CDP-3002 by Cloudera Actual Free Exam Q&As

Question 1

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

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Correct Answer: D Vote an answer

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Question 2

For a Hive table that is both partitioned and bucketed, what considerations must be taken into account to optimize a join query involving this table?

A. Bucketing considerations are irrelevant in the context of join queries, with partitioning being the sole factor impacting performance. B. The join should exclusively rely on the partitioned columns, ignoring the bucketed columns for optimal performance. C. Both the partitioning and bucketing columns should align with the join columns where possible to maximize the efficiency of data retrieval. D. Ensuring the join columns are neither partitioned nor bucketed as it may lead to increased complexity.

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Correct Answer: C Vote an answer

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Question 3

You're working with a CSV file containing missing dat
a. How can you efficiently handle missing values in a Spark DataFrame created from this file?

A. Ignore the rows with missing values during DataFrame creation B. Implement custom logic to identify and handle missing values individually C. Use the fillna() function with an appropriate replacement value D. Convert the DataFrame to a different format that doesn't support missing values

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Correct Answer: C Vote an answer

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Question 4

What are the potential trade-offs to consider when using checkpointing in Spark applications?

A. All of the above B. Checkpointing introduces overhead for storing and recovering data, impacting performance C. Checkpointing always improves performance and has no drawbacks D. Checkpointing requires manual configuration and can be error-prone

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Correct Answer: A,B Vote an answer

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Question 5

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

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

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Correct Answer: B Vote an answer

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Question 6

Which of the following is true about persisting RDDs in Apache Spark?
A Persisting an RDD in memory allows for faster access but increases the risk of data loss.

A. Persisting an RDD to disk is generally recommended for all RDDs to prevent data loss. B. Using MEMORY_ONLY_SER storage level reduces memory usage but increases CPU usage due to serialization. C. It is not possible to persist an RDD using a combination of memory and disk.

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Correct Answer: C Vote an answer

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Question 7

You are writing a PySpark application where you need to collect the final results from various Executors and present them to the user. Which aspect of the Spark Driver's role is primarily involved in this process?

A. Managing Kubernetes API interactions for Executor lifecycle. B. Collecting final results from Executors and providing the output. C. Optimizing resource utilization within the Kubernetes cluster. D. Constructing the logical plan from the user application.

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Correct Answer: B Vote an answer

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Question 8

You are working with a large, skewed dataset in Spark. How would you optimize processing to mitigate the impact of skew and improve performance?

A. Broadcast the skewed data to all executors. B. Addressing skewed data requires C. Implement custom partitioners to evenly distribute skewed values. D. Use salting on the skewed column during data partitioning.

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Correct Answer: B Vote an answer

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Question 9

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.

B. Enable checkpointing in Kubernetes configuration files. C. Implement checkpointing at the application level outside of Spark. D.

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Correct Answer: A Vote an answer

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Question 10

You want to debug an issue within your Spark application that interacts with Hive tables. What tools and techniques can you employ for effective debugging?

A. Print debug statements throughout the code to inspect intermediate data B. Implement unit tests for individual Spark operations within your application C. Leverage Spark's web UI and Hive logs for general error messages D. Use Spark's Lineage Viewer to visualize data flow and identify potential errors

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Correct Answer: B,D Vote an answer

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Question 11

In Spark, when is it most beneficial to use the repartitionByRange method?

A. When decreasing the number of partitions to reduce task scheduling overhead. B. When the data is highly skewed and a uniform distribution of data across partitions is required. C. When sorting data within each partition by a specified column or set of columns is required. D. When minimizing network traffic during shuffle operations is the primary concern.

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Correct Answer: C Vote an answer

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Question 12

You need to create a new Hive table from a Spark DataFrame. What are the different approaches you can consider?

A. Use the DataFrame.write.saveAsTable("table_name") method with appropriate options B. All of the above C. Directly write the DataFrame to a directory in HDFS and define a corresponding Hive table schema D. Convert the DataFrame to a temporary table and then use HiveQL commands to create a permanent table

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Correct Answer: B Vote an answer

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Question 13

How does Spark handle data shuffling during distributed processing?

A. By storing all data on a single node B. Spark doesn't perform data shuffling C. By broadcasting all data to each executor D. By transferring only required data between executors

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Correct Answer: D Vote an answer

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Question 14

What happens when a task in Airflow is marked as "skipped"?

A. The entire DAG is marked as failed, and no further tasks are executed. B. The skipped task's downstream tasks are automatically executed. C. Airflow retries the skipped task until it succeeds. D. The skipped task's downstream tasks are also skipped, unless their conditions for execution are met independently.

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Correct Answer: D Vote an answer

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Question 15

In a CI/CD pipeline, what is a key consideration when integrating Cloudera Data Engineering (CDE. service API calls for deploying Spark jobs, specifically regarding security?

A. Using generic Spark Docker images for all jobs to avoid API customization B. Hardcoding API keys in the pipeline scripts C. Incorporating API token rotation and secure secret management practices D. Disabling TLS for API endpoints to simplify integration

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Correct Answer: C Vote an answer

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