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DASCA SDS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Big Data Ecosystems & Scalable Systems | 20% | - Distributed computing frameworks and cloud platforms - Data storage, processing pipelines, and governance - Scalability, performance, and optimization |
| Topic 2: Advanced AI & Generative AI | 20% | - Responsible AI, ethics, fairness, transparency, and compliance - Deep learning, neural networks, and foundation models - Generative AI techniques, use cases, and limitations |
| Topic 3: Foundational Methods & Frameworks | 25% | - Machine learning algorithms, selection, and evaluation - Data preparation, feature engineering, and quality assurance - Advanced statistics, probability, and experimental design |
| Topic 4: Data Science Applications in Business | 20% | - Measuring and demonstrating ROI of data science initiatives - Data science strategy and enterprise alignment - Translating business requirements into data science problems |
| Topic 5: MLOps & Production Deployment | 15% | - DevOps integration, automation, and CI/CD - Model lifecycle management, monitoring, and maintenance - Model governance, security, and reliability |
DASCA Senior Data Scientist Sample Questions:
1. Which of the following is an SLAs specification in case of Internet Service Provider (ISP)?
A) All of the above
B) Mean Time Between Failures (MTBF)
C) Mean Time To Recovery (MTTR)
D) Turnaround Time (TAT)
2. Which of the following is TRUE for Chief Data Monetization Officer (CDMO)?
i. CDMO should focus on driving and deriving value from the organization's data and analytic assets.
ii. The CDMO should own the organization's investment decisions with respect to data and analytics.
iii. CDMO should have revenue and margin responsibilities.
A) All of the above
B) i, ii
C) ii, iii
3. The DevOps movement is an outgrowth of which of the following software development methodologies?
A) Agile
B) Test-driven development and model-driven development
C) Waterfall
D) Promise-based algorithms
4. In unsupervised learning, learning takes place by based on these deductions in input data and developing patterns:
A) Detecting regularities
B) Detecting irregularities
C) None of the above
D) Both A and B
5. Exploratory analytic algorithms help the Data Science team to better:
A) Gain a high-level understanding of relationships
B) All of the above
C) Understand the data content
D) Both A and B
E) Understand patterns in the data
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: A | Question # 3 Answer: A | Question # 4 Answer: D | Question # 5 Answer: B |
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