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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Technologies & Tools | 15% | - Cloud AI Platforms (AWS, Azure, Google Cloud) - AI Applications: NLP, Computer Vision, Predictive Analytics - Frameworks: TensorFlow, PyTorch, Scikit-learn - Automation & RPA Integration |
| Topic 2: Ethics, Governance & Future Trends | 10% | - Data Privacy, Security & Compliance - Emerging Trends & Future of AI - AI Ethics, Bias & Fairness - Explainable AI & Transparency |
| Topic 3: Data Preparation & Engineering | 15% | - Data Cleaning, Preprocessing & Transformation - Data Quality & Governance - Feature Engineering & Selection - Data Collection & Sources |
| Topic 4: Machine Learning & Algorithms | 20% | - Types of Machine Learning
|
| Topic 5: AI Fundamentals & Concepts | 15% | - Introduction to Artificial Intelligence
- AI vs Traditional Programming |
| Topic 6: AI in Business & Consulting | 25% | - Identifying AI Use Cases & Opportunities - Cost-Benefit Analysis & ROI Calculation - AI Project Management & Governance - AI Strategy & Roadmap Development - Implementation Planning & Change Management |
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NEW QUESTION # 24
An AI agent learns to play a game by taking actions, receiving rewards for good moves, and penalties for poor moves. Over time, it improves its strategy to maximize total reward. This is an example of ______.
Answer: C
Explanation:
Reinforcement learning is the correct answer because the AI agent learns by interacting with an environment and improving its behavior based on rewards and penalties. The goal of reinforcement learning is to learn a policy or strategy that maximizes cumulative reward over time. This differs from supervised learning, where the model learns from labeled input-output examples. It also differs from unsupervised learning, where the model searches for hidden patterns without labels or rewards. Semi-supervised learning is incorrect because the scenario does not involve a mix of labeled and unlabeled data. Regression learning is also incorrect because regression predicts continuous numerical values, while this example focuses on action selection and reward optimization. Therefore, the correct answer is C. reinforcement learning .
NEW QUESTION # 25
Which one of the following is a CORRECT benefit for using AI in product development?
Answer: D
Explanation:
The correct answer is D. a and b only because AI provides strong benefits across the product development life cycle, especially by improving speed, decision quality, and data-driven design. Statement A is correct because AI can shorten the product development life cycle by automating research, analyzing customer feedback, generating product ideas, supporting rapid prototyping, improving testing, and helping teams identify risks or opportunities earlier.
Statement B is also correct because applying AI throughout the PDLC helps organizations use data consistently at every stage, from ideation and market research to design, testing, launch, and post-launch improvement. This means products are not only based on data at the beginning but continue to reflect data- driven insights throughout development.
Statement C is not the best answer because "increase the product feature" is unclear and grammatically incomplete. AI may help improve features or identify new feature opportunities, but the statement is not as accurate as A and B. Therefore, the best answer is D. a and b only .
NEW QUESTION # 26
Which of the CORRECT cognitive modeling is used in AI applications?
Answer: B
Explanation:
The correct answer is E. All of the above because deep learning, expert systems, natural language processing, and robotics are all connected with AI applications that support or model intelligent behavior. Cognitive modeling in AI is concerned with building systems that can represent, simulate, or support human-like capabilities such as learning, reasoning, decision-making, perception, language understanding, and action.
Deep learning is used to recognize patterns from large amounts of data and is common in speech recognition, image analysis, recommendation systems, and generative AI. Expert systems use knowledge bases and rules to support decision-making in specialized domains. Natural language processing helps AI systems understand, interpret, generate, and respond to human language. Robotics applies AI to physical systems so machines can sense, plan, move, and perform tasks in real-world environments.
Since all the listed options are valid AI application areas related to intelligent and cognitive capabilities, the correct answer is E. All of the above .
NEW QUESTION # 27
A retail company has a large dataset of customer purchases but no predefined labels. The AI system groups customers into segments based on similar buying behavior. This is an example of ______.
Answer: B
Explanation:
Unsupervised learning is the correct answer because the dataset does not contain predefined labels or known target outcomes. The AI system is identifying natural patterns in the data and grouping customers with similar purchasing behavior. This type of task is commonly called clustering, which is one of the most common applications of unsupervised learning. Supervised learning is incorrect because there are no labeled examples telling the model which customer belongs to which segment. Reinforcement learning is incorrect because the system is not learning through rewards or penalties. Transfer learning involves reusing knowledge from one trained model for another related task, which is not described here. Semi-supervised learning would involve both labeled and unlabeled data, but this scenario only mentions unlabeled data. Therefore, the correct answer is B. unsupervised learning .
NEW QUESTION # 28
Which of the following is NOT CORRECT for the Elbow method?
Answer: B
Explanation:
The correct answer is E. None of the above because all three statements about the Elbow method are correct.
The Elbow method is commonly used in unsupervised learning, especially with K-means clustering, to help estimate an appropriate number of clusters. It works by running clustering with different values of K and measuring the within-cluster variation or distortion. As K increases, the error usually decreases, but after a certain point the improvement becomes much smaller. That point is visually interpreted as the "elbow." Statement A is correct because the Elbow method helps determine how many clusters should be formed.
Statement B is also correct because it is widely used with K-means clustering to select a suitable value of K.
Statement C is correct because the method is a heuristic, meaning it is a practical estimation technique rather than an exact mathematical guarantee. Since A, B, and C are all correct, none of them is NOT correct.
Therefore, the correct answer is E. None of the above .
NEW QUESTION # 29
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