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Cloudera CDP-3002 Exam Overview:
| Certification Vendor: | Cloudera |
| Exam Name: | CDP Data Engineer - Certification Exam |
| Exam Number: | CDP-3002 |
| Related Certifications: | Cloudera Certified Associate (CCA) Data Analyst Cloudera Certified Professional (CCP) Data Engineer |
| Exam Duration: | 120 minutes |
| Available Languages: | English |
| Passing Score: | 70% |
| Exam Format: | Multiple Choice, Hands-on Lab (Performance-based) |
| Real Exam Qty: | 60-70 |
| Exam Price: | USD 295 |
| Certificate Validity Period: | 2 years |
| Sample Questions: | Cloudera CDP-3002 Sample Questions |
| Exam Way: | Online proctored exam at authorized testing centers |
| Pre Condition: | Recommended: Hands-on experience with Cloudera CDP, familiarity with Python/Scala, and understanding of distributed data processing concepts |
| Official Syllabus URL: | https://www.cloudera.com/about/certification/cdp-certification.html |
Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Pipeline Orchestration | 20% | - Error Handling and Retries - Apache Airflow on CDP - Pipeline Scheduling and Triggers - Workflow Dependencies |
| Data Quality and Governance | 15% | - Data Validation and Cleansing - Data Lineage - Access Control and Security - Data Catalog and Metadata |
| CDP Platform Operations | 15% | - Cloudera Data Platform Architecture - Cluster Management and Monitoring - Data Lake and Storage - Cloudera Flow Management |
| Data Processing with Spark | 30% | - Spark Core Concepts - Spark SQL and DataFrames - DataFrame and Dataset APIs - Spark Structured Streaming - Spark Performance Optimization |
| Data Ingestion and Integration | 20% | - Stream Data Ingestion - Data Transformation and ETL - Batch Data Ingestion - CDC (Change Data Capture) - Data Federation |
Cloudera CDP Data Engineer - Certification Sample Questions:
1. In an Airflow DAG, you have tasks A, B, C, and D. Task A must complete before B and C can start, but B and C can run in parallel. Task D should only run once both B and C have completed. How do you set up these dependencies?
A) A]] B, C]] D
B) A.set_downstream([B, C]) and set_upstream([B, C])
C) [A ]] B, A]] C] and [B, C] ]] D
D) A]] B ]] C]] D
2. How does dynamic schema inference impact the processing of streaming data in real-time analytics platforms?
A) It requires streaming data to be stored before its schema can be inferred.
B) It enables the platform to adapt to changes in data format without downtime.
C) It significantly increases the latency of data processing operations.
D) It decreases the overall throughput of the analytics platform.
3. You are setting up a Spark Driver pod in Kubernetes and need to specify resource requests and limits. Which of the following configurations ensures that the Spark Driver has at least 1 CPU and 1 Gi of memory but can use up to 2 CPUs and 2 Gi of memory if available?
A) Option D
B) Option B
C) Option C
D) Option A
4. How can you use Apache Airflow to ensure a data quality check stops the workflow if it fails, without failing subsequent tasks that are not dependent on the data quality check?
A) Use the BranchPythonOperator to split the workflow conditionally.
B) Set trigger_rule='all_done' on subsequent tasks.
C) Use the ShortCircuitOperator with a condition that returns False if the check fails.
D) Use the TaskGroup with trigger_rule='one_failed' for the data quality check task.
5. Discuss the trade-offs between using wide tables (many columns) and narrow tables (few columns) in Spark and the implications for data processing efficiency.
A) Spark can optimize data access patterns for both wide and narrow tables equally.
B) Both A and C.
C) Wide tables require more storage and may lead to performance overhead due to data serialization.
D) Wide tables offer better performance for filtering operations, while narrow tables excel in aggregations.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: B | Question # 4 Answer: C | Question # 5 Answer: B |
By Bridget
