최신Cloudera CDP Data Engineer - Certification - CDP-3002무료샘플문제

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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

A. Convert the nested data into a simpler format like a single-level DataFrame

B. Directly access elements using their position within the nested structure

C. Implement custom recursive functions to navigate through the nested structure

D. Leverage Spark SQL's built-in functions like explode and struct

A. To define the Docker image for the Driver pod.

B. To specify the name of the Spark Driver pod.

C. To name the Executor pods.

D. To assign a specific node for the Driver pod.

A. Store all DAG code in a shared folder on a local file system, relying on manual coordination between team members.

B. Utilize a version control system like Git to track changes in the DAG code and enable collaborative development.

C. Grant full access to the Airflow web Ul to all team members for code modifications, regardless of their roles.

D. Implement a custom workflow management system outside of Airflow for managing DAG development.

A. You can directly inspect the custom Python script used for the check to identify potential issues.

B. All of the above

C. The Airflow logs (scheduler, worker) might contain relevant error messages from the failing task.

D. The Airflow web UI task details page might display error messages related to the failed check.

A. Modify the data files directly using low-level tools to overwrite the sensitive data.

B. Use Iceberg's EXPIRE SNAPSHOTS procedure to remove snapshots containing the sensitive data.

C. Issue a standard Iceberg DELETE query, as deleted data will automatically be expunged from the table.

D. Implement a custom process, leveraging Iceberg's row-level updates to delete the sensitive records and then using file-level operations to physically remove the data.

A. The phase of the moon

B. User-defined functions (UDFs) complexity

C. Network latency between nodes

D. Data skewness

A. Store passwords and other sensitive data directly within your Airflow code.

B. Implement encryption for sensitive data at rest and in transit.

C. Grant everyone access to the Airflow web UI to monitor and manage the pipeline.

D. Use environment variables to store sensitive credentials for accessing data sources.

A. The data might have been corrupted or deleted from the storage location

B. Spark encountered a network issue and couldn't access the persisted data

C. All of the above

D. The RDD was never persisted, or the storage level was set incorrectly

A. Implement a custom loop within a Python operator to continuously check for the file until it appears.

B. Use the File sensor operator to check for the file's existence and trigger downstream tasks upon its arrival.

C. Configure the source system to notify Airflow when the file is ready for processing.

D. Schedule the DAG to run periodically, hoping the file becomes available eventually.

A. Automatic conversion of all operations to map-reduce jobs

B. Automatic selection of the optimal storage level based on the DataFrame's size

C. Predicate pushdown and other logical optimizations before physical execution

D. Inlining of functions to reduce the overhead of JVM method calls

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

B. All of the above

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

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