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Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| CDP Platform Operations | 15% | - Cloudera Flow Management - Data Lake and Storage - Cluster Management and Monitoring - Cloudera Data Platform Architecture |
| Data Pipeline Orchestration | 20% | - Apache Airflow on CDP - Error Handling and Retries - Workflow Dependencies - Pipeline Scheduling and Triggers |
| Data Quality and Governance | 15% | - Data Validation and Cleansing - Data Lineage - Data Catalog and Metadata - Access Control and Security |
| Data Ingestion and Integration | 20% | - Batch Data Ingestion - Stream Data Ingestion - Data Federation - CDC (Change Data Capture) - Data Transformation and ETL |
| Data Processing with Spark | 30% | - Spark Core Concepts - Spark Performance Optimization - DataFrame and Dataset APIs - Spark SQL and DataFrames - Spark Structured Streaming |
Cloudera CDP Data Engineer - Certification Sample Questions:
You're building an Airflow ETL pipeline that involves data validation checks. How can you integrate these checks into the pipeline and handle potential failures?
- A. Use the FailoverOperator to automatically skip downstream tasks if the validation check fails.
- B. Implement the validation checks within custom Python operators and raise exceptions upon failure, triggering downstream tasks to handle the error.
- C. Configure the DAG to send email notifications upon validation failures for manual intervention.
- D. Leverage XCom to share the validation results with downstream tasks for further processing.
Correct Answer: B 🗳️
Explanation: Only visible for VCE4Plus members. You can sign-up / login (it's free).
Explain the concept of lineage tracking in Spark and its benefits for fault tolerance and debugging.
- A. Lineage allows for caching intermediate results, improving performance.
- B. Both A and B.
- C. Lineage tracks the dependencies between data transformations, enabling efficient re-execution on failures.
- D. It creates a log of all operations performed on data, aiding in debugging issues.
Correct Answer: B 🗳️
Explanation: Only visible for VCE4Plus members. You can sign-up / login (it's free).
In the context of Cloudera's SQL engines, what does the presence of a "Broadcast Hash Join" in an Explain Plan suggest about query performance?
- A. It suggests that the join operation might be a performance bottleneck for large datasets
- B. It means that the query will execute faster than with any other join method
- C. It indicates an optimal use of network resources
- D. It implies that no indexing is used in the join operation
Correct Answer: A 🗳️
Explanation: Only visible for VCE4Plus members. You can sign-up / login (it's free).
You want to write the results of a Spark DataFrame back to a Hive table. How can you achieve this efficiently?
- A. Convert the DataFrame to a temporary table and then use HiveQL operations
- B. Use the DataFrame.write.saveAsTable("table_name") method
- C. Implement a custom function to write individual rows to the Hive table
- D. Use the hiveContext.sql("lNSERT INTO table_name SELECT FROM df') method
Correct Answer: B 🗳️
Explanation: Only visible for VCE4Plus members. You can sign-up / login (it's free).
What is a potential downside of overly granular partitioning in Hive tables?
- A. Automatic data deduplication
- B. Increased data security
- C. Simplified data management
- D. Reduced query performance due to metadata overhead
Correct Answer: D 🗳️
Explanation: Only visible for VCE4Plus members. You can sign-up / login (it's free).
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