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Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Pipeline Orchestration | 20% | - Workflow Dependencies - Apache Airflow on CDP - Pipeline Scheduling and Triggers - Error Handling and Retries |
| Topic 2: Data Processing with Spark | 30% | - Spark SQL and DataFrames - Spark Core Concepts - Spark Performance Optimization - Spark Structured Streaming - DataFrame and Dataset APIs |
| Topic 3: CDP Platform Operations | 15% | - Cloudera Data Platform Architecture - Data Lake and Storage - Cloudera Flow Management - Cluster Management and Monitoring |
| Topic 4: Data Ingestion and Integration | 20% | - Batch Data Ingestion - Data Transformation and ETL - Data Federation - CDC (Change Data Capture) - Stream Data Ingestion |
| Topic 5: Data Quality and Governance | 15% | - Access Control and Security - Data Validation and Cleansing - Data Catalog and Metadata - Data Lineage |
Cloudera CDP Data Engineer - Certification Sample Questions:
1. What is the significance of "Sort Merge Join" appearing in an Explain Plan in Cloudera's SQL engines?
A) It indicates that the query will benefit from additional indexes
B) It is the least preferred join method due to its high CPU usage
C) It signifies that the join operation is performed by sorting and then merging two datasets, which can be efficient for large, sorted datasets
D) It suggests that the join operation is performed without sorting, leading to faster execution
2. What is the primary purpose of the Airflow Scheduler in Apache Airflow?
A) To provide a user interface for monitoring and managing DAGs
B) To monitor the state of tasks and trigger them based on their dependencies
C) To distribute tasks across workers for execution
D) To execute the code for each task in a DAG
3. Your Spark application on Kubernetes requires a secure connection to a database. You've stored the database password in Kubernetes Secrets. How would you typically access this secret in your PySpark application?
A) By querying the Kubernetes API
B) By using a ConfigMap
C) Through an external secrets manager
D) By reading the secret file from a volume mount
4. You are developing a PySpark application that processes a large dataset. Your application involves a transformation that requires shuffling data across the Executors. Which of the following responsibilities of the Spark Driver is most directly involved in this process?
A) Managing the lifecycle of Executor pods in Kubernetes.
B) Interpreting the user application and creating an execution plan.
C) Monitoring the progress and status of tasks on the Executors.
D) Managing the shuffling and aggregation of data across Executors.
5. Your Airflow DAG includes tasks that can potentially fail due to various reasons. How can you handle such failures and ensure the overall workflow continues as intended?
A) Implement custom logic within each task to handle potential errors and retry failed tasks manually.
B) Configure the DAG to automatically retry failed tasks a specific number of times.
C) Utilize Airflow XCom to share information about failed tasks with downstream tasks for alternative processing.
D) All of the above
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: B,D |
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