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EMC D-DS-OP-23 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: Deployment and Operationalization | 15% | - Model Deployment
- 1. Monitor model performance and drift
- 2. Deploy machine learning models into production environments
- 3. Implement model lifecycle management practices
|
| Topic 2: Data Engineering Pipeline Optimization | 25% | - Pipeline Design and Performance
- 1. Implement scalable data processing architectures
- 2. Optimize data ingestion and transformation workflows
- 3. Evaluate data pipeline performance bottlenecks
|
| Topic 3: Data Preparation and Feature Engineering | 20% | - Feature Engineering Techniques
- 1. Evaluate data quality and feature effectiveness
- 2. Select appropriate data preprocessing methods
- 3. Optimize feature extraction and transformation
|
| Topic 4: Data Governance and MLOps | 15% | - Governance and Operations
- 1. Apply data governance principles
- 2. Implement MLOps workflows and automation
- 3. Ensure security, compliance, and reproducibility
|
| Topic 5: Machine Learning Model Optimization | 25% | - Model Tuning and Evaluation
- 1. Improve model accuracy and efficiency
- 2. Evaluate model performance metrics
- 3. Apply hyperparameter tuning techniques
|
EMC Dell Data Scientist and Data Engineering Optimize 2023 Sample Questions:
1. What is the fastest data transfer channel for the Flume Agent Architecture?
A) JDBC
B) File
C) Memory
D) Sink Processor
2. An organization wants to simplify the data ingestion configuration process through simple click, drag, and drop type actions.
Which tool provides an easy to use graphical user interface for building data pipeline data flows?
A) Apache Pig
B) Apache Hadoop
C) Apache Airflow
D) Apache Spark
3. Which layer in the EdgeX Foundry architecture provides a REST API that enables clients to establish new requests?
A) Device Services
B) Support Services
C) Application/Export Services
D) Core Services
4. What is a common use case for a document-based NoSQL database like MongoDB?
A) Performing complex SQL queries
B) Managing hierarchical data
C) Tracking real-time data streams
D) Storing structured tabular data
5. Which skill is crucial for a data engineer to effectively manage and optimize large-scale data processing systems?
A) Cloud computing
B) Graphic design
C) Front-end development
D) Data analysis
Solutions:
Question # 1 Answer: C | Question # 2 Answer: C | Question # 3 Answer: C | Question # 4 Answer: B | Question # 5 Answer: A |