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DASCA SDS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: MLOps & Production Deployment | 15% | - Model governance, security, and reliability - Model lifecycle management, monitoring, and maintenance - DevOps integration, automation, and CI/CD |
| Topic 2: Big Data Ecosystems & Scalable Systems | 20% | - Data storage, processing pipelines, and governance - Scalability, performance, and optimization - Distributed computing frameworks and cloud platforms |
| Topic 3: 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 |
| Topic 4: Foundational Methods & Frameworks | 25% | - Machine learning algorithms, selection, and evaluation - Advanced statistics, probability, and experimental design - Data preparation, feature engineering, and quality assurance |
| Topic 5: 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 |
DASCA Senior Data Scientist Sample Questions:
Question 1
A burn down chart shows:
A. The number of hours worked after dark
B. The declining energy of the team
C. The rate of reduction of budget for a project
D. The volume of work and features completed
Question 2
Which of the following can visualize variations in the base data, which can be used to identify outliers in the data for further investigation?
A. Histogram
B. Trend Analysis
C. Scatter Plot
D. None of the above
E. Box Plots
Question 3
Exploratory analytic algorithms help the Data Science team to better:
A. Understand patterns in the data
B. Gain a high-level understanding of relationships
C. All of the above
D. Understand the data content
E. Both A and B
Question 4
Which of the following is NOT a correct situation to use Agile?
A. When changes need to be implemented during the entire process
B. When clients/stakeholders need to be able to change the scope
C. None of the above
D. When the final product isn't clearly defined
Question 5
Which of the following is NOT an example of the applications of neural networks?
A. Stock market prediction
B. None of the above
C. Character recognition
D. Image compression
E. Traveling salesman's problem
Solutions:
| Question 1 Answer: D | Question 2 Answer: E | Question 3 Answer: C | Question 4 Answer: C | Question 5 Answer: E |

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