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

2. When configuring a Hive table with bucketing, how does the choice of the bucketing column(s) influence query performance?

A) Selecting highly unique or skewed columns for bucketing can lead to uneven data distribution and potential performance bottlenecks.
B) The choice of bucketing column(s) has no impact on performance as long as the number of buckets is optimally chosen.
C) Bucketing on columns that are frequently updated optimizes data modification operations and query performance.
D) The bucketing column(s) should be chosen randomly to ensure a uniform distribution of data across buckets.

3. How does Spark handle data shuffling during distributed processing?

A) By transferring only required data between executors
B) Spark doesn't perform data shuffling
C) By storing all data on a single node
D) By broadcasting all data to each executor

4. Due to regulatory requirements, you need to permanently delete specific sensitive records from an Iceberg table. Which of the following techniques would be most appropriate?

A) Use Iceberg's EXPIRE SNAPSHOTS procedure to remove snapshots containing the sensitive data.
B) Implement a custom process, leveraging Iceberg's row-level updates to delete the sensitive records and then using file-level operations to physically remove the data.
C) Modify the data files directly using low-level tools to overwrite the sensitive data.
D) Issue a standard Iceberg DELETE query, as deleted data will automatically be expunged from the table.

5. In a CI/CD pipeline, what is a key consideration when integrating Cloudera Data Engineering (CDE. service API calls for deploying Spark jobs, specifically regarding security?

A) Incorporating API token rotation and secure secret management practices
B) Using generic Spark Docker images for all jobs to avoid API customization
C) Disabling TLS for API endpoints to simplify integration
D) Hardcoding API keys in the pipeline scripts

Solutions: