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100+ Free Cloudera CDP-3002 Practice Questions

Pass your Cloudera Certified Associate - Data Engineer (CDP-3002) exam on the first try — instant access, no signup required.

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2026 Statistics

Key Facts: Cloudera CDP-3002 Exam

50 questions

CDP-3002 is a 50-question multiple-choice exam

Cloudera Data Engineer Exam Guide (CDP-3002)

90 minutes

Time allowed to complete the CDP-3002 exam

Cloudera Data Engineer Exam Guide (CDP-3002)

55%

Passing score for the Cloudera Data Engineer exam

Cloudera Data Engineer Exam Guide (CDP-3002)

USD $330

Exam fee for CDP-3002, subject to regional variation

Cloudera certification pricing

48% Spark

Spark is the largest scored objective on CDP-3002

Cloudera Data Engineer Exam Guide (CDP-3002)

22% tuning

Performance Tuning is the second-largest CDP-3002 objective

Cloudera Data Engineer Exam Guide (CDP-3002)

Online proctored

CDP-3002 is delivered online with remote proctoring

Cloudera certification program

100

Free original practice questions in this bank

OpenExamPrep

The Cloudera CDP-3002 exam certifies associate-level data engineers on the Cloudera Data Platform. It is a 50-question, multiple-choice, online proctored exam with a 90-minute time limit and a 55% passing score, and the fee is USD $330. Objectives are weighted Spark 48%, Performance Tuning 22%, Airflow 10%, Deployment 10% and Iceberg 10%, with strong emphasis on building and tuning Spark pipelines in Cloudera Data Engineering (CDE). This 100-question bank provides original, self-contained multiple-choice practice mapped to those objectives, including Spark and SQL code-reading items.

Sample Cloudera CDP-3002 Practice Questions

Try these sample questions to test your Cloudera CDP-3002 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 of the following operations is a transformation rather than an action?
A.count()
B.collect()
C.filter()
D.show()
Explanation: Transformations such as filter() are lazy: they describe a new DataFrame/RDD lineage but do not execute until an action is called. count(), collect() and show() are actions that trigger execution of a Spark job.
2What does Spark's lazy evaluation mean for a chain of DataFrame transformations?
A.Each transformation runs immediately as it is called
B.No computation runs until an action is invoked
C.Transformations run only on the driver node
D.Spark caches every intermediate result automatically
Explanation: Spark builds a logical plan (lineage) as transformations are chained but defers execution until an action requires a result. This lets the Catalyst optimizer combine and reorder steps before any work runs.
3In Cloudera Data Engineering (CDE), Spark applications run on which underlying cluster orchestration technology?
A.Apache YARN
B.Apache Mesos
C.Kubernetes
D.Standalone scheduler
Explanation: Cloudera Data Engineering runs Spark on Kubernetes, using containerized executors inside CDE virtual clusters. This is a key architectural difference from legacy CDH/HDP deployments that used YARN.
4Given a PySpark DataFrame df, which expression returns a new DataFrame keeping only rows where the column 'age' is greater than 30?
A.df.where(df.age > 30)
B.df.select(df.age > 30)
C.df.groupBy(df.age > 30)
D.df.agg(df.age > 30)
Explanation: df.where(condition) (an alias of filter()) returns a new DataFrame containing only rows that satisfy the boolean condition. where() and filter() are interchangeable in the DataFrame API.
5Which PySpark call adds a new column 'bonus' equal to 10% of an existing 'salary' column?
A.df.addColumn('bonus', df.salary * 0.1)
B.df.withColumn('bonus', df.salary * 0.1)
C.df.select('bonus', df.salary * 0.1)
D.df.newColumn('bonus', df.salary * 0.1)
Explanation: withColumn(name, expression) returns a new DataFrame with the added or replaced column computed from the expression. It is the standard way to derive a column in the DataFrame API.
6A developer writes: df.groupBy('dept').agg(avg('salary').alias('avg_sal')). What does this produce?
A.One row per employee with the average salary
B.One row per department with its average salary
C.The overall average salary across all departments
D.An error because agg requires count()
Explanation: groupBy('dept') buckets rows by department, and agg(avg('salary')) computes one aggregated value per group. The result has one row per distinct department with column avg_sal.
7Which statement about Spark RDDs and DataFrames is correct?
A.DataFrames cannot be optimized by Catalyst
B.RDDs carry schema information and column names
C.DataFrames are optimized by the Catalyst optimizer while raw RDDs are not
D.RDDs are always faster than DataFrames for structured data
Explanation: DataFrames expose a schema, allowing the Catalyst optimizer and Tungsten engine to optimize query plans and code generation. Raw RDD operations are opaque to Catalyst, so they miss those optimizations.
8In Spark, what does calling df.cache() accomplish?
A.It immediately writes the DataFrame to HDFS
B.It marks the DataFrame to be stored in memory after its first computation
C.It triggers a job and prints the rows
D.It permanently registers the DataFrame as a Hive table
Explanation: cache() is a lazy hint that marks a DataFrame to be persisted at the default MEMORY_AND_DISK storage level. The data is actually stored when the next action materializes it, speeding up later reuse.
9Which method reads a Parquet file into a Spark DataFrame in PySpark?
A.spark.read.parquet('/path')
B.spark.load.parquet('/path')
C.spark.parquet.read('/path')
D.spark.open('/path', format='parquet')
Explanation: spark.read.parquet(path) uses the DataFrameReader to load Parquet data, automatically inferring the schema from the file's embedded metadata. The general form spark.read.format('parquet').load(path) is equivalent.
10A pipeline reads JSON without specifying a schema using spark.read.json(path). What is the main drawback compared with supplying an explicit schema?
A.JSON cannot be read at all without a schema
B.Spark must scan the data to infer the schema, adding an extra pass and cost
C.The resulting DataFrame has no columns
D.Spark converts all values to strings
Explanation: Schema inference for JSON requires Spark to read through the data to determine column names and types, adding an extra job and I/O. Providing an explicit StructType schema avoids that inference pass and makes types deterministic.

About the Cloudera CDP-3002 Exam

The Cloudera Certified Associate - Data Engineer exam (CDP-3002) validates the skills data engineers need to design, develop and optimize data workflows on the Cloudera Data Platform (CDP). It focuses heavily on Apache Spark running on Kubernetes in Cloudera Data Engineering (CDE): the DataFrame API, distributed processing, Spark SQL and Hive integration, and persistence. The exam also covers performance tuning (the Spark optimization framework, joins, caching and partitioning), orchestrating ETL pipelines with Apache Airflow in CDE, deploying jobs through the CDE API and CLI, and core Apache Iceberg concepts. It is delivered as a 50-question, online proctored multiple-choice exam with a 90-minute limit and a 55% passing score.

Assessment

50 multiple-choice questions across five objective areas: Spark (48%), Performance Tuning (22%), Airflow (10%), Deployment (10%) and Iceberg (10%).

Time Limit

90 minutes.

Passing Score

55%.

Exam Fee

USD $330, subject to regional variation and applicable taxes. (Cloudera, Inc.)

Cloudera CDP-3002 Exam Content Outline

48%

Spark

Spark fundamentals on Kubernetes in CDE, the DataFrame and Dataset APIs, transformations versus actions, lazy evaluation, distributed processing, Spark SQL and Hive integration, reading and writing data, and persistence. This is the largest scored area, so most practice here covers PySpark DataFrame operations, joins, aggregations and code reading.

22%

Performance Tuning

Using the Spark UI and tuning tools, the Catalyst optimizer and Adaptive Query Execution, schema inference cost, shuffle and skew, broadcast versus sort-merge joins, caching/persistence levels, repartition versus coalesce, and partitioning and file-layout choices for efficient pipelines.

10%

Airflow

Authoring Apache Airflow DAGs in CDE, the CDE Airflow operators, scheduling and dependencies, implementing incremental extraction from source systems, idempotent/backfill-safe design, and orchestrating multi-step ETL with data-quality checks.

10%

Deployment

Working in the CDE Data Engineering service: virtual clusters, resources, jobs and sessions, and deploying and running jobs using the CDE CLI and REST API, including uploading files/resources and configuring Spark job parameters.

10%

Iceberg

Apache Iceberg as an open table format: metadata and manifest layers, snapshots and time travel, hidden partitioning, schema and partition evolution, and creating and querying Iceberg tables from Spark and Impala on CDP.

How to Pass the Cloudera CDP-3002 Exam

What You Need to Know

  • Passing score: 55%.
  • Assessment: 50 multiple-choice questions across five objective areas: Spark (48%), Performance Tuning (22%), Airflow (10%), Deployment (10%) and Iceberg (10%).
  • Time limit: 90 minutes.
  • Exam fee: USD $330, subject to regional variation and applicable taxes.

Keys to Passing

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

Cloudera CDP-3002 Study Tips from Top Performers

1Spend most of your time on Spark: it is 48% of the exam. Practise the PySpark DataFrame API (select, filter, withColumn, groupBy/agg, join) and be able to read code and predict its output.
2Master the difference between transformations and actions and the meaning of lazy evaluation, because many questions test which call triggers a job.
3For Performance Tuning, learn when a broadcast join beats a sort-merge join, how caching and persistence levels work, and the difference between repartition and coalesce.
4Practise reading small Airflow DAGs: understand operators, task dependencies (set with >> or set_upstream), scheduling, and how incremental/backfill-safe extraction is designed.
5Know the CDE building blocks - virtual clusters, resources, jobs and sessions - and how the CDE CLI and REST API deploy and run a Spark job.
6Review Apache Iceberg core concepts: snapshots and time travel, hidden partitioning, and schema/partition evolution, and how Iceberg differs from plain Hive tables.

Frequently Asked Questions

How many questions are on the Cloudera CDP-3002 exam and how long is it?

The CDP-3002 exam has 50 multiple-choice questions and a 90-minute time limit. It is delivered as an online, remotely proctored exam.

What score do I need to pass CDP-3002?

You need 55% to pass the Cloudera Data Engineer (CDP-3002) exam. There is no negative marking; every question is multiple choice.

What topics does the CDP-3002 exam cover?

The objectives are weighted Spark 48%, Performance Tuning 22%, Airflow 10%, Deployment 10% and Iceberg 10%. Spark on Cloudera Data Engineering and tuning are by far the largest scored areas.

How much does the CDP-3002 exam cost?

The exam fee is USD $330, subject to regional pricing variation and applicable taxes. Check the official Cloudera exam guide for current pricing in your region.

Do I need hands-on Cloudera experience to pass?

Cloudera recommends practical experience building Spark and Airflow pipelines in Cloudera Data Engineering (CDE). The exam is associate-level but assumes you can read PySpark and Spark SQL and reason about tuning.

Are these official Cloudera practice questions?

No. These are original OpenExamPrep questions written to match the published CDP-3002 objectives. Use Cloudera's official exam guide and documentation for authoritative content.