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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Ethics, Governance & Future Trends | 10% | - AI Ethics, Bias & Fairness - Explainable AI & Transparency - Data Privacy, Security & Compliance - Emerging Trends & Future of AI |
| Topic 2: Data Preparation & Engineering | 15% | - Data Collection & Sources - Data Cleaning, Preprocessing & Transformation - Data Quality & Governance - Feature Engineering & Selection |
| Topic 3: AI Fundamentals & Concepts | 15% | - AI vs Traditional Programming - Introduction to Artificial Intelligence
|
| Topic 4: AI Technologies & Tools | 15% | - AI Applications: NLP, Computer Vision, Predictive Analytics - Automation & RPA Integration - Frameworks: TensorFlow, PyTorch, Scikit-learn - Cloud AI Platforms (AWS, Azure, Google Cloud) |
| Topic 5: Machine Learning & Algorithms | 20% | - Types of Machine Learning
|
| Topic 6: AI in Business & Consulting | 25% | - Cost-Benefit Analysis & ROI Calculation - AI Strategy & Roadmap Development - AI Project Management & Governance - Implementation Planning & Change Management - Identifying AI Use Cases & Opportunities |
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問題 #29
Choose the CORRECT example of Reinforcement Learning.
答案：E
解題說明：
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 .
問題 #30
Which of the following is the CORRECT basic brain components?
答案：E
解題說明：
The correct answer is E. All of the above because the listed terms are associated with major functional or structural areas of the human brain often discussed when explaining cognition, intelligence, decision-making, emotions, perception, and behavior. The neocortex is linked with higher-order thinking, reasoning, language, planning, perception, and conscious decision-making. The limbic system is strongly associated with emotion, memory, motivation, and behavioral responses. The midbrain is involved in sensory processing, movement control, visual and auditory responses, and alertness. The brainstem supports essential automatic functions such as breathing, heart rate, sleep-wake regulation, and basic survival processes.
In artificial intelligence learning, these brain components are often introduced to help explain how human intelligence works and how AI attempts to model certain cognitive functions. Since neocortex, limbic, midbrain, and brainstem are all valid basic brain-related components, the best answer is E. All of the above .
問題 #31
Which one of the following is a CORRECT benefit for using AI in product development?
答案：E
問題 #32
Choose the CORRECT reasons. We want to study AI to automate things, because
答案：D
解題說明：
The correct answer is E. All of the above because each statement gives a valid reason for studying and using AI to automate tasks. Modern organizations deal with massive volumes of data that are too large and complex for humans to process manually. AI helps analyze this data quickly, detect patterns, and support better decisions.
Statement B is also correct because data now comes from many sources at the same time, including sensors, applications, customers, transactions, machines, documents, and digital platforms. This data is often unstructured, noisy, and difficult to manage without intelligent automation. Statement C is correct because business knowledge must be updated continuously as data changes. AI systems can learn from new patterns and support faster adaptation. Statement D is also correct because many AI applications, such as robotics, autonomous systems, fraud detection, and industrial automation, require real-time sensing, decision-making, and precise action.
Since all four reasons support the need for AI-driven automation, the correct answer is E. All of the above .
問題 #33
Supervised learning is a type of machine learning where the algorithm learns from a ______.
答案：D
解題說明：
The correct answer is A. labeled dataset . Supervised learning is a machine learning method in which an algorithm is trained using data that already contains the correct output labels or target values. Each training example includes input features and a known answer, allowing the model to learn the relationship between the inputs and the expected output. Once trained, the model can use that learned relationship to classify or predict outcomes for new data.
An unlabeled dataset is used in unsupervised learning, where the model identifies hidden patterns, clusters, or relationships without predefined labels. An "explained dataset" is not a standard machine learning category.
Option D is incorrect because supervised learning does not learn from both labeled and unlabeled datasets as its primary definition. Option E is also incorrect because "explained dataset" is not the correct term.
Therefore, supervised learning learns from a labeled dataset , making A the correct answer.
問題 #34
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