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Cloudera CDP Data Engineer - Certification Sample Questions:

1. What is the primary purpose of the Airflow Scheduler in Apache Airflow?

A) To monitor the state of tasks and trigger them based on their dependencies
B) To provide a user interface for monitoring and managing DAGs
C) To execute the code for each task in a DAG
D) To distribute tasks across workers for execution

2. As a data engineer, you are working with PySpark to analyze data stored in an HDFS cluster. You need to read a CSV file into a Spark DataFrame. Which of the following code snippets correctly reads the data from HDFS into a DataFrame?

A)

B)

C)

D)

3. After running a PySpark job, you want to analyze the performance and identify potential bottlenecks. Which tool should you use for this purpose?

A) PySpark DataFrame API.
B) Spark Web I-Jl.
C) Hadoop YARN ResourceManager.
D) PySpark SQL CLI.

4. Your Airflow DAG includes tasks that can potentially fail due to various reasons. How can you handle such failures and ensure the overall workflow continues as intended?

A) Utilize Airflow XCom to share information about failed tasks with downstream tasks for alternative processing.
B) Configure the DAG to automatically retry failed tasks a specific number of times.
C) All of the above
D) Implement custom logic within each task to handle potential errors and retry failed tasks manually.

5. You need to design your Airflow DAG for data quality checks to be easily monitored and visualized. How can you achieve this?

A) Leverage Airflow's built-in metrics and monitoring features like the Airflow web UI to track check execution and results.
B) Utilize Airflow XCom to share the outcome of the checks (success/failurE. with downstream tasks for further processing.
C) Implement custom logic to send detailed logs about the check results to external monitoring tools.
D) All of the above

Solutions: