EXAM CDP-3002 TRAINING, AUTHORIZED CDP-3002 CERTIFICATION

Exam CDP-3002 Training, Authorized CDP-3002 Certification

Exam CDP-3002 Training, Authorized CDP-3002 Certification

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Tags: Exam CDP-3002 Training, Authorized CDP-3002 Certification, Exam CDP-3002 Vce Format, CDP-3002 Exam Sample, Dump CDP-3002 Collection

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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q293-Q298):

NEW QUESTION # 293
What mechanism does Airflow provide to retry failed tasks?

  • A. The on failure callback function in DAG definitions
  • B. Airflow Scheduler's automatic rerun feature
  • C. Manual intervention and rerun via the Airflow Webserver
  • D. The retry_delay and retries parameters in task definitions

Answer: D

Explanation:
Airflow allows task retries by specifying retries (the number of retry attempts) and retry_delay (the time to wait between retries) parameters directly in the task definitions. This mechanism enables automatic retry of tasks that fail, helping to handle transient issues or dependencies that may not be ready, without needing manual intervention or relying on callbacks for handling failures.


NEW QUESTION # 294
While working with Spark SQL, you encounter an error message stating "Unable to resolve table 'table_name"'. What could be the potential cause of this error?

  • A. The Spark application doesn't have sufficient permissions to access the table
  • B. The table doesn't exist in the Hive metastore
  • C. All of the above
  • D. The table schema is not compatible with Spark SQL

Answer: C

Explanation:
All listed options could potentially lead to the error message. The table might not exist in the Hive metastore A, access permissions might be insufficient B, or the schema might not be compatible with Spark SQL C. Examining the metastore, checking permissions, and validating the schema can help diagnose the specific cause.


NEW QUESTION # 295
You've discovered that a production Iceberg table has several corrupted data files. Which of the following actions could help address this issue?

  • A. Restore the table to a previous snapshot using Iceberg's time travel feature.
  • B. Apply Iceberg's REMOVE ORPHAN FILES procedure.
  • C. Run the VACUUM procedure on the Iceberg table.
  • D. Drop and recreate the Iceberg table from scratch.

Answer: B

Explanation:
B). Partitioning by both sensor ID and timestamp allows for efficient filtering and pruning when querying specific sensors and time ranges, which is typical for time-series data.


NEW QUESTION # 296
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.executor.memoryoverhead'
  • B. 'spark.executor.instances'
  • C. 'spark.executor.memory'
  • D. 'spark.driver.memory'

Answer: C

Explanation:
The configuration 'spark.executor.memory' is used to specify the amount of memory to be allocated to each Spark Executor in a Spark application. This setting is crucial for ensuring efficient memory management for the Executors running in Kubernetes pods.


NEW QUESTION # 297
Your team is using PySpark and wants to ensure task re-execution in case of a node failure. What mechanism in Spark ensures that tasks are retried on other nodes upon failure?

  • A. Task Re-execution
  • B. Master Node Redundancy
  • C. Checkpointing
  • D. Data Replication

Answer: A

Explanation:
Task re-execution is the mechanism in Spark that ensures tasks are retried on other nodes in the event of a node failure. This is a key feature of Spark's fault tolerance capability, allowing it to handle worker node failures without data loss.


NEW QUESTION # 298
......

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