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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 Professional (CCP) Data Engineer Cloudera Certified Associate (CCA) Data Analyst |
| Certificate Validity Period: | 2 years |
| Exam Price: | USD 295 |
| Exam Duration: | 120 minutes |
| Exam Format: | Hands-on Lab (Performance-based), Multiple Choice |
| Available Languages: | English |
| Passing Score: | 70% |
| Real Exam Qty: | 60-70 |
| 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% | - Pipeline Scheduling and Triggers - Apache Airflow on CDP - Workflow Dependencies - Error Handling and Retries |
| Data Quality and Governance | 15% | - Data Catalog and Metadata - Data Lineage - Data Validation and Cleansing - Access Control and Security |
| Data Ingestion and Integration | 20% | - Data Federation - Data Transformation and ETL - Batch Data Ingestion - Stream Data Ingestion - CDC (Change Data Capture) |
| CDP Platform Operations | 15% | - Cluster Management and Monitoring - Cloudera Data Platform Architecture - Cloudera Flow Management - Data Lake and Storage |
| Data Processing with Spark | 30% | - Spark Performance Optimization - Spark SQL and DataFrames - Spark Structured Streaming - Spark Core Concepts - DataFrame and Dataset APIs |
Cloudera CDP Data Engineer - Certification Sample Questions:
Your Airflow DAG involves sending notifications upon successful completion of the entire pipeline. How can you achieve this functionality?
- A. Use the Email Operator to send an email notification upon successful DAG run completion.
- B. Implement a custom notification script within the final task of the DAG.
- C. Utilize Airflow variables to store notification details and access them within the final task.
- D. Configure the Airflow web UI to send alerts based on DAG run status.
Correct Answer: A 🗳️
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In Apache Spark, which of the following is the most effective strategy for minimizing data shuffling across nodes in a cluster?
- A. Filtering data after a wide transformation
- B. Increasing the number of partitions
- C. Using broadcast variables for small data
- D. Decreasing the number of partitions
Correct Answer: C 🗳️
Explanation: Only visible for SurePassExams members. You can sign-up / login (it's free).
Which of the following is a best practice for organizing tasks within a DAG in Apache Airflow?
- A. Dynamically generate tasks at runtime to avoid defining them explicitly in the DAG.
- B. Use a single Pythonoperator to execute all tasks as functions for efficiency.
- C. Group tasks with similar functionalities using SubDAGs for better readability and maintainability.
- D. Place all tasks directly in the root DAG to simplify monitoring and execution.
Correct Answer: C 🗳️
Explanation: Only visible for SurePassExams members. You can sign-up / login (it's free).
You want to schedule your ETL pipeline to run daily at 5:00 AM. How can you configure the DAG's scheduling?
- A. Utilize Airflow triggers to initiate the DAG execution at 5:00 AM daily.
- B. Set the schedule_interval parameter to "0 5
- C. Use the cron expression "daily at 5:00".
- D. Define a custom Python function to schedule the DAG at the desired time.
Correct Answer: B 🗳️
Explanation: Only visible for SurePassExams members. You can sign-up / login (it's free).
You want to perform an Iceberg table join in CDP using Spark SQL, but you notice it's much slower than expected. What could be some of the reasons? (Choose two)
- A. Spark is using nested loop joins instead of broadcast hash joins due to table sizes.
- B. Spark's dynamic query execution is enabled.
- C. Iceberg version mismatch between Spark and CDP.
- D. You're joining on a column with low cardinality (few distinct values).
- E. One of the tables isn't partitioned effectively.
Correct Answer: A,E 🗳️
By Daphne
