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Cloudera CDP Data Engineer - Certification Sample Questions:

1. Your Iceberg table experiences frequent schema evolution, with new columns being added regularly. Which of the following might you need to adjust to maintain optimal performance?

A) Kubernetes resource requests for Spark executors
B) Iceberg metadata table pruning frequency
C) The Iceberg table's write format (vl or v2)
D) Spark shuffle partitions (spark.sql.shuffle.partitions)

2. Which Apache Airflow feature should be used to parameterize a DAG run for running data quality checks on different datasets dynamically?

A) Custom Execution Context
B) Jinja templating
C) XComs
D) Airflow Variables

3. In a Kubernetes environment, why is it beneficial to run the Spark Driver in its own pod?

A) To automatically scale the Driver based on workload.
B) To isolate the Driver from Executor pods for security reasons.
C) To use a different programming language for the Driver.
D) To reduce the cost of cloud resources.

4. 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) Implement custom instrumentation code within your Spark application
C) Leverage Spark's web UI and Hive logs for basic information
D) Manually analyze Spark and Hive logs after job completion

5. When writing a DataFrame to a CSV file, what potential issues should you consider and how can you address them?

A) Choose an appropriate compression format like Gzip to reduce file size
B) No specific issues need to be considered, as CSV is a simple format
C) All of the above
D) Ensure proper handling of special characters and delimiters to avoid data corruption

Solutions: