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| Section | Objectives |
|---|---|
| AI Fundamentals and Data Systems | - Developing and deploying data systems related to AI - Fundamentals of Artificial Intelligence and Machine Learning |
| AI Solutions and Workflows | - Advanced robotics - AI workflows - Sentiment analysis - Fraud prevention with Cloud AI solutions |
| Deployment and Maintenance | - Development, deployment, testing, and maintenance of AI applications |
| Neural Networks and Technical Applications | - Enhancing approaches to problems such as translation, speech recognition, and image classification - Designing, developing, and troubleshooting technical applications on neural networks |
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NEW QUESTION # 16
Choose the CORRECT statement for Naive Bayes classifier.
Answer: D
Explanation:
The correct answer is D. a and c only . Naive Bayes is a supervised machine learning classification algorithm based on Bayes' theorem. It is called "naive" because it assumes that the features used for prediction are conditionally independent of one another, even though this may not always be fully true in real-world data.
Therefore, statement A is correct because the algorithm treats each feature as an independent variable when calculating class probabilities.
Statement C is also correct because Naive Bayes is commonly used for classification problems such as spam detection, where the model predicts whether an email is spam or not spam. It is also used in sentiment analysis, text classification, document categorization, and simple probabilistic classification tasks.
Statement B is not the best statement because the key idea is not about "unique features" specifically, but about the independence assumption applied to features. Therefore, the correct answer is D. a and c only .
NEW QUESTION # 17
Which of the CORRECT cognitive modeling is used in AI applications?
Answer: D
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 # 18
Unsupervised learning is a type of machine learning where the algorithm learns from a ______.
Answer: D
Explanation:
The correct answer is B. Unlabeled dataset . Unsupervised learning is a machine learning approach where the algorithm works with data that does not contain predefined labels, target outputs, or correct answers. Instead of being told what each data point represents, the model analyzes the structure of the data and identifies hidden patterns, groupings, similarities, or relationships on its own.
This type of learning is commonly used for clustering, association rule mining, anomaly detection, dimensionality reduction, and customer segmentation. For example, an unsupervised learning model may group customers based on buying behavior without being given category labels in advance.
A labeled dataset is used in supervised learning, where the model learns from input-output pairs. An
"explained dataset" is not a standard machine learning category. Since unsupervised learning specifically depends on unlabeled data, the correct answer is B. Unlabeled dataset .
NEW QUESTION # 19
Choose the CORRECT second step in the ML lifecycle?
Answer: C
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 # 20
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 # 21
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