Exam Name: Senior Data Scientist
Certification Provider: DASCA
Corresponding Certification: DASCA Data Scientist
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Updated: Aug 19, 2026
No. of Questions: 87 Questions & Answers with Testing Engine
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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: Big Data Ecosystems & Scalable Systems | 20% | - Scalability, performance, and optimization - Data storage, processing pipelines, and governance - Distributed computing frameworks and cloud platforms |
| Topic 3: Data Science Applications in Business | 20% | - Translating business requirements into data science problems - Data science strategy and enterprise alignment - Measuring and demonstrating ROI of data science initiatives |
| Topic 4: 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 |
| Topic 5: Foundational Methods & Frameworks | 25% | - Machine learning algorithms, selection, and evaluation - Data preparation, feature engineering, and quality assurance - Advanced statistics, probability, and experimental design |
DASCA Senior Data Scientist Sample Questions:
1. Which of the following is True about Time Series Analysis?
A) Both A and B
B) All of the above
C) Predicting when/whether an event will occur, such as a failure of the machine generating the data
D) Projecting the value of the time series at future points in time, such as a stock whose price we want to predict
E) Identifying interesting patterns in a corpus of time series data that is too large for a human to comb through
2. Example of amortized performance is:
A) Hadoop dictionaries
B) HDFS dictionaries
C) All of the above
D) MapReduce dictionaries
E) Python dictionaries
3. Maximum Likelihood Estimation (MLE) is a way to frame:
A) Both A and C
B) Small class of problems in Data Science
C) Large class of problems in HDFS
D) Small class of problems in HDFS
E) Large class of problems in Data Science
4. Machine learning can be categorized as:
A) Unsupervised learning
B) All of the above
C) Supervised learning
D) Reinforcement learning
5. Which of the following is NOT an example of the applications of neural networks?
A) Character recognition
B) Traveling salesman's problem
C) None of the above
D) Stock market prediction
E) Image compression
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: E | Question # 3 Answer: E | Question # 4 Answer: B | Question # 5 Answer: B |
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