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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. In Spark, what is the advantage of using the 'coalesce' method over the 'repartition' method when reducing the number of partitions in an RDD?

A) 'coalesce' triggers a full shuffle of the data, improving data distribution.
B) 'repartition' is incapable of reducing the number of partitions.
C) 'coalesce' reduces the number of partitions without a full data shuffle, enhancing performance.
D) 'coalesce' can increase the number of partitions without shuffling.

2. When would it be advantageous to use both partitioning and bucketing on a Hive table?

A) When data needs to be stored in a single file for archival purposes
B) When data security is a primary concern
C) When dealing with large datasets that require efficient querying and data sampling
D) When managing small datasets to reduce complexity

3. Your project involves integrating Spark with a NoSQL database, MongoDB. You need to write a DataFrame 'df into a MongoDB collection named 'orders'. Which PySpark code snippet correctly achieves this?

A)

B)

C)

D)

4. What is the recommended approach in Apache Airflow for ensuring data quality checks are performed after data is loaded into multiple target systems, which might complete their loading processes at different times?

A) Implement multiple ExternalTaskSensor instances, each waiting for a specific loading task to complete.
B) Use a CrossDagDep operator to manage dependencies across multiple DAGs.
C) Utilize a BranchPythonOperator to dynamically route the workflow based on system availability.
D) Schedule the data quality checks at a fixed delay after the longest expected load time.

5. You're building an Airflow DAG to extract data from a database table that is constantly updated. How can you implement incremental extraction to avoid processing the entire table each time the DAG runs?

A) Leverage the Previous execution operator to access the execution date of the previous DAG run and use it as a filter in the extraction query.
B) Configure the database connection to only retrieve newly added data.
C) Use the File sensor to check for the presence of a new data file and trigger the DAG only when the file appears.
D) Implement a custom Python script to track the last processed record and use it to filter the data during subsequent runs.

Solutions: