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Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| CDP Platform Operations | 15% | - Cloudera Data Platform Architecture - Cloudera Flow Management - Data Lake and Storage - Cluster Management and Monitoring |
| Data Ingestion and Integration | 20% | - Data Transformation and ETL - Stream Data Ingestion - Data Federation - Batch Data Ingestion - CDC (Change Data Capture) |
| Data Pipeline Orchestration | 20% | - Pipeline Scheduling and Triggers - Workflow Dependencies - Error Handling and Retries - Apache Airflow on CDP |
| Data Quality and Governance | 15% | - Access Control and Security - Data Validation and Cleansing - Data Catalog and Metadata - Data Lineage |
| Data Processing with Spark | 30% | - Spark SQL and DataFrames - Spark Core Concepts - Spark Structured Streaming - DataFrame and Dataset APIs - Spark Performance Optimization |
Cloudera CDP Data Engineer - Certification Sample Questions:
You have an Airflow DAG that extracts data from a CSV file. How can you ensure that the DAG doesn't attempt to process the same file multiple times in case of retries or task failures?
- A. Configure the DAG to run only once and manually trigger subsequent runs when needed.
- B. Modify the extraction task to skip processing existing files based on a timestamp or marker within the file.
- C. Use the File lock operator before the extraction task to prevent other tasks from accessing the file while it's being processed.
- D. Implement a custom mechanism to rename the processed file after each successful run.
Correct Answer: C 🗳️
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When leveraging caching in Spark, which scenario illustrates the use of the MEMORY ONLY SER storage level most effectively?
- A. Caching datasets that require fast, sequential access without the need for serialization.
- B. Caching large datasets that do not fit into memory when stored in deserialized form.
- C. Caching small, static datasets used in lookup operations.
- D. Caching datasets that are frequently accessed and modified.
Correct Answer: B 🗳️
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How can you monitor the storage level and usage of persisted RDDs in your Spark application?
- A. All of the above
- B. Use Spark's web UI and look for information under the "Storage" tab
- C. Leverage Spark metrics like rdd.getStorageLevel() and rdd.getPersistedSize()
- D. Manually analyze the Spark application code
Correct Answer: A 🗳️
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Which of the following is TRUE about using Explain Plans for performance tuning?
- A. They provide detailed network latency information
- B. They are only useful for queries that involve joins
- C. They can help identify operations that may become performance bottlenecks
- D. Explain Plans always show the actual runtime of a query
Correct Answer: C 🗳️
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You encounter an error during the execution of your Airflow DAG. How can you identify the root cause of the issue and debug it effectively?
- A. Inspect the code of failed tasks directly within the Airflow web Ul for potential issues.
- B. All of the above
- C. Utilize Airflow logs (scheduler, worker) to analyze detailed error messages and stack traces.
- D. Rely on the error message displayed on the Airflow UI, even if it's generic.
Correct Answer: B 🗳️
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