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Cloudera CDP-3002 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: Integration & Optimization | 5% | - Troubleshooting
- 1. Resource management
- 2. Bottleneck identification
- Hive & Spark Integration
- 1. Cross-engine query execution
|
| Topic 2: Data Storage & Modeling | 22% | - Data Formats & Storage
- 1. Columnar formats (Parquet, ORC)
- 2. Schema design & evolution
- Distributed Persistence
- 1. HDFS & cloud storage integration
- 2. Data locality & access patterns
- Apache Iceberg
- 1. Table management & ACID compliance
- 2. Partitioning & layout optimization
|
| Topic 3: Deployment & Operations | 10% | - CDP Data Engineering Service
- 1. API & CLI usage
- 2. Environment configuration
- Security & Governance
- 1. Data lineage & compliance
- 2. Access control & authentication
|
| Topic 4: Apache Spark Development & Processing | 48% | - Spark Streaming & Structured Streaming
- 1. Micro-batch & stream processing
- 2. Checkpointing & fault tolerance
- Performance Optimization
- 1. Job tuning, partitioning & bucketing
- 2. Caching & persistence strategies
- Spark Architecture & Execution Model
- 1. Distributed data processing concepts
- 2. Spark application lifecycle
- Spark SQL & DataFrames
- 1. DataFrame/Dataset API usage
- 2. Query optimization & execution plans
|
| Topic 5: Workflow Orchestration | 15% | - Pipeline Development
- 1. Workflow monitoring & alerting
- 2. End-to-end data pipeline design
- Apache Airflow
- 1. Incremental data extraction
- 2. DAG design & scheduling
- 3. Task dependencies & error handling
|
Cloudera CDP Data Engineer - Certification Sample Questions:
Your Spark application involves complex data transformations requiring multiple shuffles. How can you leverage Spark's in- memory caching mechanisms to improve performance?
A Use rdd.persist(StorageLevel.MEMORY_ONLY) for all intermediate RDDs
- A. Implement custom caching logic using in-memory data structures
- B. Tune Spark configuration parameters like spark.storage.memoryFraction
- C. Leverage Spark SQL caching with materialized views
Reveal Solution
Discussion
Correct Answer: B 🗳️
Explanation: Only visible for Free4Dump members. You can sign-up / login (it's free).
What is the purpose of the Airflow XCom feature?
- A. To enable communication between tasks in a DAG
- B. To monitor and log the execution of DAGs
- C. To schedule DAG runs at specific intervals
- D. To execute tasks on remote machines
Reveal Solution
Discussion
Correct Answer: A 🗳️
Explanation: Only visible for Free4Dump members. You can sign-up / login (it's free).
What is the key benefit of using the Cloudera Data Engineering service compared to building and managing data pipelines manually?
- A. Improved data security through proprietary tools.
- B. Cost savings due to open-source technology.
- C. Faster development time for simple data pipelines.
- D. Increased scalability, reliability, and manageability.
Reveal Solution
Discussion
Correct Answer: D 🗳️
Explanation: Only visible for Free4Dump members. You can sign-up / login (it's free).
You need to design a DAG that can be easily paused, resumed, or skipped based on specific conditions. How can you achieve this functionality?
- A. Utilize Airflow pools to dynamically control the execution of tasks based on resource availability.
- B. Implement custom logic within each task to check for pause or skip conditions.
- C. Leverage Airflow XCom to share information about the desired action (pause, resume, skip) between tasks.
- D. Configure the DAG to run continuously without any manual intervention.
Reveal Solution
Discussion
Correct Answer: C 🗳️
Explanation: Only visible for Free4Dump members. You can sign-up / login (it's free).
You're working with a complex DataFrame containing nested structures (e.g., arrays of structs). How can you access and manipulate data within these nested structures?
- A. Directly access elements using their position within the nested structure
- B. Leverage Spark SQL's built-in functions like explode and struct
- C. Implement custom recursive functions to navigate through the nested structure
- D. Convert the nested data into a simpler format like a single-level DataFrame
Reveal Solution
Discussion
Correct Answer: B 🗳️
Explanation: Only visible for Free4Dump members. You can sign-up / login (it's free).