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DASCA SDS Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Artificial Intelligence Applications | - NLP and Computer Vision Overview - Deep Learning Fundamentals |
| Machine Learning and Predictive Modeling | - Unsupervised Learning - Supervised Learning - Model Evaluation and Validation |
| Mathematics and Statistics for Data Science | - Linear Algebra and Optimization - Probability and Statistical Inference |
| Big Data Engineering and Technologies | - Distributed Computing Concepts - Data Pipelines and ETL |
| Data Management and Governance | - Data Privacy and Security Principles - Data Quality and Lifecycle Management |
DASCA Senior Data Scientist Sample Questions:
1. Self-driving car is an example of:
A) All of the above
B) Reinforcement learning
C) Supervised learning
D) Unsupervised learning
2. Which of the following is a "thinking like a data scientist" decomposition process?
A) Strategic Nouns
B) Business Initiative
C) All of the above
D) Business Stakeholder
E) Both B and C
3. Bernoulli random variable is a type of:
A) Continuous random variable
B) Sometimes Discrete or sometimes Continuous random variable
C) Both A and B
D) Discrete random variable
4. Which of the following is NOT Structured data?
A) XML
B) CSV files
C) Relational databases
D) None of the above
E) Data tables
5. Which of the following standardizes scores similar to a percentile rank but preserves equal interval properties of a Z-score?
A) Normal Curve Equivalent (NCE)
B) High Curve Equivalent (HCE)
C) Medium Curve Equivalent (MCE)
D) Trend analysis
E) None of the above
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: C | Question # 3 Answer: D | Question # 4 Answer: A | Question # 5 Answer: A |






