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Cloudera CDP Data Engineer - Certification Sample Questions:

1. You're working with a real-time streaming application using Spark Streaming. How can you ensure that your application gracefully handles late-arriving data and maintains data consistency?

A) Recompute the entire stream from scratch for each late record
B) Ignore late-arriving data altogether
C) Implement micro-batching with windowing and watermarking techniques
D) Use Spark's checkpointing functionality to recover from failures

2. What is a primary consideration when deciding to cache data in a distributed computing environment like Apache Spark?

A) Caching every dataset regardless of its access frequency
B) The trade-off between memory usage and computational efficiency
C) Ensuring data is encrypted before caching
D) Using disk storage for all cached data to improve fault tolerance

3. What is the primary purpose of using Cloudera's API in a data engineering context?

A) To increase the time it takes to deploy applications.
B) To bypass security configurations for rapid development.
C) To manually configure and start cluster services.
D) To automate the provisioning, configuration, and scaling of clusters.

4. Why are partitioned tables beneficial in Hive for large datasets?

A) They ensure that all data is stored in a single file, simplifying access.
B) They automatically encrypt data based on the partition key.
C) They allow for faster query processing by scanning only relevant partitions.
D) They decrease the security of the data.

5. An Airflow DAG designed to run a sequence of data validation checks generates a dynamic number of validation tasks based on the incoming data's characteristics. Each validation task must complete successfully before a final data processing task can begin. Which Airflow feature is most suitable for implementing this pattern?

A) SubDAGs
B) A BranchPythonOperator with a follow-up Join task
C) The Dynamic Task Mapping feature
D) The TriggerDagRunOperator

Solutions: