Get 2026 Updated Free Cloudera CDP-3002 Exam Questions & Answer [Q27-Q45]

Rate this post

Get 2026 Updated Free Cloudera CDP-3002 Exam Questions and Answer

CDP-3002 Dumps PDF and Test Engine Exam Questions

QUESTION 27
How does partition pruning contribute to query performance in the Cloudera Data Platform?

By increasing the number of partitions

By removing unnecessary data replication

By selecting only relevant partitions for query execution

By encrypting data at rest

QUESTION 28
How can you monitor the storage level and usage of persisted RDDs in your Spark application?

Manually analyze the Spark application code

Use Spark’s web UI and look for information under the “Storage” tab

Leverage Spark metrics like rdd.getStorageLevel() and rdd.getPersistedSize()

All of the above

QUESTION 29
What is the impact of setting the Spark configuration spark.sql.autoBroadcastJoinThreshold to -1?

It disables the broadcast join feature, forcing all joins to be shuffled joins.

It sets an unlimited threshold for broadcasting tables, which may cause out-of-memory errors.

It automatically selects the optimal threshold for broadcasting based on the cluster’s current workload.

It increases the threshold for choosing which table to broadcast in a join, potentially improving join performance.

QUESTION 30
You’re building an Airflow ETL pipeline that involves data validation checks. How can you integrate these checks into the pipeline and handle potential failures?

Implement the validation checks within custom Python operators and raise exceptions upon failure, triggering downstream tasks to handle the error.

Use the FailoverOperator to automatically skip downstream tasks if the validation check fails.

Leverage XCom to share the validation results with downstream tasks for further processing.

Configure the DAG to send email notifications upon validation failures for manual intervention.

QUESTION 31
An Iceberg job fails with an “out of memory” error. Which Spark configuration changes might help? (Choose two)

Increase driver memory (spark.driver.memory)

Increase executor memory (spark.executor.memory)

Decrease the size of Spark partitions (spark.sql.shuffle.partitions)

Enable Spark broadcast joins

Upgrade to a larger Kubernetes pod size.

QUESTION 32
What is the primary advantage of using Apache Spark for distributed processing compared to traditional single-node processing?

Improved data visualization capabilities

Increased storage capacity

Faster processing of large datasets

Enhanced data security

QUESTION 33
Your Airflow DAG includes tasks that can potentially fail due to various reasons. How can you handle such failures and ensure the overall workflow continues as intended?

Implement custom logic within each task to handle potential errors and retry failed tasks manually.

Configure the DAG to automatically retry failed tasks a specific number of times.

Utilize Airflow XCom to share information about failed tasks with downstream tasks for alternative processing.

All of the above

QUESTION 34
A PySpark application is facing performance issues due to uneven distribution of data across the nodes. Which approach would best help in resolving this issue?

Use ‘df.collect()’.

Apply ‘df.coalesce(Y to reduce the number of partitions.

Employ to redistribute data based on a key.

Convert the DataFrame to an RDD and then back to a DataFrame.

QUESTION 35
You need to design an Airflow DAG that waits for a specific file to become available before proceeding with the downstream tasks. How can you achieve this dependency?

Use the File sensor operator to check for the file’s existence and trigger downstream tasks upon its arrival.

Implement a custom loop within a Python operator to continuously check for the file until it appears.

Configure the source system to notify Airflow when the file is ready for processing.

Schedule the DAG to run periodically, hoping the file becomes available eventually.

QUESTION 36
In Apache Airflow, which operator is best suited for running data quality checks on a Hive table after data ingestion?

BashOperator

PythonOperator

HiveOperator

ShortCircuitOperator

QUESTION 37
Your Spark application encounters performance issues when reading data from a large Hive table. What potential optimization techniques can you explore?

Increase the number of Spark executors without further optimization

Use a different file format for the Hive table, like CSV, for faster parsing

Leverage partition pruning to only read relevant data from the table

Implement custom data compression logic within Spark for improved storage efficiency

QUESTION 38
Which of the following commands is used to install PySpark in your development environment?

pip install pyspark

npm install pyspark

yarn add pyspark

brew install pyspark

QUESTION 39
You want to write the results of a Spark DataFrame back to a Hive table. How can you achieve this efficiently?

Convert the DataFrame to a temporary table and then use HiveQL operations

Use the DataFrame.write.saveAsTable(“table_name”) method

Implement a custom function to write individual rows to the Hive table

Use the hiveContext.sql(“lNSERT INTO table_name SELECT FROM df’) method

QUESTION 40
What is the correct way to define a start date for a DAG in Apache Airflow, ensuring that the DAG does not trigger immediately upon deployment?

Use datetime.now() as the start date.

Set the start date to a future date using the datetime module.

Use ) to automatically set the start date to one day before the current date.

Leave the start date undefined.

QUESTION 41
You want to select specific columns from a Spark DataFrame and rename them. How can you achieve this in Spark SQL?

Use the select() method with column names and aliases within parentheses

Modify the original DataFrame schema directly

Implement custom logic to iterate through the DataFrame and create a new one

Use Spark SQL’s ALTER TABLE statement to modify the table schema

QUESTION 42
You’re experimenting with Iceberg table formats (vl and v2). Which of the following statements is true regarding their differences?

V2 introduces mandatory partitioning, while V1 allows for unpartitioned tables.

V2 supports new data types like UUIDs, which are unavailable in V1.

V2 uses manifest lists instead of manifest files for tracking data files.

V2 tables are generally less performant than V1 tables due to added metadata overhead.

QUESTION 43
How can you utilize Spark SQL for complex data analysis involving joins and aggregations on large datasets?

By directly writing SQL queries against distributed data in Spark.

By implementing custom logic in RDDs for data manipulation.

By using Spark ML libraries for machine learning tasks on data.

Both A and B.

QUESTION 44
When deploying a packaged PySpark application using ‘spark-submit’, which option is used to include the packaged dependencies?

–packages

–jars

–py-files

-files

QUESTION 45
In the context of Spark SQL, what does the Catalyst optimizer use to optimize queries?

A cost-based optimization model that considers the size of intermediate data

A rule-based optimization model that applies predefined rules to simplify queries

Machine learning algorithms to predict the fastest query execution plan

Verified CDP-3002 exam dumps Q&As with Correct 320 Questions and Answers: https://www.braindumpstudy.com/CDP-3002_braindumps.html

Tags: CDP-3002 cheap dumps, CDP-3002 new practice questions ebook, CDP-3002 online version, CDP-3002 prep guide, CDP-3002 reliable test testking, CDP-3002 study guide free pdf, CDP-3002 valid exam vce, new CDP-3002 exam test

Exam Study Guide & Braindumps

Get 2026 Updated Free Cloudera CDP-3002 Exam Questions & Answer [Q27-Q45]

Leave a Reply Cancel reply