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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. 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) 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).
B) Utilize Python libraries like Pandas or Spark for data manipulation and validation within the PythonOperator.
C) Use the PythonOperator to write custom Python scripts for each individual check and chain them together in the DAG.
D) All of the above

2. Which tool or API is primarily used for monitoring and inspecting the performance of Spark applications in real-time?
A Spark History Server

A) Spark Web UI
B) Hadoop YARN ResourceManager UI
C) Apache Ambari

3. In the context of data quality checks with Apache Airflow, what is the primary purpose of using the EmailOperator?

A) To notify team members of failed data quality checks
B) To execute quality checks on email data
C) To retry failed tasks via email commands
D) To collect data from email attachments for quality checks

4. While working with Spark SQL, you encounter an error message stating "Unable to resolve table 'table_name"'. What could be the potential cause of this error?

A) The table schema is not compatible with Spark SQL
B) The Spark application doesn't have sufficient permissions to access the table
C) The table doesn't exist in the Hive metastore
D) All of the above

5. You're working with a CSV file containing missing dat
a. How can you efficiently handle missing values in a Spark DataFrame created from this file?

A) Convert the DataFrame to a different format that doesn't support missing values
B) Ignore the rows with missing values during DataFrame creation
C) Use the fillna() function with an appropriate replacement value
D) Implement custom logic to identify and handle missing values individually

Solutions: