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DASCA SDS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Advanced AI & Generative AI | 20% | - Responsible AI, ethics, fairness, transparency, and compliance - Generative AI techniques, use cases, and limitations - Deep learning, neural networks, and foundation models |
| Data Science Applications in Business | 20% | - Data science strategy and enterprise alignment - Measuring and demonstrating ROI of data science initiatives - Translating business requirements into data science problems |
| Big Data Ecosystems & Scalable Systems | 20% | - Distributed computing frameworks and cloud platforms - Data storage, processing pipelines, and governance - Scalability, performance, and optimization |
| Foundational Methods & Frameworks | 25% | - Advanced statistics, probability, and experimental design - Machine learning algorithms, selection, and evaluation - Data preparation, feature engineering, and quality assurance |
| MLOps & Production Deployment | 15% | - Model lifecycle management, monitoring, and maintenance - Model governance, security, and reliability - DevOps integration, automation, and CI/CD |
DASCA Senior Data Scientist Sample Questions:
1. Spark programs can be written in:
A) Scala
B) Java
C) None of the above
D) Python
E) All of the above
2. Which of the following is FALSE for Social Network Analysis (SNA)?
A) SNA is used to investigate social structures and relationships across social networks
B) Social Network Analysis (SNA) is an example of graph analysis
C) Social Network Analysis (SNA) is an example of trend analysis
D) SNA characterizes networked structures in terms of nodes and the ties or edges that connect them
E) None of the above
3. Which of the following is NOT a valid section of Big Data Strategy document?
A) Key business initiatives
B) Business decisions
C) Business strategy
D) Key decisions
E) Key business entities
4. JSON takes hierarchical data structures and serializes them into:
A) Both A and B
B) Plain string format
C) Any desired format
D) Plain text format
E) None of the above
5. Designing an algorithm to play chess is usually an example of which type of machine learning?
A) Supervised learning
B) Pattern density
C) Clustering
D) Reinforcement learning
Solutions:
| Question # 1 Answer: E | Question # 2 Answer: C | Question # 3 Answer: B | Question # 4 Answer: A | Question # 5 Answer: D |
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