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Cloudera CDP-3002 Exam Syllabus Topics:

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

2. In the context of Cloudera's SQL engines, what does the presence of a "Broadcast Hash Join" in an Explain Plan suggest about query performance?

A) It indicates an optimal use of network resources
B) It implies that no indexing is used in the join operation
C) It means that the query will execute faster than with any other join method
D) It suggests that the join operation might be a performance bottleneck for large datasets

3. You are deploying a Spark application on Kubernetes and need to specify the amount of memory allocated to each Executor. In your PySpark code, which configuration setting will you use?

A) 'spark.driver.memory'
B) 'spark.executor.memoryoverhead'
C) 'spark.executor.instances'
D) 'spark.executor.memory'

4. You're implementing a data quality process for Iceberg tables in CDP Which of the following Iceberg features can help you enforce constraints and detect data anomalies? (Choose two)

A) Manifest files
B) Metadata tables
C) Partitioning
D) Snapshots
E) Iceberg table constraints (not yet fully supporteD.

5. In a Kubernetes environment, why is it beneficial to run the Spark Driver in its own pod?

A) To use a different programming language for the Driver.
B) To reduce the cost of cloud resources.
C) To isolate the Driver from Executor pods for security reasons.
D) To automatically scale the Driver based on workload.

Solutions: