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Instant Download DASCA : SDS Questions & Answers as PDF & Test Engine
- Exam Code: SDS
- Exam Name: Senior Data Scientist
- Updated: Sep 04, 2026
- No. of Questions: 87 Questions and Answers
- Download Limit: Unlimited
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DASCA SDS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Advanced AI & Generative AI | 20% | - Deep learning, neural networks, and foundation models - Generative AI techniques, use cases, and limitations - Responsible AI, ethics, fairness, transparency, and compliance |
| Foundational Methods & Frameworks | 25% | - Data preparation, feature engineering, and quality assurance - Machine learning algorithms, selection, and evaluation - Advanced statistics, probability, and experimental design |
| Big Data Ecosystems & Scalable Systems | 20% | - Distributed computing frameworks and cloud platforms - Scalability, performance, and optimization - Data storage, processing pipelines, and governance |
| MLOps & Production Deployment | 15% | - DevOps integration, automation, and CI/CD - Model lifecycle management, monitoring, and maintenance - Model governance, security, and reliability |
| 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 |
DASCA Senior Data Scientist Sample Questions:
Question 1
The aim of developing Scribe was to offer reliability with using:
A. Extensive protocols
B. Complex protocols
C. None of the above
D. High disk space usage
Question 2
Which of the following is a trend analysis component of time series decomposition?
A. Seasonal
B. Irregular
C. Cyclical
D. Both A and B
E. All of the above
Question 3
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. Have a wealth of data that they do not know how to monetize
C. 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
D. Both A and B
E. All of the above
Question 4
Data wrangling is the process of getting the data from:
A. Its modified meaning format into something suitable for more conventional analytics
B. None of the above
C. Its raw format into something suitable for more conventional analytics
D. Both A and B
Question 5
Self-driving car is an example of:
A. Supervised learning
B. Unsupervised learning
C. Reinforcement learning
D. All of the above
Solutions:
| Question 1 Answer: C | Question 2 Answer: E | Question 3 Answer: E | Question 4 Answer: C | Question 5 Answer: C |
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