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Cloudera CDP Data Engineer - Certification Sample Questions:

1. You're working with a Spark application that processes sensitive dat
a. How can you ensure that persisted data remains secure even if accessed from unauthorized sources?

A) Implement custom access control mechanisms within your application
B) Encrypt the data before persisting it and decrypt it when needed
C) No additional security measures are needed, as Spark handles data security
D) Rely on Spark's lineage tracking to prevent unauthorized access

2. You're building an Airflow DAG to automate data quality checks on the output of your ETL pipeline. The checks involve performing various data validation tasks like checking for missing values, ensuring data type consistency, and verifying data integrity based on specific business rules. How can you implement these checks within Airflow?

A) Utilize Python libraries like Pandas or Spark for data manipulation and validation within the PythonOperator.
B) Use the PythonOperator to write custom Python scripts for each individual check and chain them together in the DAG.
C) Leverage dedicated Airflow operators like BigQueryCheckOperator or S3KeySensor (these operators are specific to certain data sources and not generally applicable for all data quality checks).
D) All of the above

3. Your Airflow DAG involves sending notifications upon successful completion of the entire pipeline. How can you achieve this functionality?

A) Use the Email Operator to send an email notification upon successful DAG run completion.
B) Utilize Airflow variables to store notification details and access them within the final task.
C) Configure the Airflow web UI to send alerts based on DAG run status.
D) Implement a custom notification script within the final task of the DAG.

4. You have deployed a Spark application on Kubernetes, which is experiencing intermittent failures. To improve fault tolerance, you decide to implement checkpointing. Which of the following is the best approach to add checkpointing in a PySpark application?

A) Implement checkpointing at the application level outside of Spark.
B)

C) Enable checkpointing in Kubernetes configuration files.
D)

5. You're working with a large dataset containing nested JSON structures. How can you efficiently process this data using Spark, ensuring data integrity and avoiding excessive parsing overhead?

A) Implement a custom parser for the specific JSON structure
B) Convert the entire dataset to a single string and process it line by line
C) Leverage Spark SQL's built-in JSON support with appropriate schema definition
D) Use generic string manipulation functions to extract data from JSON

Solutions: