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DASCA SDS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Big Data Engineering | 15-20% | - Cloud Computing for Data Science
|
| Topic 2: Advanced Statistical Modeling | 20-25% | - Regression Analysis
|
| Topic 3: Foundations of Data Science | 15-20% | - Data Science Ethics
|
| Topic 4: Machine Learning at Scale | 25-30% | - Supervised Learning
|
| Topic 5: Data Visualization and Communication | 10-15% | - Visualization Principles
|
DASCA Senior Data Scientist Sample Questions:
1. The spokes of the "Hub and Spoke" analytics architecture are the analytic use cases or applications that help the organization to optimize:
A) Key business processes
B) Uncover new monetization opportunities
C) All of the above
D) Both A and B
E) Deliver a more compelling customer experience
2. Which of the following is NOT used to implement Agile?
A) Scrum
B) Kanban
C) Extreme Programming (XP)
D) Six Sigma
3. The aim of developing Scribe was to offer reliability with using:
A) High disk space usage
B) Extensive protocols
C) Complex protocols
D) None of the above
4. Spark is written in:
A) Python
B) Java
C) C
D) C++
E) Scala
5. Which of the following statements is correct?
A) 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
B) 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
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 10 times faster in memory and 100 times faster on disk in comparison to the traditional Hadoop MapReduce
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: E | Question # 5 Answer: C |




