CDP-3002 by Cloudera Valid Free Exam Practice Test

Question 1

You're developing a Spark application with multiple stages, and you want to ensure that later stages only start processing after all data from the previous stage is complete. How can you achieve this dependency management in Spark?

A. Leverage Spark's lineage tracking and stage boundaries B. Implement custom logic to track completion of each stage C. Use explicit rdd.persist() calls in each stage D. Set appropriate configuration for spark.sql.shuffe.partitions

Correct Answer: A

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Question 2

In the context of Airflow, what is the purpose of the "XCom" feature?

A. To enable communication between different tasks within the same DAG. B. To execute commands across different operating systems. C. To manage dependencies and package installations across tasks. D. To trigger external systems when a DAG reaches a specific state.

Correct Answer: A

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Question 3

Which technologies are typically involved in schema inference processes in Cloudera Data Platform (CDP)?

A. Apache NiFi B. Traditional RDBMS C. Apache Spark D. Manual scripting E. Apache Avro

Correct Answer: A,C,E

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Question 4

You're building a Spark application that involves complex iterative data processing. Which option allows you to efficiently access and update intermediate results between iterations?

A. Use Spark's broadcast variables for frequently accessed data across iterations B. Leverage Spark's in-memory caching capabilities with rdd.cache() C. Implement custom data structures for managing intermediate data D. Store intermediate results in temporary tables using Spark SQL

Correct Answer: B

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Question 5

In an Airflow DAG, you have tasks A, B, C, and D. Task A must complete before B and C can start, but B and C can run in parallel. Task D should only run once both B and C have completed. How do you set up these dependencies?

A. A]] B ]] C]] D B. A]] B, C]] D C. A.set_downstream([B, C]) and set_upstream([B, C]) D. [A ]] B, A]] C] and [B, C] ]] D

Correct Answer: D

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Question 6

You need to design a DAG that can be easily triggered based on external events or data availability. How can you achieve this functionality?

A. Utilize Airflow sensors like FileSensor or S3KeySensor to wait for specific conditions before triggering the DAG. B. Schedule the DAG to run continuously at a fixed interval. C. Implement custom code within the DAG to actively check for external events or data availability. D. Rely on manual intervention to trigger the DAG whenever necessary.

Correct Answer: A

Question 7

How can "Explain Plan" help in optimizing query performance regarding data partitioning?

A. By showing the number of partitions created on the fly B. By revealing the encryption method used for partitioned data C. By indicating whether the query is able to take advantage of partition pruning D. By displaying the total size of all partitions

Correct Answer: C

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Question 8

Which Spark SQL join strategy is selected by default when the Catalyst optimizer cannot determine the size of tables involved in a join?

A. CartesianJoin B. SortMergeJoin C. ShuffleHashJoin D. BroadcastHashJoin

Correct Answer: B

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Question 9

Your project involves integrating Spark with a NoSQL database, MongoDB. You need to write a DataFrame 'df into a MongoDB collection named 'orders'. Which PySpark code snippet correctly achieves this?

A.

B.

C.

D.

Correct Answer: B

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Question 10

You have deployed a Spark application on Kubernetes, which is experiencing intermittent failures. To improve fault tolerance, you decide to implement checkpointing. Which of the following is the best approach to add checkpointing in a PySpark application?

A. Implement checkpointing at the application level outside of Spark. B.

C.

D. Enable checkpointing in Kubernetes configuration files.

Correct Answer: C

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Question 11

In the context of Cloudera's Optimization Framework, what role does data statistics collection play?

A. It is used to generate more data B. It helps the optimizer make informed decisions about data layout and query execution plans C. It provides metadata for security enforcement D. It reduces the need for data compression

Correct Answer: B

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Question 12

In Apache Spark, which of the following is the most effective strategy for minimizing data shuffling across nodes in a cluster?

A. Filtering data after a wide transformation B. Decreasing the number of partitions C. Using broadcast variables for small data D. Increasing the number of partitions

Correct Answer: C

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Question 13

You need to design a DAG that can be easily scaled horizontally to handle increasing workload. How can you achieve this goal?

A. Utilize external scheduling tools like Cron to manage DAG execution independently of Airflow workers. B. Increase the number of worker processes in the Airflow web server configuration. C. Implement custom logic within each task to handle parallel processing independently. D. Leverage Airflow pools to manage resource allocation and limit concurrent task execution.

Correct Answer: B,D

Question 14

You're tasked with optimizing the performance of your ETL pipeline in Airflow. What are some potential strategies to consider?

A. All of the above B. Leverage partitioning and bucketing techniques in the data warehouse to improve query performance. C. Increase the number of worker processes in Airflow to parallelize task execution. D. Utilize efficient data structures and algorithms within your custom Python operators for data transformation.

Correct Answer: A

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Cloudera CDP Data Engineer - Certification - CDP-3002 Exam Practice Test