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Cloudera CDP Data Engineer - Certification Sample Questions:

1. You are working on a project that involves processing large datasets stored in HDFS. You need to read a CSV file into a DataFrame using PySpark. Which of the following code snippets correctly achieves this?

A)

B)

C)

D)

2. If a Spark Driver pod in Kubernetes is reaching its CPU limit and experiencing performance issues, what is the most appropriate first action?

A) Increase the CPU requests in the pod's YAML configuration.
B) Restart the Kubernetes cluster.
C) Decrease the memory limits in the pod's YAML configuration.
D) Increase the CPU limits in the pod's YAML configuration.

3. Which of the following is a best practice for organizing tasks within a DAG in Apache Airflow?

A) Dynamically generate tasks at runtime to avoid defining them explicitly in the DAG.
B) Use a single Pythonoperator to execute all tasks as functions for efficiency.
C) Place all tasks directly in the root DAG to simplify monitoring and execution.
D) Group tasks with similar functionalities using SubDAGs for better readability and maintainability.

4. You notice a significant performance overhead when persisting a large RDD to disk. What potential factors could contribute to this, and how can you mitigate them?

A) Using HDFS as the storage location instead of local disks
B) All of the above
C) Insufficient disk space on the cluster nodes
D) Inefficient serialization format for the RDD data

5. What is the impact of caching intermediate data in Spark on iterative algorithms' performance?

A) It improves performance by reducing the need to recompute data in each iteration.
B) It decreases fault tolerance by storing data in volatile storage.
C) It significantly increases the execution time of each iteration.
D) It has no impact on performance but increases storage requirements.

Solutions: