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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q79-Q84):

NEW QUESTION # 79
What is the primary purpose of using Cloudera's API in a data engineering context?

A. To increase the time it takes to deploy applications.
B. To bypass security configurations for rapid development.
C. To automate the provisioning, configuration, and scaling of clusters.
D. To manually configure and start cluster services.

Answer: C

Explanation:
The primary purpose of using Cloudera's API in a data engineering context is to automate the provisioning, configuration, and scaling of clusters. This enables efficient management of big data environments by allowing programmatic control over various aspects of the cluster, reducing the need for manual intervention and enabling faster and more reliable deployments.

NEW QUESTION # 80
In a CI/CD pipeline, what is a key consideration when integrating Cloudera Data Engineering (CDE. service API calls for deploying Spark jobs, specifically regarding security?

A. Incorporating API token rotation and secure secret management practices
B. Using generic Spark Docker images for all jobs to avoid API customization
C. Disabling TLS for API endpoints to simplify integration
D. Hardcoding API keys in the pipeline scripts

Answer: A

Explanation:
A key consideration when integrating CDE service API calls for deploying Spark jobs in a CI/CD pipeline, specifically regarding security, involves incorporating API token rotation and secure secret management practices. This ensures that authentication credentials used in API calls are securely managed and rotated regularly to protect access to the CDE environment. Hardcoding API keys, using generic Docker images to avoid API customization, and disabling TLS for API endpoints would all undermine security best practices and potentially expose the environment to unauthorized access or other security risks.

NEW QUESTION # 81
You need to create a new Hive table from a Spark DataFrame. What are the different approaches you can consider?

A. Directly write the DataFrame to a directory in HDFS and define a corresponding Hive table schema
B. Convert the DataFrame to a temporary table and then use HiveQL commands to create a permanent table
C. All of the above
D. Use the DataFrame.write.saveAsTable("table_name") method with appropriate options

Answer: C

Explanation:
While each option offers a way to create a Hive table from a Spark DataFrame, they provide different levels of control and convenience. Option A requires manual schema definition, while B offers a concise approach with configuration options. Option C might be useful for specific scenarios, but B is generally preferred.

NEW QUESTION # 82
Your Airflow DAG involves tasks that require access to specific resources like databases or external services. How can you ensure these resources are available and properly configured for the DAG execution?

A. Implement custom logic within each task to dynamically discover and connect to resources.
B. Hardcode connection details (credentials, URLs) directly within the DAG code.
C. Grant everyone access to all resources to avoid potential configuration issues.
D. Utilize Airflow connections to store and manage resource details securely.

Answer: D

Explanation:
While option A might seem straightforward, it can become difficult to manage and maintain for complex workflows. Option B emphasizes effective DAG design principles:Modularization: Breaking down the workflow into smaller, reusable tasks improves clarity, maintainability, and testing. Dependencies: Defining clear dependencies between tasks ensures they execute in the correct order.

NEW QUESTION # 83
You're tasked with optimizing an existing Airflow DAG that processes large datasets daily. The DAG has multiple tasks, some of which frequently fail due to memory constraints on the worker nodes. Which approach would best mitigate this issue without upgrading hardware?

A. Split the DAG into multiple smaller DAGs to reduce the load on workers.
B. Increase the priority_weight of memory-intensive tasks to ensure they run first.
C. Implement the Retry operator to automatically retry failed tasks.
D. Use the Pool feature to limit the number of concurrent memory-intensive tasks.

Answer: D

Explanation:
The Pool feature in Airflow allows you to limit the number of tasks running concurrently in a given pool of resources, which can help manage memory usage and prevent workers from being overwhelmed by memory-intensive tasks. By assigning memory-intensive tasks to a pool with a limited number of slots, you ensure that only a certain number of these tasks can execute at the same time, thereby managing the overall memory consumption on worker nodes. Increasing priority_weight affects task scheduling order but does not manage resource constraints, retrying tasks does not address the root cause of memory issues, and splitting the DAG might help organizational clarity but does not directly address concurrent memory usage.

NEW QUESTION # 84
......

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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q79-Q84):

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