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Cloudera CDP Data Engineer - Certification Sample Questions:

1. In Apache Spark, which of the following is the most effective strategy for minimizing data shuffling across nodes in a cluster?

A) Filtering data after a wide transformation
B) Increasing the number of partitions
C) Using broadcast variables for small data
D) Decreasing the number of partitions

2. You're deploying your Airflow ETL pipelines to a production environment. What are some best practices to ensure reliability and scalability?

A) Utilize version control for your DAG code and configuration files to track changes and facilitate rollbacks.
B) Implement robust error handling and retry mechanisms within your DAGs.
C) Configure Airflow to run with high resource limits to handle unexpected spikes in workload.
D) All of the above

3. You're integrating data quality checks into a complex ETL pipeline with numerous tasks and dependencies. How can you ensure the checks are executed in the correct order and don't interfere with other pipeline tasks?

A) Run all tasks (ETL and checks) concurrently, assuming they are independent.
B) Schedule the data quality checks as a separate DAG and trigger it after the ETL pipeline completes.
C) Implement a custom script to manage the execution of the data quality checks independently.
D) Utilize Airflow upstream/downstream dependencies to define the execution order between check tasks and other pipeline tasks.

4. 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 forces Airflow to ignore the start_date and schedule the DAG run immediately.
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 disables backfilling entirely, ensuring that only future DAG runs are executed.

5. What is the role of a Spark driver in a distributed processing job?

A) Coordinates tasks across the cluster
B) Performs computations on individual data partitions
C) Manages communication between executors and workers
D) Stores and processes intermediate data

Solutions: