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Cloudera CDP-3002 Exam Syllabus Topics:

Cloudera CDP Data Engineer - Certification Sample Questions:

1. Considering the dynamic nature of data workloads, how can Spark's dynamic resource allocation feature impact caching strategies?

A) By automatically persisting all cached data to disk to prevent data loss during node decommissioning.
B) By potentially evicting cached data when executors are removed due to reduced workload, affecting performance.
C) By encrypting cached data to enhance security when reallocating resources dynamically.
D) By increasing the memory available for caching as the data workload increases, without manual intervention.

2. In the context of Spark, what is a potential downside of indiscriminate use of data caching, especially with the MEMORY_AND DISK storage level?

A) It enhances data security by storing intermediate results in encrypted form.
B) It can lead to reduced fault tolerance due to reliance on in-memory storage.
C) It can decrease network traffic by reducing the need for data shuffling.
D) It may increase execution time due to overheads from frequent disk 1/0 operations.

3. 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) 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) Schedule the DAG to run periodically, hoping the file becomes available eventually.
D) Configure the source system to notify Airflow when the file is ready for processing.

4. You're deploying a Spark application in a Kubernetes cluster. Which YAML specification would correctly define a SparkApplication in cluster mode using Scala and the Spark Pi example?

A) Option D
B) Option B
C) Option C
D) Option A

5. 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) Hardcoding API keys in the pipeline scripts
C) Using generic Spark Docker images for all jobs to avoid API customization
D) Disabling TLS for API endpoints to simplify integration

Solutions: