 604 questions
Or $15/mo for all 253 exams
Detailed explanations
Free preview stays available