100+ Free Databricks Spark Developer Associate Practice Questions

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Question 1

Score: 0/0

In Apache Spark, which process is responsible for coordinating the execution of jobs and scheduling tasks on executors?

Executor

Driver

Worker

Cluster Manager

to track

2026 Statistics

Key Facts: Databricks Spark Developer Associate Exam

Exam Questions

Databricks

70%

Passing Score

Databricks

90 min

Exam Duration

Databricks

$200

Exam Fee

Databricks

Online

Delivery

Webassessor / Kryterion

2 years

Validity

Must retake

The Databricks Spark Developer Associate exam costs $200, has 60 multiple-choice questions in 90 minutes, and requires a 70% passing score. Delivered online via Webassessor/Kryterion. Covers Spark architecture (driver/executors/jobs/stages, narrow vs wide, AQE), DataFrame API, joins, aggregations, schemas, partitioning, caching, UDFs, and Spark SQL. Valid 2 years.

Sample Databricks Spark Developer Associate Practice Questions

Try these sample questions to test your Databricks Spark Developer Associate exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ 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.F.when

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. F.lit produces a literal. ifnull is SQL-style (not a PySpark F function). when is conditional.

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 or Scala. 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.

Questions

60 scored questions

Time Limit

90 minutes

Passing Score

70%

Exam Fee

$200 (Databricks / Webassessor (Kryterion online proctored))

Databricks Spark Developer Associate Exam Content Outline

15-20%

Spark Architecture & Execution

Driver, executors, cluster manager, jobs/stages/tasks, shuffle, narrow vs wide transformations, lineage, lazy evaluation, actions vs transformations, deploy modes, Adaptive Query Execution (AQE)

40-45%

DataFrame API & Column Operations

select, filter/where, groupBy, agg, join types, orderBy, withColumn/withColumnRenamed, col/expr/F.* functions (lit, when, coalesce, concat), casting, explode, handling nulls

15-20%

Schemas, Data Sources & I/O

StructType/StructField, data types, schema inference vs explicit, reading/writing CSV/JSON/Parquet/ORC/Delta, partitionBy, bucketBy, save modes, multiline JSON, header/sep options

10-15%

Aggregations, Window Functions & Spark SQL

groupBy + agg with F.count/sum/avg/min/max, window functions with Window.partitionBy/orderBy, row_number/rank/dense_rank/lead/lag, rowsBetween/rangeBetween, createOrReplaceTempView, spark.sql

10-15%

Performance, Caching, UDFs & Streaming

repartition vs coalesce, cache/persist with StorageLevel, broadcast hint for small tables, sort-merge vs broadcast hash join, Python UDFs vs pandas_udf, Spark Connect, Structured Streaming basics, Delta Lake on Databricks

How to Pass the Databricks Spark Developer Associate Exam

What You Need to Know

Passing score: 70%
Exam length: 60 questions
Time limit: 90 minutes
Exam fee: $200

Keys to Passing

Complete 500+ practice questions
Score 80%+ consistently before scheduling
Focus on highest-weighted sections
Use our AI tutor for tough concepts

Databricks Spark Developer Associate Study Tips from Top Performers

1Memorize the difference between transformations (lazy) and actions (eager) — count, collect, take, show, write trigger execution

2Know narrow (map, filter, select, union, coalesce) vs wide (groupBy, repartition, join, distinct) transformations

3Master join types: inner, left_outer, right_outer, full_outer, left_semi, left_anti, cross — and broadcast hint

4Understand when to use coalesce (narrow, reduce partitions) vs repartition (full shuffle, any count)

5For window functions, know Window.partitionBy().orderBy() and rowsBetween/rangeBetween frames

6Prefer pandas_udf (Arrow-based, vectorized) over plain Python UDFs for performance

7Know Delta Lake extras on Databricks — MERGE INTO, OPTIMIZE, Z-ORDER, time travel with VERSION AS OF

Frequently Asked Questions

What is the Databricks Spark Developer Associate exam?

The Databricks Certified Associate Developer for Apache Spark is an associate-level certification that validates developer skills with the Spark DataFrame API. It tests knowledge of Spark architecture, DataFrame transformations and actions, joins, aggregations, window functions, schemas, partitioning, caching, and Spark SQL. The exam supports Python and Scala API questions.

How many questions are on the Databricks Spark Developer exam?

The exam has 60 multiple-choice questions to be completed in 90 minutes. The passing score is 70%. Questions are a mix of API syntax recall, conceptual architecture, and code-analysis questions where you must predict output or identify the correct DataFrame operation.

How much does the Databricks Spark Developer exam cost?

The exam registration fee is $200 USD (plus applicable tax). It is delivered through Webassessor with Kryterion online proctoring — you can take it from home with a webcam. Databricks offers free self-paced training at customer-academy.databricks.com that aligns with exam objectives.

How long is the Databricks Spark Developer certification valid?

The Databricks Certified Associate Developer for Apache Spark certification is valid for 2 years. After that you must retake the current version of the exam to maintain certified status. Databricks updates the exam periodically to reflect Spark 3.x/4.x and Spark Connect features.

Should I take the Python or Scala version of the Spark Developer exam?

Databricks lets you see code examples in both Python (PySpark) and Scala in the same exam — you can choose which language you read for each question. Most candidates use Python because it is the dominant Spark language on Databricks, but Scala developers can rely on Scala syntax for most questions.

How should I prepare for the Databricks Spark Developer exam?

Plan for 40-60 hours of hands-on practice. Work through 'Apache Spark Programming with Databricks' in Databricks Academy. Memorize F.* function signatures, window specs, and join syntax. Practice on the Databricks Community Edition or free trial. Read the Spark DataFrame API docs. Complete 100+ practice questions.