Cloudera CDP-3002 Questions & Answers - in .pdf
- Vendor: Cloudera
- Exam Code: CDP-3002
- Exam Name: CDP Data Engineer - Certification Exam
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- Vendor: Cloudera
- Exam Code: CDP-3002
- Exam Name: CDP Data Engineer - Certification Exam
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Cloudera CDP-3002 Exam Overview:
| Certification Vendor: | Cloudera |
|---|---|
| Exam Name: | CDP Data Engineer - Certification Exam |
| Exam Number: | CDP-3002 |
| Real Exam Qty: | 50 |
| Certificate Validity Period: | 3 years |
| Passing Score: | 55% |
| Related Certifications: | Cloudera Certified Data Engineer CDP Generalist |
| Exam Price: | $330 USD |
| Exam Duration: | 90 minutes |
| Exam Format: | Multiple Response, Scenario-Based Questions, Multiple Choice |
| Available Languages: | English |
| Recommended Training: | Apache Iceberg Fundamentals (DENG-152) Advanced Spark Performance Tuning (DENG-200) Cloudera Data Engineering (DENG-100) |
| Exam Registration: | Cloudera Certification Portal |
| Sample Questions: | Cloudera CDP-3002 Sample Questions |
| Exam Way: | Online Proctored (remote), no onsite option |
| Pre Condition: | No mandatory prerequisites; recommended experience: 1+ year designing/developing data pipelines with Cloudera tools, Spark, and Airflow |
| Official Syllabus URL: | https://www.cloudera.com/certification/cdp-data-engineer.html |
Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Deployment & Operations | 10% | - CDP Data Engineering Service
|
| Apache Spark Development & Processing | 48% | - Spark Architecture & Execution Model
|
| Workflow Orchestration | 15% | - Pipeline Development
|
| Integration & Optimization | 5% | - Hive & Spark Integration
|
| Data Storage & Modeling | 22% | - Data Formats & Storage
|
Cloudera CDP Data Engineer - Certification Sample Questions:
Question #1
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. Using generic Spark Docker images for all jobs to avoid API customization
B. Disabling TLS for API endpoints to simplify integration
C. Incorporating API token rotation and secure secret management practices
D. Hardcoding API keys in the pipeline scripts
Question #2
In Spark, when is it most beneficial to use the repartitionByRange method?
A. When minimizing network traffic during shuffle operations is the primary concern.
B. When sorting data within each partition by a specified column or set of columns is required.
C. When the data is highly skewed and a uniform distribution of data across partitions is required.
D. When decreasing the number of partitions to reduce task scheduling overhead.
Question #3
What does setting the Spark configuration parameter 'spark.sql.shuffle.partitions' impact?
A The default level of parallelism for joins and aggregations
A. The serialization format of data
B. The compression codec used for shuffle files
C. The memory allocation for executor instances
Question #4
What is the difference between RDDs and DataFrames in Spark?
A. Both B and C
B. RDDs are mutable, while DataFrames are immutable
C. RDDs are lower-level abstractions, while DataFrames offer higher-level functionalities
D. RDDs represent distributed datasets, while DataFrames work with structured data
Question #5
In the context of dynamic partitioning in Hive, what challenge does the use of too many dynamic partitions in a single load operation present?
A. It can lead to an excessive number of small files, negatively impacting HDFS performance.
B. It simplifies the management of Hive metadata, reducing the load on the NameNode.
C. It automatically disables the execution of map-reduce jobs.
D. It enhances data security by segmenting data into finer-grained partitions.
Solutions:
| Question #1 Correct Answer: C | Question #2 Correct Answer: B | Question #3 Correct Answer: A | Question #4 Correct Answer: A | Question #5 Correct Answer: A |
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