Cloudera CDP-3002 : CDP Data Engineer - Certification Exam

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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. Which technique in the Optimization Framework is primarily used to improve query performance by reducing the number of rows to be scanned in a table?

A) Indexing
B) Predicate pushdown
C) Data compaction
D) Data replication

2. 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) Use a CrossDagDep operator to manage dependencies across multiple DAGs.
B) Implement multiple ExternalTaskSensor instances, each waiting for a specific loading task to complete.
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.

3. What is the correct way to define a start date for a DAG in Apache Airflow, ensuring that the DAG does not trigger immediately upon deployment?

A) Use ) to automatically set the start date to one day before the current date.
B) Set the start date to a future date using the datetime module.
C) Leave the start date undefined.
D) Use datetime.now() as the start date.

4. You're integrating data quality checks into a complex ETL pipeline with numerous tasks and dependencies. How can you ensure the checks are executed in the correct order and don't interfere with other pipeline tasks?

A) Utilize Airflow upstream/downstream dependencies to define the execution order between check tasks and other pipeline tasks.
B) Schedule the data quality checks as a separate DAG and trigger it after the ETL pipeline completes.
C) Implement a custom script to manage the execution of the data quality checks independently.
D) Run all tasks (ETL and checks) concurrently, assuming they are independent.

5. How does Hive handle bucketing when the data inserted into a bucketed table does not evenly distribute across the buckets?

A) Hive dynamically adjusts the number of buckets to evenly distribute the data.
B) Hive rejects the data insertion and raises an error.
C) Hive automatically rebalances the data across buckets using a round-robin distribution.
D) Hive distributes the data based on the hash value of the bucketing column, potentially leading to skewed buckets.

Solutions: