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DASCA SDS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Big Data Ecosystems & Scalable Systems | 20% | - Distributed computing frameworks and cloud platforms - Scalability, performance, and optimization - Data storage, processing pipelines, and governance |
| Data Science Applications in Business | 20% | - Measuring and demonstrating ROI of data science initiatives - Translating business requirements into data science problems - Data science strategy and enterprise alignment |
| Advanced AI & Generative AI | 20% | - Generative AI techniques, use cases, and limitations - Deep learning, neural networks, and foundation models - Responsible AI, ethics, fairness, transparency, and compliance |
| MLOps & Production Deployment | 15% | - Model governance, security, and reliability - Model lifecycle management, monitoring, and maintenance - DevOps integration, automation, and CI/CD |
| Foundational Methods & Frameworks | 25% | - Data preparation, feature engineering, and quality assurance - Advanced statistics, probability, and experimental design - Machine learning algorithms, selection, and evaluation |
DASCA Senior Data Scientist Sample Questions:
1. Which of the following is TRUE for Tensor?
A) Tensor is an array of floating-point numbers
B) All of the above
C) In Tensor, there can be arbitrarily many dimensions to the array
D) Tensor is used to describe multidimensional arrays of numbers on which we perform linear operations
E) Both B and C
2. What is Scrum?
A) Scrum and Agile are the same
B) None of the above
C) Agile is a subset of Scrum
D) Scrum is a subset of Agile
3. Which of the following is NOT a main data container in Python?
A) Lists
B) LinkedList
C) Tuples
D) Dict
4. Which of the following can visualize variations in the base data, which can be used to identify outliers in the data for further investigation?
A) None of the above
B) Histogram
C) Trend Analysis
D) Scatter Plot
E) Box Plots
5. Which of the following statements is correct?
A) 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
B) 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
C) 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
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: B | Question # 2 Answer: D | Question # 3 Answer: B | Question # 4 Answer: E | Question # 5 Answer: C |
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