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Cloudera CDP Data Engineer - Certification Sample Questions:

1. You're tasked with creating a DAG in Airflow that orchestrates a complex data processing workflow. What are some key considerations for designing an effective DAG?

A) Implement extensive logging within each task for detailed information, even if it slows down execution.
B) Break down the workflow into smaller, modular tasks with clear dependencies.
C) Configure the DAG to run continuously without any specific scheduling or triggering mechanism.
D) Use a single DAG for the entire workflow, regardless of its complexity.

2. How can you use Apache Airflow to ensure a data quality check stops the workflow if it fails, without failing subsequent tasks that are not dependent on the data quality check?

A) Set trigger_rule='all_done' on subsequent tasks.
B) Use the TaskGroup with trigger_rule='one_failed' for the data quality check task.
C) Use the ShortCircuitOperator with a condition that returns False if the check fails.
D) Use the BranchPythonOperator to split the workflow conditionally.

3. Which feature in Apache Airflow allows you to retry a data quality check task if it fails initially due to transient issues?

A) SLAs
B) Branching
C) catchup
D) retries parameter in the task definition

4. Which of the following is a critical consideration when deciding between using a sort merge join and a shuffle hash join in a distributed data processing system like Spark?

A) The relative size of the datasets and the available memory on each executor
B) The network latency between nodes in the cluster
C) The availability of secondary indexes on the join keys
D) The version of the Spark cluster being used

5. In Apache Airflow, how does the "catchup" parameter in a DAG definition influence the scheduling of DAG runs when backfilling data?

A) It limits the number of backfilled DAG runs to the most recent execution interval only.
B) It disables backfilling entirely, ensuring that only future DAG runs are executed.
C) It enables backfilling, causing Airflow to schedule DAG runs for all intervals between the DAG's start_date and the current date.
D) It forces Airflow to ignore the start_date and schedule the DAG run immediately.

Solutions: