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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q170-Q175):

NEW QUESTION # 170
When tuning Spark applications, why is it important to adjust the spark.executor.cores configuration?

A. To specify the number of tasks that can be run in parallel on a single executor
B. To determine the number of partitions created during shuffle operations
C. To directly control the amount of memory available to each executor
D. To limit the maximum size of Spark's internal data structures

Answer: A

Explanation:
The spark.executor.cores configuration specifies the number of cores to use on each executor. By adjusting this value, you control the number of tasks that can run in parallel on a single executor, directly impacting the application's concurrency and overall performance. Option B is incorrect as this configuration does not control data structure sizes. Option C is misleading; while more cores may imply more parallel tasks and thus potentially more memory usage, it does not directly set the memory allocation. Option D is incorrect because the number of partitions created during shuffle operations is controlled by configurations like spark.sql.shuffle.partitions, not by the number of executor cores.

NEW QUESTION # 171
Your Spark application involves a complex data pipeline with multiple dependent stages. How can you configure Spark to handle failures gracefully and ensure data consistency across the pipeline?

A. Implement custom error handling logic within each stage
B. Retry failed stages indefinitely until successful completion
C. Leverage checkpointing and lineage tracking for selective failure recovery
D. Use Spark's built-in fault tolerance mechanisms with automatic retries

Answer: C

Explanation:
Option A can lead to cascading failures, while custom error handling B might be complex. Relying solely on automatic retries D might not be sufficient for complex pipelines. Checkpointing allows saving intermediate data periodically, enabling recovery from failures by reprocessing only affected stages, improving efficiency and data consistency.

NEW QUESTION # 172
As a data engineer, you are working with PySpark to analyze data stored in an HDFS cluster. You need to read a CSV file into a Spark DataFrame. Which of the following code snippets correctly reads the data from HDFS into a DataFrame?

Answer: D

Explanation:
The correct way to read a CSV file from HDFS into a Spark DataFrame in PySpark is using 'spark.read.format("csv").load("hdfs:///path/to/data.csv")*. This method specifies the format as CSV and loads the file from the specified HDFS path.

NEW QUESTION # 173
How can you implement a data quality check in Apache Airflow that verifies the row count of a table does not decrease from the previous DAG run?

A. Utilize the PreviousDagRunSensor with a custom Python function for comparison.
B. Implement a custom SqlSensor that checks the row count against a stored value in XComs.
C. Store the row count from the previous run in Airflow Variables and compare it using a Pythonoperator.
D. Use the BranchPythonOperator to branch the workflow based on the row count comparison logic.

Answer: C

Explanation:
By storing the row count from the previous DAG run in Airflow Variables, you can then use a Pythonoperator to retrieve this value and compare it against the current row count. This method allows for a flexible and dynamic approach to monitoring table row counts over time, ensuring data consistency and alerting to potential data loss or anomalies.

NEW QUESTION # 174
You're working with a large dataset that needs to be partitioned and processed in chunks to improve efficiency. How can you achieve this using Airflow operators?

A. Leverage the File transform operator to partition the data based on specific criteria within the operator itself.
B. Implement a custom Python script to handle partitioning and then use the BashOperator to execute the script within the DAG.
C. Configure the source system to provide the data pre-partitioned for efficient processing.
D. Use the Split Operator to divide the data into smaller subsets and chain them with downstream processing tasks.

Answer: D

Explanation:
The Split Operator is specifically designed to split data into smaller chunks and trigger downstream tasks for each partition, making it ideal for handling large datasets efficiently.

NEW QUESTION # 175
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Sean Hall Sean Hall

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