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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
Which strategy is most effective for managing schema evolution in a big data application that relies on schema inference?

A. Completely disabling schema inference and relying on static schemas
B. Manually updating the inferred schema for each data ingestion
C. Using a single version of the schema for all data, regardless of changes
D. Integrating schema registry services that track and manage schema versions

Answer: D

Explanation:
Integrating schema registry services to track and manage schema versions is the most effective strategy for managing schema evolution in applications that rely on schema inference. Schema registries provide a centralized repository for schemas, enabling applications to handle changes in data structure over time gracefully. This approach ensures compatibility across different data versions and simplifies schema management, particularly in systems that must adapt to evolving data formats without disrupting ongoing data processing.

NEW QUESTION # 18
A data analyst is performing a join operation in PySpark between a large DataFrame Clarge_df) and a much smaller DataFrame ('small_df). The process is very slow. What can the analyst do to optimize this join operation?

A. Apply before the join.
B. Increase the number of partitions in 'small_df'.
C. Utilize the 'broadcast' function in the join operation.
D. Use "key").

Answer: C

Explanation:
The 'broadcast' function is used in PySpark to optimize join operations where one DataFrame is significantly smaller than the other. It broadcasts the smaller DataFrame across all nodes so that it is available in memory, reducing the data shuffling over the network.

NEW QUESTION # 19
Your Iceberg table experiences frequent schema evolution, with new columns being added regularly. Which of the following might you need to adjust to maintain optimal performance?

A. Kubernetes resource requests for Spark executors
B. Spark shuffle partitions (spark.sql.shuffle.partitions)
C. Iceberg metadata table pruning frequency
D. The Iceberg table's write format (vl or v2)

Answer: C

Explanation:
Frequent schema changes generate many metadata files. Iceberg's metadata tables may need more frequent pruning (expire_snapshots) to avoid performance issues during reads.

NEW QUESTION # 20
You're building a large and complex ETL pipeline with numerous tasks and dependencies. What are some best practices to ensure its maintainability and understandability?

A. All of the above
B. Use clear and descriptive names for tasks, operators, variables, and DAG comments throughout the code.
C. Break down the pipeline into smaller, modular sub-DAGs with well-defined functionalities.
D. Implement extensive logging within each task to capture detailed execution information.

Answer: A

Explanation:
All options listed in D are crucial for maintaining a large and complex Airflow ETL pipeline. Using descriptive naming, modularizing with sub-DAGs, and adding relevant logging practices enhance clarity and facilitate troubleshooting in the long run.

NEW QUESTION # 21
When deploying a Spark application in Kubernetes, what is the purpose of specifying the '-conf spark.kubernetes.driver.pod.name=spark-driver-pod' in the 'spark-submit' command?

A. To specify the name of the Spark Driver pod.
B. To define the Docker image for the Driver pod.
C. To assign a specific node for the Driver pod.
D. To name the Executor pods.

Answer: A

Explanation:
The configuration 'spark.kubernetes.driver.pod.name' in the 'spark-submit' command is used to specify the name of the Kubernetes pod that will run the Spark Driver.

NEW QUESTION # 22
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