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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q97-Q102):

NEW QUESTION # 97
In Apache Airflow, what is the purpose of setting max_active_runs in a DAG's configuration?

A. To specify the maximum number of DAG runs that can be executed in parallel.
B. To determine the maximum number of DAG files that can be parsed at any given time.
C. To control the number of retries for a failed task.
D. To limit the number of task instances that can run concurrently within the DAG.

Answer: A

Explanation:
The max_active_runs setting in a DAG's configuration limits the number of DAG runs that can be executed concurrently. This is useful for controlling resource utilization and ensuring that the Airflow instance does not get overwhelmed by too many parallel executions of the same DAG.

NEW QUESTION # 98
What is a potential downside of overly granular partitioning in Hive tables?

A. Increased data security
B. Simplified data management
C. Reduced query performance due to metadata overhead
D. Automatic data deduplication

Answer: C

Explanation:
Overly granular partitioning in Hive tables can lead to a large number of partitions, which increases the metadata that Hive needs to manage. This can negatively impact query performance because Hive must load and process this extensive metadata before executing queries, potentially leading to slower response times.

NEW QUESTION # 99
You are designing a data pipeline that involves ingesting data from multiple sources, performing data transformations using Spark, and storing the results in a data lake. How would you leverage the Cloudera Data Engineering service to ensure efficient and fault-tolerant execution?

A. Design the pipeline with stages and steps, leveraging Spark operators for transformations and utilizing retries and error handling mechanisms.
B. Utilize separate Spark jobs for each data source and transformation step.
C. Develop a single Spark job containing all transformation logic.
D. Implement custom logic within the YAML configuration file to manage data flow and error handling.

Answer: A

NEW QUESTION # 100
What is the best practice for handling DAG dependencies in Apache Airflow when one DAG's output is another DAG's input?

A. Use the TriggerDagRunOperator to trigger one DAG from another upon completion.
B. Use the ExternalTaskSensor to wait for a task in another DAG to complete.
C. Directly call one DAG from another using the PythonOperator.
D. Manually trigger the dependent DAG after the first DAG completes.

Answer: B

Explanation:
The ExternalTaskSensor is designed to wait for a specific task in another DAG to complete before proceeding. This is a robust way to handle dependencies between DAGs, ensuring that the downstream DAG only starts its execution once the necessary output from the upstream DAG is ready, without tightly coupling the two DAGs' execution logic.

NEW QUESTION # 101
When monitoring a PySpark application in Kubernetes, you notice that Executor pods are frequently restarting. What is the most likely cause of this issue?

A. Insufficient memory allocation for Executor pods.
B. Too many tasks assigned to each Executor.
C. Dynamic resource allocation is disabled.
D. Executor pods are not configured to restart upon failure.

Answer: A

Explanation:
Frequent restarting of Executor pods in Kubernetes can be a sign of insufficient memory allocation. If Executors are allocated less memory than required for the tasks they are running, they may fail and restart frequently.

NEW QUESTION # 102
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