최신Cloudera CDP Data Engineer - Certification - CDP-3002무료샘플문제

A. It implies that the data is perfectly partitioned for the query

B. It may indicate potential performance issues for large datasets due to the high computational cost

C. It suggests that the query is fully optimized for distributed execution

D. It guarantees optimal performance for all dataset sizes

A. Checkpointing requires manual configuration and can be error-prone

B. Checkpointing always improves performance and has no drawbacks

C. Checkpointing introduces overhead for storing and recovering data, impacting performance

D. All of the above

A. Simplified data management

B. Reduced query performance due to metadata overhead

C. Increased data security

D. Automatic data deduplication

A. By removing unnecessary data replication

B. By increasing the number of partitions

C. By encrypting data at rest

D. By selecting only relevant partitions for query execution

A. Break down the DAG into smaller sub-DAGs with well-defined functionalities and clear naming conventions.

B. Use cryptic and abbreviated names for tasks and variables, assuming everyone understands the context.

C. Implement extensive logging within each task, regardless of its purpose, to capture detailed execution information.

D. Utilize comments sparingly within the DAG code, as the logic should be self-explanatory.

A. Compatibility issues with older Spark versions; ensure you're using a version supporting hidden partitioning

B. Errors due to incorrect partition discovery; you'll need to manually update Iceberg table metadata.

C. No problems; hidden partitioning is designed for this use case.

D. Performance issues due to unnecessary file scanning; consider adding event_timestamp as an explicit partition.

A. Utilize infrastructure as code (laC. tools like Terraform to manage Airflow deployment and configuration in a consistent and repeatable manner.

B. Thoroughly test the DAG in a staging environment before deploying it to production.

C. All of the above

D. Deploy the DAG directly to the production environment without any testing or staging phase.

A. Break down the DAG into smaller sub-DAGs with well-defined functionalities.

B. Use clear and descriptive names for tasks, operators, and variables throughout the DAG code.

C. All of the above

D. Implement extensive logging within each task to capture detailed execution information.

A. Utilize Iceberg's migration feature to automatically handle the conversion.

B. Use the ALTER TABLE statement in Hive to convert the table format to Iceberg.

C. Create an external Iceberg table pointing to the existing Hive data and then use Iceberg's INSERT OVERWRITE to copy the data.

D. Export the data from Hive and reload directly into a new Iceberg table.

A. All intermediate data will be automatically persisted to disk

B. No impact on the application, as Spark manages memory efficiently

C. Increased risk of application crashes due to memory exhaustion

D. Improved performance due to faster data access