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DASCA SDS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Big Data Engineering | 15-20% | - Distributed Computing
|
| Machine Learning at Scale | 25-30% | - Deep Learning Fundamentals
|
| Foundations of Data Science | 15-20% | - Data Science Ethics
|
| Advanced Statistical Modeling | 20-25% | - Statistical Inference
|
| Data Visualization and Communication | 10-15% | - Tools and Technologies
|
DASCA Senior Data Scientist Sample Questions:
1. Exploratory analytic algorithms help the Data Science team to better:
A) Both A and B
B) Understand the data content
C) All of the above
D) Gain a high-level understanding of relationships
E) Understand patterns in the data
2. Which of the following is NOT an example of graphical model?
A) Geographical networks
B) Electrical circuits
C) Flow charts
D) Computer networks
E) Road maps
3. Which of the following is a "thinking like a data scientist" decomposition process?
A) Business Initiative
B) All of the above
C) Both B and C
D) Business Stakeholder
E) Strategic Nouns
4. Which of these statements reflects a null hypothesis?
A) Men will score higher than women on spatial awareness
B) Women will score higher than men on empathy
C) There will be no relationship between caffeine consumption and performance
D) There will be a significant difference between group 1 and group 2
E) As temperature increases, so too will the level of aggression
5. Which of the following is TRUE for Tensor?
A) In Tensor, there can be arbitrarily many dimensions to the array
B) All of the above
C) Tensor is used to describe multidimensional arrays of numbers on which we perform linear operations
D) Both B and C
E) Tensor is an array of floating-point numbers
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: C | Question # 3 Answer: B | Question # 4 Answer: C | Question # 5 Answer: B |








