Try and practice the latest DASCA : SDS real questions & answers

Last Updated: Aug 11, 2026

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DASCA SDS Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Foundational Methods & Frameworks25%- Machine learning algorithms, selection, and evaluation
- Advanced statistics, probability, and experimental design
- Data preparation, feature engineering, and quality assurance
Topic 2: MLOps & Production Deployment15%- Model lifecycle management, monitoring, and maintenance
- DevOps integration, automation, and CI/CD
- Model governance, security, and reliability
Topic 3: Data Science Applications in Business20%- Measuring and demonstrating ROI of data science initiatives
- Data science strategy and enterprise alignment
- Translating business requirements into data science problems
Topic 4: Advanced AI & Generative AI20%- Generative AI techniques, use cases, and limitations
- Responsible AI, ethics, fairness, transparency, and compliance
- Deep learning, neural networks, and foundation models
Topic 5: Big Data Ecosystems & Scalable Systems20%- Scalability, performance, and optimization
- Data storage, processing pipelines, and governance
- Distributed computing frameworks and cloud platforms

DASCA Senior Data Scientist Sample Questions:

1. Business Intelligence (BI) is:

A) BI focuses on descriptive analytics
B) Both B and C
C) Both A and B
D) BI focuses on "What happened?"
E) BI focuses on reporting on the future state of the business


2. Tar is an example of:

A) Archive file format
B) Text file format
C) CSV file format
D) ARV file format
E) None of the above


3. Which of the following is a Python library for fitting Bayesian networks to real data?

A) MyLib
B) SciLib
C) MCMC
D) SCIMC
E) PyMC


4. Which of the following is True about Time Series Analysis?

A) Predicting when/whether an event will occur, such as a failure of the machine generating the data
B) Projecting the value of the time series at future points in time, such as a stock whose price we want to predict
C) All of the above
D) Both A and B
E) Identifying interesting patterns in a corpus of time series data that is too large for a human to comb through


5. Which of the following architectural techniques is used for parallel processing?

A) The SuperVector Technique
B) The Superscalar Technique
C) Both B and C
D) Very Long Instruction Words (VLIW) Technique
E) Both A and B


Solutions:

Question # 1
Answer: C
Question # 2
Answer: A
Question # 3
Answer: E
Question # 4
Answer: C
Question # 5
Answer: E

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