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Latest Cloudera CDP-3002: Dumps CDP Data Engineer - Certification Exam Questions - Authoritative DumpExam Reliable CDP-3002 Exam Blueprint

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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q71-Q76):

NEW QUESTION # 71
You are developing a PySpark application that processes a large dataset. Your application involves a transformation that requires shuffling data across the Executors. Which of the following responsibilities of the Spark Driver is most directly involved in this process?

A. Monitoring the progress and status of tasks on the Executors.
B. Managing the lifecycle of Executor pods in Kubernetes.
C. Interpreting the user application and creating an execution plan.
D. Managing the shuffling and aggregation of data across Executors.

Answer: D

Explanation:
The Spark Driver is responsible for managing operations that involve shuffling data. In this scenario, where the PySpark application includes a transformation requiring data shuffling, the Driver coordinates the shuffling and aggregation of data across different Executors to ensure correct execution of the transformation.

NEW QUESTION # 72
When writing a DataFrame to a CSV file, what potential issues should you consider and how can you address them?

A. All of the above
B. Ensure proper handling of special characters and delimiters to avoid data corruption
C. No specific issues need to be considered, as CSV is a simple format
D. Choose an appropriate compression format like Gzip to reduce file size

Answer: A

Explanation:
CSV files can present challenges. Special characters and delimiters B need proper handling to avoid misinterpretations. Compression C like Gzip can significantly reduce file size without data loss. Considering all these aspects ensures efficient and reliable storage of DataFrame data in CSV format.

NEW QUESTION # 73
You're working with an Airflow DAG that performs data quality checks on sensitive dat a. How can you ensure data security during the checks?

A. Implement data masking techniques to obfuscate sensitive information during the checks.
B. All of the above
C. Utilize environment variables to store sensitive data and access them within the PythonOperator.
D. Store sensitive data like thresholds and comparison values directly within the DAG code.

Answer: A,C

NEW QUESTION # 74
Which of the following best describes the benefit of partition pruning in Spark SQL?

A. It decreases the amount of data shuffled during join operations.
B. It dynamically adjusts partition sizes based on the query execution plan.
C. It selectively processes partitions that meet certain criteria, reducing 1/0.
D. It increases the number of partitions to maximize parallelism.

Answer: C

Explanation:
Partition pruning is a performance optimization in Spark SQL that allows Spark to read only a subset of partitions from a DataFrame or table that meet certain criteria specified in the query. This significantly reduces the amount of data read from disk and processed, thereby decreasing I/O and improving query performance. Partition pruning is particularly effective for queries with filters that can be applied to partitioned columns. Options A and B describe different aspects of performance optimization but are not directly related to partition pruning. Option D suggests an adaptive behavior not specifically tied to the concept of partition pruning.

NEW QUESTION # 75
When leveraging caching in Spark, which scenario illustrates the use of the MEMORY ONLY SER storage level most effectively?

A. Caching datasets that are frequently accessed and modified.
B. Caching datasets that require fast, sequential access without the need for serialization.
C. Caching large datasets that do not fit into memory when stored in deserialized form.
D. Caching small, static datasets used in lookup operations.

Answer: C

Explanation:
The MEMORY_ONLY_SER storage level caches data in memory but in serialized form, which is more space-efficient than storing data in deserialized form (MEMORY_ONLY). This makes MEMORY ONLY_SER most effective for caching large datasets that do not fit into memory in their deserialized form, as serialization reduces the size of the data at the cost of requiring deserialization upon access, which can add computational overhead.

NEW QUESTION # 76
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