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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q74-Q79):
NEW QUESTION # 74
You're working with an ETL pipeline that extracts data from multiple sources. How can you ensure that the pipeline only processes the latest data and avoids re-processing already processed data?
- A. Use timestamps or versioning information provided by the data sources to identify new data.
- B. Rely on Airflow's built-in mechanisms to handle data freshness automatically.
- C. Configure the data sources to only provide new data by default.
- D. Implement a custom mechanism to track the last processed record for each source and filter data accordingly.
Answer: A,D
Explanation:
While option C might not always be feasible or reliable depending on the specific data source, both option A and B provide valid approaches. Utilizing timestamps, versioning, or custom tracking mechanisms allows the pipeline to identify and process only the latest data, avoiding unnecessary re-processing.
NEW QUESTION # 75
How does the Cloudera Data Engineering service integrate with cloud storage solutions like Amazon S3 or Azure Blob Storage?
- A. Requires manual configuration for each storage bucket.
- B. Not directly supported, requires external tools.
- C. Utilizes built-in connectors for seamless access.
- D. Requires custom scripting for each cloud storage provider.
Answer: C
Explanation:
The Cloudera Data Engineering service comes with pre-built connectors for various cloud storage solutions like Amazon S3 and Azure Blob Storage. These connectors simplify data movement and integration between the platform and cloud storage solutions.
NEW QUESTION # 76
You're deploying your Airflow DAGs to a production environment. What are some best practices to ensure reliability and maintainability?
- A. Implement version control for your DAG code and configuration files to track changes and facilitate rollbacks.
- B. Schedule DAGs to run as frequently as possible to ensure real-time data processing.
- C. Grant full access to the Airflow web UI to everyone for monitoring and troubleshooting purposes.
- D. Use the same Airflow environment for development, testing, and production deployments.
Answer: A,C
NEW QUESTION # 77
You are working with a complex Spark application involving multiple stages, and you want to ensure that later stages only start processing after all data from the previous stage is complete. How can you achieve this dependency management in Spark?
- A. Leverage Spark Streaming concepts like micro-batching for real-time data processing
- B. Use explicit persist() calls with different storage levels for each stage
- C. Rely on Spark's lineage tracking and stage boundaries to enforce dependencies
- D. Implement custom logic with synchronization mechanisms between stages
Answer: A,C
Explanation:
Spark automatically tracks lineages and defines stages based on your code. This allows Spark to manage dependencies efficiently B. You don't need explicit persist() calls for each stage or custom synchronization (a, C.. Spark Streaming concepts D are relevant for real-time processing scenarios, not dependency management in batch processing.
A combination of techniques can significantly improve Spark application performance. Increasing executors A can help, but it's not the sole solution. Vectorized operations B use optimized instructions for faster data processing. Caching intermediate results C reduces redundant computations. Combining these strategies leverages Spark's capabilities effectively and optimizes processing, especially for complex transformations.
NEW QUESTION # 78
You have an Airflow DAG that includes tasks for data extraction, transformation, and loading. You notice that the transformation tasks are computationally intensive and are causing delays in the DAG's execution. To optimize performance, you decide to offload these tasks to a cloud-based service that can scale dynamically. Which approach ensures minimal changes to the DAG structure while integrating this optimization?
- A. Modify the transformation tasks to use the PythonOperator to make API calls to the cloud service, handling the transformation.
- B. Implement the transformation tasks as DockerOperator tasks, with each task running in a containerized environment on the cloud service.
- C. Replace the transformation tasks with HttpSensor tasks that trigger the cloud service and poll for completion.
- D. Use the ExternalTaskSensor to wait for the transformation to complete on the cloud service before proceeding.
Answer: A
Explanation:
Option C offers a direct and efficient way to integrate the cloud-based service into the existing DAG with minimal changes. By modifying the transformation tasks to use the PythonOperator for making API calls to the cloud service, you can offload the computational work while maintaining the overall structure and logic of the DAG. This approach allows for dynamic scaling of resources on the cloud service and keeps the task orchestration within Airflow. The HttpSensor is primarily used for sensing or polling a condition, not for offloading and executing tasks. The ExternalTaskSensor is designed to wait for a task in a different DAG to complete, which doesn't apply here. The DockerOperator could potentially offload computation but assumes the cloud service can execute Docker containers directly, which may not align with the specific scaling capabilities or interfaces of the service in question.
NEW QUESTION # 79
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