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Cloudera CDP Data Engineer - Certification Sample Questions:

1. Which of the following is TRUE about using Explain Plans for performance tuning?

A) Explain Plans always show the actual runtime of a query
B) They provide detailed network latency information
C) They can help identify operations that may become performance bottlenecks
D) They are only useful for queries that involve joins

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

3. You're experimenting with Iceberg table formats (vl and v2). Which of the following statements is true regarding their differences?

A) V2 tables are generally less performant than V1 tables due to added metadata overhead.
B) V2 introduces mandatory partitioning, while V1 allows for unpartitioned tables.
C) V2 supports new data types like UUIDs, which are unavailable in V1.
D) V2 uses manifest lists instead of manifest files for tracking data files.

4. You're building an Airflow DAG to extract data from a database table that is constantly updated. How can you implement incremental extraction to avoid processing the entire table each time the DAG runs?

A) Leverage the Previous execution operator to access the execution date of the previous DAG run and use it as a filter in the extraction query.
B) Implement a custom Python script to track the last processed record and use it to filter the data during subsequent runs.
C) Configure the database connection to only retrieve newly added data.
D) Use the File sensor to check for the presence of a new data file and trigger the DAG only when the file appears.

5. In Apache Airflow, which strategy allows for the dynamic generation of tasks within a DAG based on external data sources, such as a list of database tables?

A) Utilizing the @dag decorator with dynamic input parameters
B) Employing the TaskFlow API with dynamic task mapping
C) Using the Variable class to store and retrieve the list of tables
D) Implementing a PythonOperator that generates other tasks at runtime

Solutions: