Cloudera Certification CDP-3002 Braindumps

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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

Question #1
How can you monitor the storage level and usage of persisted RDDs in your Spark application?

A. Leverage Spark metrics like rdd.getStorageLevel() and rdd.getPersistedSize()
B. Manually analyze the Spark application code
C. All of the above
D. Use Spark's web UI and look for information under the "Storage" tab

Question #2
You are working with a large, skewed dataset in Spark. How would you optimize processing to mitigate the impact of skew and improve performance?

A. Addressing skewed data requires
B. Use salting on the skewed column during data partitioning.
C. Implement custom partitioners to evenly distribute skewed values.
D. Broadcast the skewed data to all executors.

Question #3
In the context of caching data for reuse in Spark, how does the Tungsten project contribute to enhancing memory management and execution efficiency?

A. By optimizing Spark's internal memory management and data processing operations
B. By enforcing strict data typing to reduce runtime type checking overhead
C. By rewriting Spark operations in a lower-level language for direct memory access
D. By providing a graphical interface for memory management

Question #4
You are writing a PySpark application where you need to collect the final results from various Executors and present them to the user. Which aspect of the Spark Driver's role is primarily involved in this process?

A. Optimizing resource utilization within the Kubernetes cluster.
B. Collecting final results from Executors and providing the output.
C. Constructing the logical plan from the user application.
D. Managing Kubernetes API interactions for Executor lifecycle.

Question #5
You need to design your Airflow DAG for data quality checks to be scalable and manageable as the number of datasets and checks grows. How can you achieve this?

A. Implement a modular design using sub-DAGs, where each sub-DAG encapsulates the data quality checks for a specific dataset.
B. Utilize Airflow variables to store configuration details like data source paths and check thresholds.
C. Hardcode all data quality checks and data sources directly within the DAG code.
D. Leverage external configuration files (e.g., YAML or JSON) to define data quality checks and associated parameters.

Solutions: