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Cloudera CDP Data Engineer - Certification Sample Questions:

1. You are working with a large dataset consisting of multiple files. How can you efficiently load the data into Spark while considering efficient storage and processing?

A) Use spark.read.textFile("/path/to/directory/") to read all files at once
B) All of the above
C) Leverage partitioning techniques like spark.read.textFile("/path/to/directory").repartition(n)
D) Load each file individually using spark.read.textFile("/path/to/file")

2. You need to design a DAG that can be easily scaled horizontally to handle increasing workload. How can you achieve this goal?

A) Increase the number of worker processes in the Airflow web server configuration.
B) Leverage Airflow pools to manage resource allocation and limit concurrent task execution.
C) Implement custom logic within each task to handle parallel processing independently.
D) Utilize external scheduling tools like Cron to manage DAG execution independently of Airflow workers.

3. You need to design your Airflow DAG for data quality checks to be easily monitored and visualized. How can you achieve this?

A) All of the above
B) Implement custom logic to send detailed logs about the check results to external monitoring tools.
C) Utilize Airflow XCom to share the outcome of the checks (success/failurE. with downstream tasks for further processing.
D) Leverage Airflow's built-in metrics and monitoring features like the Airflow web UI to track check execution and results.

4. Your Spark application involves complex data transformations requiring multiple shuffles. How can you leverage Spark's in- memory caching mechanisms to improve performance?
A Use rdd.persist(StorageLevel.MEMORY_ONLY) for all intermediate RDDs

A) Tune Spark configuration parameters like spark.storage.memoryFraction
B) Implement custom caching logic using in-memory data structures
C) Leverage Spark SQL caching with materialized views

5. What command and parameters should be used to update an existing Spark job in the Cloudera Data Engineering (CDE. service to increase its executor memory using the CDE CLI?

A) cde job config -name my-spark-job -set spark.executor.memory=6g
B) cde spark submit -update -name my-spark-job -executor-memory 6G
C) cde job run -edit my-spark-job -conf spark.executor.memory=6g
D) cde job update -name my-spark-job --conf spark.executor.memory=6g

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