Cloudera CDP-3002 Test Passed : CDP Data Engineer - Certification Exam

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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. How can you utilize Spark SQL for complex data analysis involving joins and aggregations on large datasets?

A) By using Spark ML libraries for machine learning tasks on data.
B) By implementing custom logic in RDDs for data manipulation.
C) Both A and B.
D) By directly writing SQL queries against distributed data in Spark.

2. You are processing a large dataset using Spark and need to ensure that the results are available for subsequent stages without recomputing. Which approach achieves this efficiently?

A) Store the data in a temporary table using Spark SQL
B) Leverage Spark's automatic checkpointing mechanism
C) Use rdd.persist() with the appropriate storage level based on your needs
D) Implement custom logic to save the data to HDFS between stages

3. Your Airflow DAG involves tasks that require access to confidential data like passwords or API keys. How can you securely manage and access these credentials within the DAG?

A) Utilize environment variables to store the credentials and access them within the tasks.
B) All of the above
C) Implement a custom secret management solution outside of Airflow.
D) Store the credentials directly within the DAG code, assuming limited access to the codebase.

4. Consider a PySpark application that calculates the count of rows in a CSV file. The main application file is 'count_rows.py', and it uses a custom library 'data_utils.py'. After packaging, which 'spark-submit' command correctly submits this job including the custom library?

A) 'spark-submit -py-files data utils.py count_rows.py'
B) 'spark-submit --archives data_utils.py count_rows.py'
C) 'spark-submit --files data_utils.py count_rows.py'
D) 'spark-submit --jars data utils.py count_rows.py'

5. What is the recommended way to handle dependencies between data quality checks in Apache Airflow to ensure that checks are performed in a specific sequence?

A) Use the SequentialExecutor for the Airflow environment.
B) Use the depends_on_past parameter in each data quality check task.
C) Implement each data quality check as a separate DAG.
D) Explicitly set task dependencies using the set_upstream or set_downstream methods.

Solutions: