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Cloudera CDP Data Engineer - Certification Sample Questions:

1. In Apache Airflow, how can you ensure that a data quality check task is only executed if a previous data ingestion task succeeded and the ingested data volume exceeds a certain threshold?

A) Utilize the ShortCircuitOperator with a custom Python function to check both conditions.
B) Configure the trigger_rule parameter to all_success and use a PythonOperator to verify the data volume.
C) Implement a PythonOperator with a pre_execute hook for the condition check.
D) Use a BranchPythonOperator with a condition that checks the success of the ingestion task and the data volume.

2. A PySpark application is facing performance issues due to uneven distribution of data across the nodes. Which approach would best help in resolving this issue?

A) Employ to redistribute data based on a key.
B) Use 'df.collect()'.
C) Apply 'df.coalesce(Y to reduce the number of partitions.
D) Convert the DataFrame to an RDD and then back to a DataFrame.

3. You're tasked with scheduling an ETL pipeline in Airflow that extracts data from a database, transforms it, and loads it into a data warehouse. How can you achieve this using Airflow operators?

A) Utilize Airflow's built-in ETL operators like BigQueryOperator or MySqlOperator (these operators are specific to certain data sources and not generally applicable for all ETL pipelines).
B) Use three separate Python operators for each stage (extract, transform, loaD. and chain them together in the DAG.
C) Leverage dedicated operators like PostgresHook for extraction, custom Python operators for transformation, and S3Hook for loading.
D) Implement a single custom Python script containing the entire ETL logic and call it within a BashOperator.

4. You have an Airflow DAG that includes tasks for data extraction, transformation, and loading. You notice that the transformation tasks are computationally intensive and are causing delays in the DAG's execution. To optimize performance, you decide to offload these tasks to a cloud-based service that can scale dynamically. Which approach ensures minimal changes to the DAG structure while integrating this optimization?

A) Use the ExternalTaskSensor to wait for the transformation to complete on the cloud service before proceeding.
B) Modify the transformation tasks to use the PythonOperator to make API calls to the cloud service, handling the transformation.
C) Implement the transformation tasks as DockerOperator tasks, with each task running in a containerized environment on the cloud service.
D) Replace the transformation tasks with HttpSensor tasks that trigger the cloud service and poll for completion.

5. Which component of the Cloudera Data Engineering service is responsible for managing and scheduling data pipelines?

A) Cloudera Navigator
B) Cloudera Scheduler
C) Cloudera Manager
D) CDH Management Console

Solutions: