100% PASS REALISTIC CLOUDERA NEW CDP-3002 EXAM PDF

100% Pass Realistic Cloudera New CDP-3002 Exam Pdf

100% Pass Realistic Cloudera New CDP-3002 Exam Pdf

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Tags: New CDP-3002 Exam Pdf, CDP-3002 Valid Exam Pass4sure, CDP-3002 Test Labs, Exam CDP-3002 Simulations, CDP-3002 Reliable Exam Prep

At 2Pass4sure, we are aware that every applicant of the CDP Data Engineer - Certification Exam (CDP-3002) examination is different. We know that everyone has a distinct learning style, situations, and set of goals, therefore we offer Cloudera CDP-3002 updated exam preparation material in three easy-to-use formats to accommodate every exam applicant's needs. This article will go over the three formats of the CDP Data Engineer - Certification Exam (CDP-3002) practice material that we offer.

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Cloudera CDP Data Engineer - Certification Exam Sample Questions (Q72-Q77):

NEW QUESTION # 72
For automating the deployment of Spark applications within a Cloudera Data Engineering (CDE. environment using the CDE CLI, what is the primary consideration to ensure seamless integration with existing CI/CD pipelines?

  • A. Utilizing the cde job create command with appropriate flags for version control integration
  • B. Embedding CDE CLI commands within pipeline scripts and managing credentials securely
  • C. Converting all Spark code to be Kubernetes-native before deployment
  • D. Ensuring all Spark applications are containerized before deployment

Answer: B

Explanation:
The primary consideration for automating the deployment of Spark applications within a CDE environment using the CDE CLI and ensuring seamless integration with existing CI/CD pipelines involves embedding CDE CLI commands within the pipeline scripts and managing credentials securely. This approach allows the automation process to programmatically interact with the CDE service to deploy or update Spark applications, while also emphasizing the importance of secure credential management to protect access to the CDE environment and maintain security best practices.


NEW QUESTION # 73
A PySpark application is facing performance issues due to uneven distribution of data across the nodes. Which approach would best help in resolving this issue?

  • A. Convert the DataFrame to an RDD and then back to a DataFrame.
  • B. Use 'df.collect()'.
  • C. Employ to redistribute data based on a key.
  • D. Apply 'df.coalesce(Y to reduce the number of partitions.

Answer: C

Explanation:
Using in PySpark helps to redistribute the data more evenly based on the specified key column. This can alleviate issues caused by data skewness and improve the performance of distributed operations.


NEW QUESTION # 74
You're designing a schema for an Iceberg table that will store time-series sensor dat a. Which of the following considerations is most important for optimal query performance and storage efficiency?

  • A. Partitioning the data by both sensor ID and timestamp.
  • B. Pre-aggregating sensor data at hourly intervals before inserting into the Iceberg table.
  • C. Normalizing sensor data across multiple tables to reduce redundancy.
  • D. Using nested structs to represent the complex structure of sensor readings.

Answer: A


NEW QUESTION # 75
You need to handle potential errors and retries within your Airflow ETL pipeline. How can you achieve this functionality?

  • A. Configure the DAG to retry failed tasks automatically with a set number of retries.
  • B. Use the BranchPythonOperator to check for specific error conditions and branch the DAG execution accordingly.
  • C. Implement exception handling within your custom Python operators and define retry behavior manually.
  • D. All of the above

Answer: D

Explanation:
All options mentioned in D are valid approaches to handle errors and retries in Airflow. You can implement custom error handling, utilize conditional branching based on errors, and configure automatic retries for failed tasks to ensure robustness in your ETL pipeline.


NEW QUESTION # 76
You need to design an Airflow DAG that waits for a specific file to become available before proceeding with the downstream tasks. How can you achieve this dependency?

  • A. Configure the source system to notify Airflow when the file is ready for processing.
  • B. Use the File sensor operator to check for the file's existence and trigger downstream tasks upon its arrival.
  • C. Implement a custom loop within a Python operator to continuously check for the file until it appears.
  • D. Schedule the DAG to run periodically, hoping the file becomes available eventually.

Answer: B

Explanation:
The File sensor operator specifically addresses the need to wait for a file before proceeding in an Airflow DAG. Option A ensures that the downstream tasks only start after the desired file is present.


NEW QUESTION # 77
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