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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. Your ETL pipeline involves complex data transformations that require libraries not readily available in the Airflow environment. How can you ensure these libraries are accessible during pipeline execution?

A) Configure Airflow to use a virtual environment with pre-installed libraries.
B) Package the libraries with your DAG code and reference them within the Python operators.
C) Install the required libraries directly into the Airflow environment.
D) Utilize system-wide library installations, assuming they are accessible to the Airflow user.

2. You are writing a PySpark application where you need to collect the final results from various Executors and present them to the user. Which aspect of the Spark Driver's role is primarily involved in this process?

A) Managing Kubernetes API interactions for Executor lifecycle.
B) Collecting final results from Executors and providing the output.
C) Constructing the logical plan from the user application.
D) Optimizing resource utilization within the Kubernetes cluster.

3. Your Spark application encounters performance issues when reading data from a large Hive table. What potential optimization techniques can you explore?

A) Use a different file format for the Hive table, like CSV, for faster parsing
B) Increase the number of Spark executors without further optimization
C) Implement custom data compression logic within Spark for improved storage efficiency
D) Leverage partition pruning to only read relevant data from the table

4. If you want to set a minimum and maximum number of Executor pods for a Spark application in Kubernetes, which pair of PySpark configuration settings would you use?

A) 'spark.executor.memory', 'spark.executor.memoryoverhead'
B) 'spark.dynamicAllocation.minExecutors', 'spark.dynamicAllocation.maxExecutors'
C) 'spark.executor.instances', 'spark.executor.cores'
D) 'spark.kubernetes.container.image', 'spark.kubernetes.executor.limit.cores'

5. For improving join performance, why is it recommended to filter data before joining tables in Apache Spark?

A) To enforce strict data typing across joined datasets
B) To increase the amount of data being shuffled
C) To reduce the volume of data processed during the join
D) To prepare data for broadcast joins irrespective of size

Solutions: