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DASCA SDS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Big Data Ecosystems & Scalable Systems | 20% | - Scalability, performance, and optimization - Distributed computing frameworks and cloud platforms - Data storage, processing pipelines, and governance |
| Data Science Applications in Business | 20% | - Measuring and demonstrating ROI of data science initiatives - Translating business requirements into data science problems - Data science strategy and enterprise alignment |
| MLOps & Production Deployment | 15% | - Model lifecycle management, monitoring, and maintenance - Model governance, security, and reliability - DevOps integration, automation, and CI/CD |
| Foundational Methods & Frameworks | 25% | - Data preparation, feature engineering, and quality assurance - Machine learning algorithms, selection, and evaluation - Advanced statistics, probability, and experimental design |
| Advanced AI & Generative AI | 20% | - Deep learning, neural networks, and foundation models - Responsible AI, ethics, fairness, transparency, and compliance - Generative AI techniques, use cases, and limitations |
DASCA Senior Data Scientist Sample Questions:
Which of the following is NOT an example of graphical model?
- A. Flow charts
- B. Computer networks
- C. Electrical circuits
- D. Road maps
- E. Geographical networks
Correct Answer: A 🗳️
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The Big Data Vision Workshop process is ideal for organizations who:
- A. Have a desire to leverage Big Data to transform their business but do not know where and how to start
- B. Both A and B
- C. Have a wealth of data that they do not know how to monetize
- D. Have a desire to leverage the Big Data Vision Workshop to identify where and how to leverage data and analytics to power their business models
- E. All of the above
Correct Answer: E 🗳️
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Machine learning can be categorized as:
- A. Supervised learning
- B. Reinforcement learning
- C. Unsupervised learning
- D. All of the above
Correct Answer: D 🗳️
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ElementTree sub-library gives us direct access to:
- A. Insert tree of the XML
- B. Copy tree of the XML
- C. Parse tree of the XML
- D. Delete tree of the XML
- E. None of the above
Correct Answer: C 🗳️
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In unsupervised learning, learning takes place by based on these deductions in input data and developing patterns:
- A. Both A and B
- B. Detecting regularities
- C. None of the above
- D. Detecting irregularities
Correct Answer: A 🗳️
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