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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. 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 increases the number of partitions to maximize parallelism.
C) It dynamically adjusts partition sizes based on the query execution plan.
D) It selectively processes partitions that meet certain criteria, reducing 1/0.

2. You're debugging a slow-running Spark job writing a large Iceberg table. Which optimization techniques could improve performance? (Choose three.

A) Filtering data as early as possible in the Spark transformation pipeline
B) Converting the DataFrame to RDD for Iceberg writes
C) Repartitioning the DataFrame before writing to Iceberg
D) Using the Z-Order clustering option in Iceberg
E) Disabling Spark's adaptive query execution

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

A) Hadoop YARN ResourceManager UI
B) Spark History Server
C) spark Web UI
D) Apache Ambari

4. You need to design a DAG that can be easily monitored and visualized for performance insights. How can you achieve this?

A) Utilize Airflow's built-in metrics and monitoring features like the Airflow web UI to track DAG execution and task performance.
B) Implement custom logic within each task to send detailed performance metrics to external monitoring tools.
C) Implement alerts and notifications within the DAG to trigger upon specific events or performance thresholds.
D) All of the above

5. You're working with a large dataset stored in multiple Parquet files across different HDFS directories. How can you efficiently load and process this data using Spark, ensuring data locality and minimizing shuffle operations?

A) Implement a custom function to read each Parquet file individually
B) Use spark.read.parquet("/path/to/data/") with recursive directory listing
C) Leverage Spark SQL catalogs and partition discovery
D) Directly load all files using spark.read.parquet("/path/to/data/")

Solutions: