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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. In Apache Airflow, what is the purpose of setting max_active_runs in a DAG's configuration?

A) To determine the maximum number of DAG files that can be parsed at any given time.
B) To control the number of retries for a failed task.
C) To limit the number of task instances that can run concurrently within the DAG.
D) To specify the maximum number of DAG runs that can be executed in parallel.

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

3. A team is planning to use PySpark to read data from an Apache Cassandra database. Which of the following options correctly demonstrates how to load data from a Cassandra table named in the keyspace 'sales'?

A)

B)

C)

D)

4. You need to handle potential errors and retries within your Airflow ETL pipeline. How can you achieve this functionality?

A) All of the above
B) Implement exception handling within your custom Python operators and define retry behavior manually.
C) Configure the DAG to retry failed tasks automatically with a set number of retries.
D) Use the BranchPythonOperator to check for specific error conditions and branch the DAG execution accordingly.

5. How does dynamic schema inference impact the processing of streaming data in real-time analytics platforms?

A) It enables the platform to adapt to changes in data format without downtime.
B) It significantly increases the latency of data processing operations.
C) It decreases the overall throughput of the analytics platform.
D) It requires streaming data to be stored before its schema can be inferred.

Solutions: