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

2. What technique can be employed to optimize join performance by reducing data shuffle across the network?

A) Partitioning both datasets on a different key
B) Decreasing the memory allocated to executors
C) Increasing the number of partitions
D) Using broadcast joins for smaller datasets

3. How does setting the "priority_weight" parameter in Airflow tasks influence the scheduler's behavior?

A) It allocates more CPIJ and memory resources to tasks with higher priority weights.
B) It determines the order in which tasks are executed within the same DAG.
C) It influences the order in which tasks are selected for execution across multiple DAGs competing for resources.
D) It sets a fixed time slot during which the task must be executed, overriding the scheduler's automatic timing.

4. You are designing a data pipeline that involves ingesting data from multiple sources, performing data transformations using Spark, and storing the results in a data lake. How would you leverage the Cloudera Data Engineering service to ensure efficient and fault-tolerant execution?

A) Utilize separate Spark jobs for each data source and transformation step.
B) Design the pipeline with stages and steps, leveraging Spark operators for transformations and utilizing retries and error handling mechanisms.
C) Implement custom logic within the YAML configuration file to manage data flow and error handling.
D) Develop a single Spark job containing all transformation logic.

5. Why is it recommended to use the DataFrame API over RDDs for most data processing tasks in Spark?

A) DataFrames provide more fine-grained control over partitioning and parallelism.
B) RDDs are deprecated and will be removed in future versions of Spark.
C) DataFrames automatically optimize queries using the Catalyst optimizer and Tungsten execution engine.
D) DataFrames require less memory and compute resources compared to RDDs.

Solutions: