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Real CDP-3002 Exam Questions | Best CDP-3002 Vce

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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q160-Q165):

NEW QUESTION # 160
Which feature of Apache Avro facilitates dynamic schema inference during data serialization and deserialization?

A. Manual schema declaration
B. Schema versioning via external services
C. Strong data typing
D. Embedded schema within data files

Answer: D

Explanation:
Apache Avro facilitates dynamic schema inference through the embedding of schemas within data files. This feature allows each data file to carry its schema, enabling seamless serialization and deserialization processes without the need for external schema definitions. The embedded schema ensures that the data structure is always known, simplifying data processing and integration tasks.

NEW QUESTION # 161
How can you implement a data quality check in Apache Airflow that verifies the row count of a table does not decrease from the previous DAG run?

A. Utilize the PreviousDagRunSensor with a custom Python function for comparison.
B. Store the row count from the previous run in Airflow Variables and compare it using a Pythonoperator.
C. Implement a custom SqlSensor that checks the row count against a stored value in XComs.
D. Use the BranchPythonOperator to branch the workflow based on the row count comparison logic.

Answer: B

Explanation:
By storing the row count from the previous DAG run in Airflow Variables, you can then use a Pythonoperator to retrieve this value and compare it against the current row count. This method allows for a flexible and dynamic approach to monitoring table row counts over time, ensuring data consistency and alerting to potential data loss or anomalies.

NEW QUESTION # 162
Which Spark configuration parameter should be increased to improve performance when dealing with large broadcast variables?

A. spark.executor.memory
B. spark.default.parallelism
C. spark.driver.memory
D. spark.broadcast.blockSize

Answer: C

Explanation:
When using large broadcast variables, increasing the spark.driver.memory is beneficial because the driver needs sufficient memory to store the broadcast variables before they are sent to executors. Broadcast variables are sent from the driver to all executors, and having insufficient driver memory could lead to out-of-memory errors or performance bottlenecks. spark.executor.memory affects executor memory size, spark.broadcast.blockSize is not a valid Spark configuration parameter, and spark.default.parallelism affects the default number of partitions in RDDs, but not the handling of broadcast variables.

NEW QUESTION # 163
How does partition pruning contribute to query performance in the Cloudera Data Platform?

A. By selecting only relevant partitions for query execution
B. By encrypting data at rest
C. By removing unnecessary data replication
D. By increasing the number of partitions

Answer: A

Explanation:
Partition pruning enhances query performance by allowing the engine to skip over partitions of the data that are not relevant to the query, thereby reducing the amount of data scanned and processed.

NEW QUESTION # 164
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. Constructing the logical plan from the user application.
B. Collecting final results from Executors and providing the output.
C. Optimizing resource utilization within the Kubernetes cluster.
D. Managing Kubernetes API interactions for Executor lifecycle.

Answer: B

Explanation:
The Spark Driver's role includes collecting the final results of the tasks executed by the Executors. In this scenario, where the PySpark application requires gathering and presenting results, the Driver compiles data from various Executors and provides the final output, fulfilling its responsibility in result collection and output generation.

NEW QUESTION # 165
......

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