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DASCA Senior Data Scientist : SDS
- Exam Code: SDS
- Exam Name: Senior Data Scientist
- Updated: Aug 06, 2026
- Q & A: 87 Questions and Answers
About DASCA Senior Data Scientist : SDS Exam
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DASCA SDS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: MLOps & Production Deployment | 15% | - Model governance, security, and reliability - DevOps integration, automation, and CI/CD - Model lifecycle management, monitoring, and maintenance |
| Topic 2: Foundational Methods & Frameworks | 25% | - Advanced statistics, probability, and experimental design - Data preparation, feature engineering, and quality assurance - Machine learning algorithms, selection, and evaluation |
| Topic 3: Big Data Ecosystems & Scalable Systems | 20% | - Distributed computing frameworks and cloud platforms - Scalability, performance, and optimization - Data storage, processing pipelines, and governance |
| Topic 4: 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 5: 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 |
DASCA Senior Data Scientist Sample Questions:
1. Which of the following is main Machine Learning Library in Python?
A) NumPy
B) None of the above
C) SciPy
D) Scikit-learn
E) Matplotlib
2. Which of the following phases is NOT a Big Data Business Model Maturity Index?
A) Data Monetization
B) Business Metamorphosis
C) Business Strategy
D) Business Monitoring
E) Business Optimization
3. Which of the following is TRUE for "By" analysis?
A) "By" analysis is used to create a collaborative technique to drive alignment between the business users and the data scientists to identify and brainstorm variables and metrics that might be better predictors of business performance.
B) All of the above
C) "By" analysis is a technique by which business subject matter experts (SMEs) and the Data Science team could collaborate to uncover new variables and metrics that might be better predictors of business performance.
D) The "By" analysis technique reinforces the process of "thinking like a data scientist."
E) Both B and C
4. ARIMA model is:
A) Autointeractive moving average
B) All of the above
C) Autoreactive moving average
D) Autoregressive moving average
E) Autoresponsive moving average
5. Which of the following statements is correct?
A) Apache claimed that Spark is able to run parallel jobs 50 times faster in memory and 5 times faster on disk in comparison to the traditional Hadoop MapReduce
B) Apache claimed that Spark is able to run parallel jobs 100 times faster in memory and 10 times faster on disk in comparison to the traditional Hadoop MapReduce
C) Apache claimed that Spark is able to run parallel jobs 10 times faster in memory and 100 times faster on disk in comparison to the traditional Hadoop MapReduce
D) Apache claimed that Spark is able to run parallel jobs 1000 times faster in memory and 100 times faster on disk in comparison to the traditional Hadoop MapReduce
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C | Question # 3 Answer: B | Question # 4 Answer: D | Question # 5 Answer: B |
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