CDP-3002 NEW QUESTIONS | RELIABLE CDP-3002 EXAM REGISTRATION

CDP-3002 New Questions | Reliable CDP-3002 Exam Registration

CDP-3002 New Questions | Reliable CDP-3002 Exam Registration

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Tags: CDP-3002 New Questions, Reliable CDP-3002 Exam Registration, Official CDP-3002 Practice Test, Reliable CDP-3002 Exam Prep, Valid CDP-3002 Test Syllabus

TestValid has many CDP Data Engineer - Certification Exam (CDP-3002) practice questions that reflect the pattern of the real Cloudera CDP-3002 exam. TestValid allows you to create a CDP Data Engineer - Certification Exam (CDP-3002) exam dumps according to your preparation. It is easy to create the CDP Data Engineer - Certification Exam (CDP-3002) practice questions by following just a few simple steps. Our CDP-3002 exam dumps are customizable based on the time and type of questions.

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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q247-Q252):

NEW QUESTION # 247
You have an Airflow DAG that extracts data from a CSV file. How can you ensure that the DAG doesn't attempt to process the same file multiple times in case of retries or task failures?

  • A. Modify the extraction task to skip processing existing files based on a timestamp or marker within the file.
  • B. Use the File lock operator before the extraction task to prevent other tasks from accessing the file while it's being processed.
  • C. Configure the DAG to run only once and manually trigger subsequent runs when needed.
  • D. Implement a custom mechanism to rename the processed file after each successful run.

Answer: B

Explanation:
The File lock operator is specifically designed to prevent concurrent access to a file during Airflow DAG execution. Option A ensures that only one task can process the CSV file at a time, preventing duplicate processing in case of retries or failures.


NEW QUESTION # 248
In the context of big data processing, what is a potential downside of relying heavily on schema inference?

  • A. Enhanced data security and compliance
  • B. Potential performance overhead due to the dynamic analysis of data structure
  • C. Increased data storage efficiency
  • D. Reduced flexibility in handling different data types

Answer: B

Explanation:
While schema inference provides significant flexibility and ease of data processing, it can introduce performance overhead. This overhead arises because the system must dynamically analyze the data structure to infer the schema, which can consume additional computational resources, especially with large volumes of data or complex data structures, potentially impacting overall processing speed.


NEW QUESTION # 249
In a meeting about Spark applications deployment on Kubernetes, your team discusses strategies for handling node failures. Which Kubernetes feature should be utilized to maintain high availability in case of node failures?
A Horizontal Pod Autoscaler

  • A. Resource Quotas
  • B. Pod Priority
  • C. ReplicaSets

Answer: B

Explanation:
ReplicaSets in Kubernetes are designed to maintain a stable set of replica Pods running at any given time, making them suitable for handling node failures and maintaining high availability. This ensures that if a node fails, Kubernetes can automatically redistribute the pods to other available nodes.


NEW QUESTION # 250
Which tool or API is primarily used for monitoring and inspecting the performance of Spark applications in real-time?
A Spark History Server

  • A. Apache Ambari
  • B. Spark Web UI
  • C. Hadoop YARN ResourceManager UI

Answer: C

Explanation:
The Spark Web UI is a monitoring tool that provides information about the execution of a Spark application. It gives insights into the scheduler stages and tasks, executor usage, storage usage, and environmental settings. It is accessible while the application is running and is the primary tool for real-time performance monitoring. The Spark History Server helps in inspecting the application performance after it has completed. Hadoop YARN ResourceManager UI and Apache Ambari are more generally used for cluster management and monitoring, not specifically for real-time Spark application performance.


NEW QUESTION # 251
You've deployed a Spark application using CDE that works heavily with Iceberg tables. To optimize performance, you want to ensure effective use of Kubernetes resources. Which of the following strategies helpful? (Choose two)

  • A. Implement Kubernetes liveness and readiness probes for your Spark driver and executor pods.
  • B. Configure dynamic resource allocation for your Spark executors.
  • C. Leverage Kubernetes taints and tolerations to dedicate specific nodes to Iceberg workloads.
  • D. Always use the latest available version of Spark.
  • E. Set pod anti-affinity rules to prevent multiple Spark tasks from being scheduled on the same node.

Answer: B,C

Explanation:
A). Dynamic resource allocation lets Spark scale up and down based on workload, improving resource usage efficiency. D. Taints and tolerations provide fine-grained control over pod scheduling, ensuring critical Iceberg jobs get priority on suitable nodes. B. While pod anti-affinity can help, resource distribution for data-intensive tasks is more important. C. Probes ensure Kubernetes restarts failed pods, but don't directly impact Iceberg performance. E. Newer Spark versions may have Iceberg optimizations, but resource management is crucial. Spark


NEW QUESTION # 252
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