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Cloudera CDP Data Engineer - Certification Sample Questions:

1. A colleague proposes using Iceberg's optimistic locking feature for a high-concurrency scenario where multiple jobs frequently modify the same table. What potential issues should you raise?

A) Optimistic locking in Iceberg introduces a significant performance overhead.
B) Iceberg's optimistic locking isn't suitable for distributed environments like CDP.
C) Optimistic locking might result in many failed transactions and retries, potentially leading to job instability.
D) Iceberg's optimistic locking requires manual conflict resolution.

2. 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 triggers a full shuffle of the data, improving data distribution.
C) coalesce reduces the number of partitions without a full data shuffle, enhancing performance.
D) coalesce can increase the number of partitions without shuffling.

3. You're working with a large dataset that needs to be partitioned and processed in chunks to improve efficiency. How can you achieve this using Airflow operators?

A) Use the Split Operator to divide the data into smaller subsets and chain them with downstream processing tasks.
B) Configure the source system to provide the data pre-partitioned for efficient processing.
C) Implement a custom Python script to handle partitioning and then use the BashOperator to execute the script within the DAG.
D) Leverage the File transform operator to partition the data based on specific criteria within the operator itself.

4. You're facing slow performance in your Spark application due to frequent shuffles. What are some strategies you can employ to optimize shuffle operations and improve performance?

A) All of the above
B) Reduce the number of partitions in your RDDs
C) Increase the number of executors in your cluster
D) Use a broadcast variable for small, frequently accessed data

5. You need to enable secure access to Iceberg tables in CDP, controlling permissions at the table, column, and row level. Which of the following approaches would you investigate?

A) Configure role-based access control (RBA directly within Iceberg table properties.
B) Implement custom Spark IJDFs to enforce authorization logic.
C) Integrate with CDP's Kerberos-based authentication system.
D) Leverage Apache Ranger's integration with Iceberg.

Solutions: