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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Governance and MLOps | 15% | - Governance and Operations
|
| Topic 2: Machine Learning Model Optimization | 25% | - Model Tuning and Evaluation
|
| Topic 3: Deployment and Operationalization | 15% | - Model Deployment
|
| Topic 4: Data Engineering Pipeline Optimization | 25% | - Pipeline Design and Performance
|
| Topic 5: Data Preparation and Feature Engineering | 20% | - Feature Engineering Techniques
|
1. What is a common use case for a document-based NoSQL database like MongoDB?
A) Managing hierarchical data
B) Performing complex SQL queries
C) Storing structured tabular data
D) Tracking real-time data streams
2. Which of the following are data pipeline best practices? (Select all that apply)
A) Using a single tool for all pipeline components
B) Using a monolithic design for simplicity
C) Ensuring data quality and validation
D) Avoiding error handling and monitoring
3. Which of the following are key roles in data governance? (Select all that apply)
A) Data Owner
B) Chief Information Officer (CIO)
C) Data Scientist
D) Data Steward
E) Data Analyst
4. Which of the following are components of the Apache Spark Streaming architecture? (Select all that apply)
A) Worker node
B) Discretized streams (DStreams)
C) HDFS storage
D) Kafka topics
E) Driver node
5. What type of expertise is required to be an effective data engineer?
A) Accounting
B) Business analysis
C) Project management
D) Big Data tools
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: C | Question # 3 Answer: A,B,D | Question # 4 Answer: A,B,E | Question # 5 Answer: D |
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