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| Section | Weight | Objectives |
|---|---|---|
| Foundational Methods & Frameworks | 25% | - Data preparation, feature engineering, and quality assurance - Machine learning algorithms, selection, and evaluation - Advanced statistics, probability, and experimental design |
| 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 |
| Big Data Ecosystems & Scalable Systems | 20% | - Scalability, performance, and optimization - Distributed computing frameworks and cloud platforms - Data storage, processing pipelines, and governance |
| MLOps & Production Deployment | 15% | - Model lifecycle management, monitoring, and maintenance - Model governance, security, and reliability - DevOps integration, automation, and CI/CD |
1. Spark programs can be written in:
A) Scala
B) None of the above
C) Python
D) Java
E) All of the above
2. Which of the following is a "thinking like a data scientist" decomposition process?
A) Business Initiative
B) Both B and C
C) Strategic Nouns
D) Business Stakeholder
E) All of the above
3. Which of the following is a trend analysis component of time series decomposition?
A) Both A and B
B) Seasonal
C) Irregular
D) Cyclical
E) All of the above
4. Image files can be broken down into two broad categories:
i. Rasterized
ii. Vectorized
iii. Sectorized
A) i, iii
B) None of the above
C) i, ii
D) ii, iii
5. Which of the following is NOT a cluster management tool?
A) Apache Ambari
B) Zettaset Orchestrator
C) Apache Hadoop
D) Apache Mesos
Solutions:
| Question # 1 Answer: E | Question # 2 Answer: E | Question # 3 Answer: E | Question # 4 Answer: C | Question # 5 Answer: C |
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