Cloudera CDP-3002 real exam prep : CDP Data Engineer - Certification Exam

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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. A data engineer is deploying a Spark application on a Kubernetes cluster. To minimize downtime during updates, which Kubernetes deployment strategy should be considered?

A) Persistent Volumes
B) Rolling Updates
C) Node Affinity
D) Pod Autoscaling

2. You're working with a complex data pipeline involving Spark and Hive, and you need to monitor its performance and identify potential bottlenecks. Which tools and techniques can you employ for effective monitoring?

A) Utilize YARN resource manager and Spark/Hive metrics for detailed monitoring
B) Leverage Spark's web UI and Hive logs for basic information
C) Manually analyze Spark and Hive logs after job completion
D) Implement custom instrumentation code within your Spark application

3. Which of the following best describes the benefit of partition pruning in Spark SQL?

A) It decreases the amount of data shuffled during join operations.
B) It selectively processes partitions that meet certain criteria, reducing 1/0.
C) It increases the number of partitions to maximize parallelism.
D) It dynamically adjusts partition sizes based on the query execution plan.

4. You encounter an error message stating "Task timed out" while running your Airflow DAG. What are some potential causes and how can you troubleshoot them?

A) The DAG is running too frequently, overloading the system resources.
B) All of the above
C) The data processing task is taking longer than the configured timeout for the task.
D) The extraction task is encountering issues connecting to the source system.

5. When leveraging caching in Spark, which scenario illustrates the use of the MEMORY ONLY SER storage level most effectively?

A) Caching datasets that are frequently accessed and modified.
B) Caching datasets that require fast, sequential access without the need for serialization.
C) Caching small, static datasets used in lookup operations.
D) Caching large datasets that do not fit into memory when stored in deserialized form.

Solutions: