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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

Question 1

You're facing slow performance in your Spark application due to frequent shuffles. What are some strategies you can employ to optimize shuffle operations and improve performance?

A. Reduce the number of partitions in your RDDs
B. Increase the number of executors in your cluster
C. Use a broadcast variable for small, frequently accessed data
D. All of the above

Question 2

For a Hive table that is both partitioned and bucketed, what considerations must be taken into account to optimize a join query involving this table?

A. Both the partitioning and bucketing columns should align with the join columns where possible to maximize the efficiency of data retrieval.
B. The join should exclusively rely on the partitioned columns, ignoring the bucketed columns for optimal performance.
C. Ensuring the join columns are neither partitioned nor bucketed as it may lead to increased complexity.
D. Bucketing considerations are irrelevant in the context of join queries, with partitioning being the sole factor impacting performance.

Question 3

You're deploying a Spark application in a Kubernetes cluster. Which YAML specification would correctly define a SparkApplication in cluster mode using Scala and the Spark Pi example?

A. Option A
B. Option D
C. Option B
D. Option C

Question 4

Which tool or API is primarily used for monitoring and inspecting the performance of Spark applications in real-time?
A Spark History Server

A. Apache Ambari
B. Hadoop YARN ResourceManager UI
C. Spark Web UI

Question 5

You're working with a team of data engineers who collaborate on developing and maintaining Airflow DAGs. How can you ensure version control and maintain a consistent development workflow?

A. Grant full access to the Airflow web Ul to all team members for code modifications, regardless of their roles.
B. Store all DAG code in a shared folder on a local file system, relying on manual coordination between team members.
C. Implement a custom workflow management system outside of Airflow for managing DAG development.
D. Utilize a version control system like Git to track changes in the DAG code and enable collaborative development.

Solutions: