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DASCA SDS Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Data Management and Governance | - Data Quality and Lifecycle Management - Data Privacy and Security Principles |
| Topic 2: Mathematics and Statistics for Data Science | - Linear Algebra and Optimization - Probability and Statistical Inference |
| Topic 3: Artificial Intelligence Applications | - NLP and Computer Vision Overview - Deep Learning Fundamentals |
| Topic 4: Big Data Engineering and Technologies | - Distributed Computing Concepts - Data Pipelines and ETL |
| Topic 5: Machine Learning and Predictive Modeling | - Supervised Learning - Model Evaluation and Validation - Unsupervised Learning |
DASCA Senior Data Scientist Sample Questions:
Which of the following standardizes scores similar to a percentile rank but preserves equal interval properties of a Z-score?
- A. Medium Curve Equivalent (MCE)
- B. Normal Curve Equivalent (NCE)
- C. None of the above
- D. Trend analysis
- E. High Curve Equivalent (HCE)
Correct Answer: B 🗳️
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Which of the following is NOT a part of Internal Process Optimization?
- A. Business Monitoring
- B. Business Metamorphosis
- C. Business Insights
- D. Business Optimization
- E. None of the above
Correct Answer: B 🗳️
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Self-driving car is an example of:
- A. Supervised learning
- B. Unsupervised learning
- C. All of the above
- D. Reinforcement learning
Correct Answer: D 🗳️
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Spark programs can be written in:
- A. Java
- B. Python
- C. Scala
- D. All of the above
- E. None of the above
Correct Answer: D 🗳️
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Which of the following errors refers to the wrong negation of a true null hypothesis?
- A. Logical Error
- B. Type I Error
- C. Hypothesis Error
- D. None of the above
- E. Type II Error
Correct Answer: B 🗳️
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