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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. Which Spark configuration parameter should be increased to improve performance when dealing with large broadcast variables?

A) spark.driver.memory
B) spark.executor.memory
C) spark.default.parallelism
D) spark.broadcast.blockSize

2. You want to schedule your ETL pipeline to run daily at 5:00 AM. How can you configure the DAG's scheduling?

A) Use the cron expression "daily at 5:00".
B) Set the schedule_interval parameter to "0 5
C) Utilize Airflow triggers to initiate the DAG execution at 5:00 AM daily.
D) Define a custom Python function to schedule the DAG at the desired time.

3. You need to design your Airflow DAG for data quality checks to be scalable and manageable as the number of datasets and checks grows. How can you achieve this?

A) Leverage external configuration files (e.g., YAML or JSON) to define data quality checks and associated parameters.
B) Implement a modular design using sub-DAGs, where each sub-DAG encapsulates the data quality checks for a specific dataset.
C) Hardcode all data quality checks and data sources directly within the DAG code.
D) Utilize Airflow variables to store configuration details like data source paths and check thresholds.

4. Which feature of Apache Avro facilitates dynamic schema inference during data serialization and deserialization?

A) Embedded schema within data files
B) Manual schema declaration
C) Strong data typing
D) Schema versioning via external services

5. When leveraging caching in Spark, which scenario illustrates the use of the MEMORY ONLY SER storage level most effectively?

A) Caching datasets that require fast, sequential access without the need for serialization.
B) Caching datasets that are frequently accessed and modified.
C) Caching small, static datasets used in lookup operations.
D) Caching large datasets that do not fit into memory when stored in deserialized form.

Solutions: