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| Section | Objectives |
|---|---|
| Topic 1: Responsible AI & Ethics | - AI governance and compliance (e.g., GDPR) - Bias detection and mitigation |
| Topic 2: Model Development & Machine Learning | - Supervised and unsupervised learning - Model training and optimization |
| Topic 3: Model Evaluation & Metrics | - Model validation and selection - Performance metrics (precision, recall, F1, AUC-ROC) |
| Topic 4: Data Preparation & Feature Engineering | - Data cleaning and preprocessing - Feature engineering techniques |
| Topic 5: Deployment & AI Operations | - Model monitoring and drift management - Model deployment pipelines |
| Topic 6: AI Strategy & Business Transformation | - AI-driven business use cases - ROI and value creation with AI |
| Topic 7: AI Fundamentals & Business Applications | - AI fundamentals and architectures - AI applications in business operations |
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NEW QUESTION # 24
Artificial narrow intelligence ANI is also commonly expressed as ____.
Answer: E
Explanation:
The correct answer is A. Weak AI . Artificial Narrow Intelligence, or ANI, is commonly called Weak AI because it is designed to perform a specific task or a limited set of tasks within a defined domain. Examples include recommendation engines, search engines, spam filters, facial recognition systems, voice assistants, fraud detection tools, and chatbots. These systems can perform their assigned functions effectively, but they do not possess general intelligence, consciousness, self-awareness, or human-like understanding across all domains.
Strong AI and General AI refer to Artificial General Intelligence, which would be capable of broad reasoning, learning, and problem-solving across many tasks like a human. SuperAI refers to a theoretical level of intelligence beyond human capability. ExpertAI is not the standard expression for ANI. Since ANI is task- specific and limited in scope, it is correctly expressed as Weak AI .
NEW QUESTION # 25
A healthcare organization has a small number of labeled medical images and a much larger number of unlabeled images. The AI model uses both datasets to improve disease classification accuracy. This is an example of ______.
Answer: B
Explanation:
Semi-supervised learning is the correct answer because the model is trained using a combination of labeled and unlabeled data. This approach is useful when labeled data is expensive, time-consuming, or difficult to obtain, which is common in healthcare because medical images often require expert annotation. The small labeled dataset provides guidance, while the larger unlabeled dataset helps the model learn broader patterns and improve classification performance. Supervised learning is not the best answer because the scenario does not rely only on labeled data. Unsupervised learning is incorrect because the goal is disease classification, and some labeled examples are available. Reinforcement learning is incorrect because there are no rewards, actions, or environment-based feedback. Rule-based learning is also incorrect because the model is learning from data, not from manually coded rules. Therefore, the correct answer is D. semi-supervised learning .
NEW QUESTION # 26
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: E
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 # 27
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 # 28
Choose the CORRECT second step in the ML lifecycle?
Answer: E
Explanation:
The correct answer is B. Data understanding . In the machine learning lifecycle, after the initial data acquisition or data collection stage, the next major activity is to understand the available data. Data understanding involves exploring the dataset, reviewing data sources, identifying variables, checking patterns, finding missing values, detecting outliers, and understanding whether the data is suitable for the business or operational problem being solved.
Data acquisition is usually an earlier step because the data must first be collected or accessed before it can be analyzed. Data preparation comes after data understanding because teams need to know the data's structure, quality, gaps, and relevance before cleaning, transforming, engineering features, or formatting it for model training. Options D and E are not correct because the question asks for the single second step, not a combination of lifecycle activities. Therefore, the correct second step in the ML lifecycle is B. Data understanding .
NEW QUESTION # 29
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