RELIABLE CDP-3002 EXAM ANSWERS | LATEST CDP-3002 EXAM MATERIALS

Reliable CDP-3002 Exam Answers | Latest CDP-3002 Exam Materials

Reliable CDP-3002 Exam Answers | Latest CDP-3002 Exam Materials

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Tags: Reliable CDP-3002 Exam Answers, Latest CDP-3002 Exam Materials, CDP-3002 New Question, CDP-3002 Examcollection, CDP-3002 Test Papers

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Quiz 2025 Cloudera CDP-3002: CDP Data Engineer - Certification Exam – Professional Reliable Exam Answers

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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q106-Q111):

NEW QUESTION # 106
After running a PySpark job, you want to analyze the performance and identify potential bottlenecks. Which tool should you use for this purpose?

  • A. PySpark SQL CLI.
  • B. PySpark DataFrame API.
  • C. Hadoop YARN ResourceManager.
  • D. Spark Web I-Jl.

Answer: D

Explanation:
The Spark Web IJI is the primary tool for monitoring Spark jobs. It provides detailed information about the job execution, stages, tasks, and resource utilization, which is crucial for identifying performance bottlenecks and optimizing Spark jobs.


NEW QUESTION # 107
In Spark, what is the advantage of using the 'coalesce' method over the 'repartition' method when reducing the number of partitions in an RDD?

  • A. 'coalesce' can increase the number of partitions without shuffling.
  • B. 'coalesce' reduces the number of partitions without a full data shuffle, enhancing performance.
  • C. 'repartition' is incapable of reducing the number of partitions.
  • D. 'coalesce' triggers a full shuffle of the data, improving data distribution.

Answer: B

Explanation:
The 'coalesce' method is used to decrease the number of partitions in an RDD, and it does so without performing a full shuffle of the data. This makes it more efficient than 'repartition' for reducing the number of partitions because 'repartition' involves a full shuffle, which is more costly in terms of performance. 'coalesce' is particularly useful for optimization after filtering down a large dataset. Option A is incorrect as 'coalesce' is specifically designed for reducing partitions. Option B describes 'repartition', and Option D is factually incorrect.


NEW QUESTION # 108
You are working with a large dataset in PySpark and notice that certain operations are being executed repeatedly, leading to performance issues. Which of the following approaches should you adopt to optimize these operations?

  • A. Write the data to a CSV file and then read it back.
  • B. Increase the number of partitions using 'df.repartition()'.
  • C. Use the 'df.persist()' method.
  • D. Use the method.

Answer: C

Explanation:
The 'df.persist()' method is used in PySpark to store the intermediate computation of a DataFrame in memory, so when it is accessed again, it does not need to be recomputed, thus optimizing performance. 'df.cache()' is a synonym but 'persist()' offers more control over storage level.


NEW QUESTION # 109
What is the significance of "Sort Merge Join" appearing in an Explain Plan in Cloudera's SQL engines?

  • A. It suggests that the join operation is performed without sorting, leading to faster execution
  • B. It signifies that the join operation is performed by sorting and then merging two datasets, which can be efficient for large, sorted datasets
  • C. It indicates that the query will benefit from additional indexes
  • D. It is the least preferred join method due to its high CPU usage

Answer: B

Explanation:
A). "Sort Merge Join" involves sorting two datasets and then merging them based on the join condition. This method can be efficient for large datasets that are already sorted or partially sorted, as it leverages the order of data to reduce the computational overhead of the join operation.


NEW QUESTION # 110
You're building an Airflow DAG to automate data quality checks on the output of your ETL pipeline. The checks involve performing various data validation tasks like checking for missing values, ensuring data type consistency, and verifying data integrity based on specific business rules. How can you implement these checks within Airflow?

  • A. Leverage dedicated Airflow operators like BigQueryCheckOperator or S3KeySensor (these operators are specific to certain data sources and not generally applicable for all data quality checks).
  • B. All of the above
  • C. Use the PythonOperator to write custom Python scripts for each individual check and chain them together in the DAG.
  • D. Utilize Python libraries like Pandas or Spark for data manipulation and validation within the PythonOperator.

Answer: B


NEW QUESTION # 111
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