최신 Cloudera Certification CDP-3002 무료샘플문제:

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

2. You want to use Spark to perform aggregations on data stored in Hive tables. How can you achieve this efficiently and seamlessly?

A) Write custom aggregation logic using Spark functions and loop through the entire DataFrame
B) Use HiveQL's aggregation capabilities and then convert the results back to a Spark DataFrame
C) Implement custom UDFs (User-Defined Functions) in Spark for complex aggregations
D) Leverage Spark SQL's built-in aggregation functions like SUM and COUNT

3. You encounter an error message stating "Failed to find persisted data for RDD" in your Spark application. What are the potential causes and how can you troubleshoot them?

A) All of the above
B) Spark encountered a network issue and couldn't access the persisted data
C) The data might have been corrupted or deleted from the storage location
D) The RDD was never persisted, or the storage level was set incorrectly

4. How can you ensure that a set of tasks in an Airflow DAG are executed in parallel after a specific initial task is completed?

A) Use the SequentialExecutor
B) Use the parallelism parameter in the airflow.cfg file
C) Set depends_on_past=True for all tasks
D) Use the ]] and [[ operators to set task dependencies

5. You want to schedule your Airflow DAG to run every hour, starting at midnight (00:00). How can you achieve this scheduling configuration?

A) Define a custom Python function to schedule the DAG at the desired time.
B) Set the schedule_interval parameter to "0 0 ".
C) Use the cron expression "@hourly".
D) Utilize Airflow triggers to initiate the DAG execution at midnight.

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