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Cloudera CDP Data Engineer - Certification Sample Questions:

1. In a PySpark application, you're writing a function that reads a CSV file and shows the first few rows. Which of the following code snippets correctly accomplishes this task?

A) Option D
B) Option B
C) Option A
D) Option C

2. You need to design an Airflow DAG that waits for a specific file to become available before proceeding with the downstream tasks. How can you achieve this dependency?

A) Schedule the DAG to run periodically, hoping the file becomes available eventually.
B) Implement a custom loop within a Python operator to continuously check for the file until it appears.
C) Use the File sensor operator to check for the file's existence and trigger downstream tasks upon its arrival.
D) Configure the source system to notify Airflow when the file is ready for processing.

3. An Airflow DAG is designed to ingest data from multiple sources, transform it, and load it into a data warehouse. The transformation step is resource-intensive and should not run during peak hours (9 AM to 5 PM). How can you configure the DAG to meet this requirement?

A) Use the time_sensor operator to delay the transformation task until off-peak hours.
B) Configure the DAG's schedule interval and use the TimeDelta sensor for precise timing.
C) Set the max_active_runs parameter to limit executions during peak hours.
D) Utilize the BranchPythonOperator to dynamically skip the transformation task during peak hours.

4. You notice a significant performance overhead when persisting a large RDD to disk. What potential factors could contribute to this, and how can you mitigate them?

A) Inefficient serialization format for the RDD data
B) Insufficient disk space on the cluster nodes
C) All of the above
D) Using HDFS as the storage location instead of local disks

5. When deploying a Spark application in Kubernetes, what is the purpose of specifying the '-conf spark.kubernetes.driver.pod.name=spark-driver-pod' in the 'spark-submit' command?

A) To assign a specific node for the Driver pod.
B) To specify the name of the Spark Driver pod.
C) To name the Executor pods.
D) To define the Docker image for the Driver pod.

Solutions: