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Cloudera CDP Data Engineer - Certification Sample Questions:

1. What is the role of an Operator in an Apache Airflow DAG?
A To schedule DAG runs based on time or external triggers

A) To store metadata and logs for tasks and DAGs
B) To monitor the execution of DAGs and provide notifications
C) To represent a single task or a step within a DAG

2. When creating a partitioned table in Hive, what does the clause PARTITIONED BY specify?

A) The default file format for data storage
B) The column(s) used to divide the table into partitions
C) The replication factor for the HDFS data blocks
D) The compression algorithm used for data storage

3. You want to track changes to an Iceberg table over time for auditing purposes. Which combination of Iceberg features would best support this?

A) Snapshots and partition evolution
B) Snapshots and manifest lists
C) Hidden partitioning and Iceberg audit logs
D) Metadata tables and time travel

4. What does setting the Spark configuration parameter 'spark.sql.shuffle.partitions' impact?
A The default level of parallelism for joins and aggregations

A) The memory allocation for executor instances
B) The compression codec used for shuffle files
C) The serialization format of data

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

Solutions: