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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Processing Frameworks | 22% | - Apache Spark optimization - Batch and real-time processing optimization - Hadoop ecosystem tuning |
| Topic 2: Governance, Security & Compliance | 15% | - Security and privacy protection - Data governance policies - Regulatory compliance |
| Topic 3: Storage & Data Management | 18% | - Data formats, partitioning, and indexing - Distributed storage system optimization - SQL/NoSQL performance tuning |
| Topic 4: Data Pipeline Optimization | 20% | - Reduce latency and improve throughput - ETL/ELT workflow optimization - Design scalable and efficient data pipelines |
| Topic 5: Monitoring, Troubleshooting & Workload Management | 10% | - Performance monitoring and logging - Cluster resource allocation - Bottleneck identification and resolution |
| Topic 6: Streaming & IoT Data Processing | 15% | - Pravega, Kafka, NiFi usage - Real-time ingestion frameworks - Stream processing optimization |
1. What is a characteristic of Pig?
A) Performs real-time reads and writes in HDFS
B) Data warehouse infrastructure that manages jobs in the cluster
C) Uses HiveQL to translate SQL queries into MapReduce jobs
D) Alternative language to Java programming for MapReduce
2. Which layer in the EdgeX Foundry architecture provides a REST API that enables clients to establish new requests?
A) Core Services
B) Support Services
C) Application/Export Services
D) Device Services
3. Match each data type with its corresponding Redis data structure.
4. What skill is essential for a data engineer to efficiently transform and clean raw data into usable formats?
A) Machine learning
B) ETL (Extract, Transform, Load) processes
C) Data warehousing
D) Data visualization
5. Match each Apache Storm component with its description.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C | Question # 3 Answer: Only visible for members | Question # 4 Answer: B | Question # 5 Answer: Only visible for members |
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