PracticeVideosBlogFlashcards
All Practice Exams

Free Practice Questions for Databricks Spark Developer Associate

Exam-style questions and explanations by OpenExamPrep.

✓ No registration✓ No credit card
142+ Questions
100% Free

Loading practice questions...

Same family resources

Explore More Databricks Certifications

Continue into nearby exams from the same family. Each card keeps practice questions, study guides, flashcards, videos, and articles in one place.

Databricks Certified Data Analyst Associate

Practice Questions200 questionsStudy Guide38 topicsCheat Sheet76 factsFlashcards50 cardsGames20 termsFeedVideos1 videoBlogs1 blog

Databricks Certified Data Engineer Associate

Practice Questions194 questionsStudy Guide40 topicsCheat Sheet110 factsFlashcards50 cardsGames20 termsFeedVideos1 videoBlogs1 blog

Databricks Generative AI Engineer Associate

Practice Questions250 questionsStudy Guide18 topicsCheat Sheet84 factsFlashcards50 cardsGames20 termsFeed

Databricks Certified Data Engineer Professional

Practice Questions200 questionsStudy Guide40 topicsFeed

Databricks Certified Machine Learning Associate

Practice Questions200 questionsStudy Guide41 topicsFeed

Microsoft Certified: Azure Databricks Data Engineer Associate (DP-750)

Practice Questions100 questionsStudy Guide59 topicsFeed

More From This Family

Videos and articles for deeper review.

VideoDatabricks Certified Data Analyst Associate Exam Guide 2026: SQL, AI/BI Dashboards, Genie, and Free PracticeVideoDatabricks Data Engineer Associate Exam Guide (2026): Pass on Your First TryArticleDatabricks Data Engineer Associate Exam Guide (2026): Pass on Your First Try26 min readArticleDatabricks Certified Data Analyst Associate Exam Guide 2026: SQL, AI/BI Dashboards, Genie, and Free Practice14 min read
Exam Review

Key Facts: Databricks Spark Developer Associate Exam

45

Scored Questions

Databricks

Not published

Passing Score

Databricks certification FAQ

90 min

Exam Duration

Databricks

$200

Exam Fee

Databricks

Online or test center

Delivery

Webassessor / Kryterion

2 years

Validity

Must retake

Databricks lists 45 scored multiple-choice questions in 90 minutes, a $200 registration fee, Python code snippets, and online or test-center delivery. Unscored items may also appear. The passing threshold and pass rate are not published. Certification is valid for two years.

Sample Databricks Spark Developer Associate Practice Questions

Try these sample questions to review concepts for the Databricks Spark Developer Associate exam. Each question includes a detailed explanation. Start the interactive quiz above for the full 142+ question experience with AI tutoring.

1In Apache Spark, which process is responsible for coordinating the execution of jobs and scheduling tasks on executors?
A.Executor
B.Driver
C.Worker
D.Cluster Manager
Explanation: The driver runs the main() function, creates the SparkContext/SparkSession, plans jobs, and schedules tasks to executors. Executors are JVM processes that run tasks and hold cached data. Workers are nodes hosting executors. The cluster manager (YARN, Kubernetes, Standalone) allocates resources.
2Which of the following is a narrow transformation in Spark?
A.groupBy
B.join (sort-merge)
C.filter
D.distinct
Explanation: filter is narrow: each input partition maps directly to one output partition with no shuffle. groupBy, sort-merge join, and distinct all require shuffling data across partitions (wide transformations). Other narrow transformations: select, map, withColumn, coalesce (to fewer partitions).
3Which Spark action triggers the execution of transformations and returns data to the driver?
A.df.select()
B.df.filter()
C.df.collect()
D.df.withColumn()
Explanation: collect() is an action that materializes the DataFrame and returns all rows to the driver. Actions trigger execution. select, filter, and withColumn are transformations (lazy). Other actions: count, show, take, first, write.
4What does lazy evaluation mean in Spark?
A.Transformations are not executed until an action is called
B.Transformations run sequentially without optimization
C.Data is not loaded from disk
D.Only small DataFrames are evaluated
Explanation: Spark accumulates transformations into a logical plan and only executes when an action (e.g., count, collect, write) is invoked. This allows the Catalyst optimizer to reorder and fuse operations, producing an efficient physical plan. This differs from eager execution where each operation runs immediately.
5What does this PySpark code do? df.select('name', 'age').filter('age > 18').show()
A.Selects name and age columns, filters rows where age > 18, displays top rows
B.Updates name and age columns
C.Deletes rows where age > 18
D.Returns an error because filter uses a string
Explanation: The query projects two columns, filters using a SQL-string expression (df.filter takes a string or Column expression), and calls the show() action to display (default 20) rows. Both .filter() and .where() accept strings or Column expressions.
6Which PySpark code renames a column named 'FirstName' to 'first_name'?
A.df.withColumnRenamed('FirstName', 'first_name')
B.df.rename('FirstName', 'first_name')
C.df.alias('FirstName', 'first_name')
D.df.withColumn('FirstName', F.col('first_name'))
Explanation: df.withColumnRenamed(old, new) is the standard way to rename a column in a DataFrame. alias is used on Column or DataFrame expressions for aliasing, not a DataFrame-level rename. There is no df.rename in PySpark. withColumn replaces or adds columns, not renames.
7Which method adds a new column to a DataFrame based on a computed expression?
A.df.addColumn('newcol', expr)
B.df.withColumn('newcol', F.col('a') + F.col('b'))
C.df.new('newcol', expr)
D.df.insertColumn('newcol', expr)
Explanation: df.withColumn(name, col_expr) adds (or replaces) a column. Use F.col, F.lit, F.when, or any Column expression. addColumn, new, insertColumn are not PySpark methods.
8Given df has columns id, name, age, which code returns only the id and age columns cast to Long and Integer respectively?
A.df.select(F.col('id').cast('long'), F.col('age').cast('int'))
B.df.alias('id', 'long').alias('age', 'int')
C.df.cast('id', 'long').cast('age', 'int')
D.df.select(F.cast('id','long'), F.cast('age','int'))
Explanation: cast() is a method on Column objects accessed via F.col(name).cast(type). Valid type names are 'long', 'int', 'double', or spark data type instances (LongType()). Options B-D are not valid PySpark syntax.
9Which method removes duplicate rows across all columns in a DataFrame?
A.df.distinct()
B.df.unique()
C.df.removeDuplicates()
D.df.dedup()
Explanation: df.distinct() removes duplicates considering all columns. df.dropDuplicates(subset=[...]) can deduplicate based on a subset of columns. unique(), removeDuplicates(), and dedup() are not PySpark methods.
10Which F.* function replaces null values with a specified default?
A.F.coalesce
B.F.lit
C.F.ifnull
D.Both F.coalesce and F.ifnull are valid for this
Explanation: F.coalesce(col1, col2, ...) returns the first non-null value among its arguments — commonly used like coalesce(col('a'), lit(0)) to replace nulls with 0. Since Spark 3.5.0 (SPARK-43937), F.ifnull(col, default) was added and does the exact same two-argument null-replacement directly (equivalent to coalesce(col, default)). F.lit produces a literal value (not a null-replacement by itself). F.when is for general conditional CASE WHEN logic, not the idiomatic tool for this.

About the Databricks Spark Developer Associate Exam

The Databricks Certified Associate Developer for Apache Spark exam validates an associate-level developer's ability to use the Apache Spark DataFrame API in Python. It covers Spark architecture, DataFrame transformations and actions, schemas and data sources, aggregations and joins, window functions, Spark SQL, partitioning and caching, UDFs and pandas_udf, Spark Connect, Structured Streaming basics, and troubleshooting/tuning.

Exam sponsor: Databricks; delivered through Kryterion/Webassessor online or at test centers. The requirements and fees below concern the certification or admission exam, separate from our free practice resources.

Questions

45 questions

Time Limit

90 minutes

Passing Score

Not published by Databricks

Exam / Certification Fees

$200

Exam sponsor website

Reported exam pass rate: Not published by Databricks. No official aggregate pass rate is published. Exam sponsor website

Fees, eligibility, and exam policies can change. Confirm them with the exam sponsor before applying or paying.

Our practice resources: topics covered

We aim to reflect publicly available exam outlines and topic information in our study resources. Coverage, format, and difficulty may differ from the actual exam, and we cannot guarantee that every detail is accurate or current. Confirm exam requirements, fees, and policies with the official exam sponsor.

20%

Apache Spark Architecture and Components

Cluster architecture (driver, executors, cluster managers), execution hierarchy (applications, jobs, stages, tasks), lazy evaluation, shuffle mechanism, fault tolerance, and JVM memory/GC management.

20%

Using Spark SQL

SQL sources including JDBC and files, direct ORC/JSON/CSV/text/Delta queries, save modes, persistent partitioned/sorted tables, and temporary views.

30%

Developing Apache Spark™ DataFrame/DataSet API Applications

Column/row changes, validation/deduplication, count/approximate distinct/mean/summary, dates/epoch conversion, joins/unions, schema IO, iteration/list conversion, UDFs/StateStores, broadcast variables and accumulators.

10%

Troubleshooting and Tuning Apache Spark DataFrame API Applications

Partitioning/skew/shuffle reduction and cluster utilization; AQE; driver and executor logging/monitoring for OOM and underutilization.

10%

Structured Streaming

Micro-batch engine and conditional exactly-once recovery, streaming IO/output modes, projection/window aggregation, and deduplication with and without watermarks.

5%

Using Spark Connect to deploy applications

Spark Connect features and the distinction between client, cluster and local deployment modes.

5%

Using Pandas API on Apache Spark

Benefits of Pandas API on Spark and creating/invoking pandas UDFs.

Preparing for the Databricks Spark Developer Associate Exam

What You Need to Know

  • Passing score: Not published by Databricks
  • Exam length: 45 questions
  • Time limit: 90 minutes
  • Exam / certification fees: $200 Official sources

Using Our Practice Resources

  • Work through all 142 available questions
  • Review every answer and explanation
  • Track weak areas and revisit them
  • Use our AI tutor for tough concepts

Databricks Spark Developer Associate: Suggested Study Strategy

1Compare common lazy transformations with actions; metadata discovery and eager SQL commands can also launch work.
2Reason from the physical join/aggregation plan rather than assuming every join shuffles.
3Practice joins, unions, date/epoch handling, approximate aggregates, validation, and schema/row inspection.
4Use measured task size, skew and memory to choose repartition/coalesce and evaluate AQE.
5Prefer built-in functions; compare scalar Python UDFs with Arrow-batched pandas UDFs without assuming a fixed speedup.
6Distinguish watermark-based state cleanup from a global late-row filter; know streaming deduplication and sink delivery guarantees.
7Review local/client/cluster deployment and Spark Connect API limits, plus Pandas API indexing and collection risks.

Frequently Asked Questions

What is the passing score for Databricks Spark Developer Associate?

Databricks does not publish the passing threshold. Its certification FAQ says thresholds are determined statistically and can change as exams are updated; a claim of 70% or 32 correct answers is not a current official rule.

How many questions and how much time does the exam have?

The current exam has 45 scored multiple-choice questions and a 90-minute time limit. Databricks may include unidentified unscored items and factors additional time into exams for them. All code snippets are in Python; no test aides, including API documentation, are provided.

How much does the exam cost and where is it delivered?

The current registration fee is $200 USD. Databricks offers online proctoring and test-center delivery through Kryterion/Webassessor. Check the official exam page and checkout for scheduling, applicable charges and current availability.

Are there prerequisites?

No prior credential or training is required. The certification FAQ limits exams to candidates aged 18 or older. Databricks recommends related training and at least six months of hands-on experience with the tested tasks.

How long should I study, and which resources should I use?

OpenExamPrep suggests an initial 40–60-hour plan, adjusted after hands-on diagnostics; this is a planning estimate, not a Databricks requirement or pass guarantee. Use the current official exam guide, its recommended Databricks Academy courses, and Apache Spark documentation. Databricks Free Edition or a local PySpark environment can support practice, subject to their limits.

What is the retake and recertification policy?

Databricks requires at least 14 days between attempts, permits repeated attempts, and charges each attempt at the original exam price. Certification lasts two years. Recertification requires taking the full exam that is currently live.

Which topics does the current Spark Developer exam cover?

The seven domains are architecture (20%), Spark SQL (20%), DataFrame/Dataset applications (30%), troubleshooting and tuning (10%), Structured Streaming (10%), Spark Connect deployment (5%), and Pandas API on Spark (5%). The official guide linked from the certification page is the current topic reference.