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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. You're tasked with optimizing the performance of your ETL pipeline in Airflow. What are some potential strategies to consider?

A) All of the above
B) Leverage partitioning and bucketing techniques in the data warehouse to improve query performance.
C) Utilize efficient data structures and algorithms within your custom Python operators for data transformation.
D) Increase the number of worker processes in Airflow to parallelize task execution.

2. What role do user-defined functions (UDFs) play in schema inference within SQL-based data processing engines?

A) They eliminate the need for schema inference by pre-defining data schemas.
B) They primarily increase the storage requirements for schema metadata.
C) UDFs restrict the ability of the engine to infer schemas automatically.
D) UDFs can enhance schema inference by providing custom logic for data interpretation.

3. You're writing a Spark application that processes streaming data in real-time. How can you create DataFrames from streaming data sources?

A) All of the above
B) Leverage Spark Streaming's functions like createDataFrame with appropriate parameters
C) Utilize Spark SQL's streaming capabilities with the FROM clause
D) Use the same methods as for batch processing, reading data from a file

4. What mechanism does Airflow provide to retry failed tasks?

A) The retry_delay and retries parameters in task definitions
B) Airflow Scheduler's automatic rerun feature
C) Manual intervention and rerun via the Airflow Webserver
D) The on failure callback function in DAG definitions

5. Your Airflow DAG includes data quality checks that involve comparing data against predefined thresholds or reference datasets. How can you handle potential failures during these checks and ensure the pipeline doesn't proceed with unreliable data?

A) Implement custom logic within the PythonOperator to raise exceptions upon failed checks and trigger downstream tasks to handle the error.
B) All of the above
C) Use the BranchPythonOperator to check the success of the data quality checks and conditionally branch the DAG execution based on the outcome.
D) Configure the DAG to automatically retry failed tasks associated with the data quality checks.

Solutions: