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| Section | Objectives |
|---|---|
| AI Fundamentals & Business Applications | - AI fundamentals and architectures - AI applications in business operations |
| Data Preparation & Feature Engineering | - Data cleaning and preprocessing - Feature engineering techniques |
| Deployment & AI Operations | - Model monitoring and drift management - Model deployment pipelines |
| AI Strategy & Business Transformation | - AI-driven business use cases - ROI and value creation with AI |
| Model Development & Machine Learning | - Model training and optimization - Supervised and unsupervised learning |
| Model Evaluation & Metrics | - Model validation and selection - Performance metrics (precision, recall, F1, AUC-ROC) |
| Responsible AI & Ethics | - AI governance and compliance (e.g., GDPR) - Bias detection and mitigation |
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NEW QUESTION # 53
Choose the CORRECT example of Reinforcement Learning.
Answer: C
Explanation:
The correct answer is D. All of the above because robotics, game playing, and navigation are all common examples of reinforcement learning. Reinforcement learning is a machine learning approach in which an agent learns by interacting with an environment and receiving rewards or penalties based on its actions. Over time, the agent learns a policy that helps it maximize long-term reward.
Robotics is a strong example because robots can learn movement, object handling, path planning, and control actions through trial and feedback. Game playing is another classic reinforcement learning example because an AI agent can learn winning strategies by trying actions, observing outcomes, and improving decisions over repeated episodes. Navigation is also a valid example because an agent can learn the best route or movement strategy by receiving feedback about distance, obstacles, time, or success in reaching a goal.
Since all three listed options are valid applications of reinforcement learning, the correct answer is D. All of the above .
NEW QUESTION # 54
Which of the following is NOT a learning category for the ML model?
Answer: C
Explanation:
The correct answer is D. Semi Reinforcement learning because it is not commonly recognized as a standard learning category for machine learning models. The major machine learning categories include supervised learning, unsupervised learning, reinforcement learning, and semi-supervised learning. Supervised learning uses labeled datasets where the model learns from known input-output examples. Unsupervised learning uses unlabeled data to discover patterns, clusters, or hidden structures. Reinforcement learning trains an agent through interaction with an environment using rewards and penalties. Semi-supervised learning combines a small amount of labeled data with a larger amount of unlabeled data to improve learning when fully labeled datasets are limited.
"Semi Reinforcement learning" is not normally listed as a core ML learning category in standard AI and machine learning learning paths. Therefore, among the given options, the one that is NOT a learning category for the ML model is D. Semi Reinforcement learning .
NEW QUESTION # 55
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: C
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 # 56
Artificial general intelligence (AGI) is also commonly expressed as ____.
Answer: A
Explanation:
Artificial General Intelligence, or AGI, is commonly referred to as Strong AI because it describes an AI system with human-like cognitive ability across many different tasks and domains. Unlike narrow or weak AI, which is designed to perform a specific task such as image recognition, language translation, recommendation, fraud detection, or chatbot response generation, AGI would be able to understand, learn, reason, adapt, and solve problems broadly in a way similar to human intelligence.
Weak AI is incorrect because it refers to task-specific AI systems that operate within limited boundaries.
General AI is related in meaning, but the commonly used expression for AGI in AI classification is Strong AI.
SuperAI is different because it refers to intelligence that would exceed human intelligence, while ExpertAI is not the standard term for AGI. Therefore, the correct answer is B. Strong AI .
NEW QUESTION # 57
Which one of the following is a CORRECT benefit for using AI in product development?
Answer: B
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 # 58
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