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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q91-Q96):

NEW QUESTION # 91
You're working with a Spark application that processes sensitive dat
a. How can you ensure that persisted data remains secure even if accessed from unauthorized sources?

A. Rely on Spark's lineage tracking to prevent unauthorized access
B. No additional security measures are needed, as Spark handles data security
C. Implement custom access control mechanisms within your application
D. Encrypt the data before persisting it and decrypt it when needed

Answer: D

Explanation:
Spark does not inherently provide encryption for persisted data A. Lineage tracking C helps track data flow but doesn't prevent unauthorized access. Implementing custom access control D can be complex. Encrypting data before persistence B ensures only authorized users with the decryption key can access the sensitive information.

NEW QUESTION # 92
When using Apache Airflow to schedule quality checks, which strategy helps ensure that checks are only run on the most recent data partition?
A Use the LatestOnlyOperator to skip tasks that are not the latest in a series of executions.

A. Apply the depends_on_past=True parameter to the quality check task.
B. Utilize the execution_date in your SQL query to target the latest partition.
C. Implement a custom PythonOperator that queries the database for the latest partition.

Answer: B

Explanation:
The LatestOnlyOperator in Apache Airflow allows you to ensure that certain tasks are only executed in the most recent DAG run. This is useful for quality checks that should only be applied to the latest data partition, avoiding unnecessary checks on historical data.

NEW QUESTION # 93
How does partition pruning contribute to query performance in the Cloudera Data Platform?

A. By removing unnecessary data replication
B. By selecting only relevant partitions for query execution
C. By increasing the number of partitions
D. By encrypting data at rest

Answer: B

Explanation:
Partition pruning enhances query performance by allowing the engine to skip over partitions of the data that are not relevant to the query, thereby reducing the amount of data scanned and processed.

NEW QUESTION # 94
You're working with a large Spark DataFrame and need to perform an aggregation operation (e.g., can you improve the performance of the aggregation? SUM, COUNT). How

A. Use Spark SQL's built-in aggregation functions like SUM and COUNT
B. Increase the number of Spark executors without further optimization
C. Leverage partitioning techniques to group relevant data together
D. All of the above

Answer: D

Explanation:
While increasing executors A might help, it's not the sole solution. Using built-in functions B is essential. Partitioning the DataFrame C based on the column involved in the aggregation reduces data shuffling across the network, significantly improving performance, especially for large datasets.

NEW QUESTION # 95
You need to design a DAG that can be easily scaled horizontally to handle increasing workload. How can you achieve this goal?

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

Answer: A,D

NEW QUESTION # 96
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