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NEW QUESTION # 27
Choose the CORRECT statement for Naive Bayes classifier.
Answer: E
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 # 28
Choose the CORRECT example of a business goal?
Answer: A
Explanation:
A business goal is a measurable outcome that an organization wants to achieve through strategy, operations, technology, or transformation initiatives. In artificial intelligence and business analytics contexts, common business goals include reducing operating costs, minimizing risks, improving customer or product outcomes, and increasing revenue. Cost reduction for operational processes is a valid business goal because AI can automate tasks, optimize resources, and reduce inefficiencies. Mitigation of business or operational risks is also a valid goal because AI can support fraud detection, compliance monitoring, anomaly detection, and predictive risk analysis. Product or service revenue improvement is another valid goal because AI can help personalize offerings, improve pricing, identify market opportunities, and increase customer value.
Since all three listed choices represent legitimate business goals that can guide AI initiatives and business transformation, the most complete and correct option is E. All of the above .
NEW QUESTION # 29
Which of the following is CORRECT for Support Vector Machine SVM?
Answer: A
Explanation:
The correct answer is D. a and b only . Support Vector Machine, or SVM, is a supervised machine learning algorithm widely used for classification problems. It works by finding the best separating boundary, called a hyperplane, between different classes in the dataset. The goal is to maximize the margin between the closest data points of each class, known as support vectors, so the model can classify new data more effectively.
Statement B is also correct because SVM can use kernel methods to transform data into higher-dimensional spaces. This helps make complex or non-linearly separable data easier to separate. For example, when data cannot be clearly grouped in a two-dimensional view, a kernel function can map it into a higher-dimensional feature space where a better separating hyperplane may be found.
Statement C is incorrect because SVM does allow dimensional transformation through kernel techniques.
Therefore, the correct choice is D. a and b only .
NEW QUESTION # 30
Select the BEST choice for ML solutions architecture coverage.
Answer: A
Explanation:
The correct answer is E. a, b and c only because ML solution architecture must cover the complete path from business need to technical implementation. Business understanding is essential because an ML solution should begin with a clear problem statement, business objective, success criteria, expected value, and operational impact. Without business understanding, the model may solve the wrong problem or fail to create measurable value.
Identification and verification of ML techniques are also part of ML solution architecture because teams must choose suitable algorithms, validate model approaches, compare methods, and confirm that the selected technique fits the data, use case, performance expectations, and business constraints. System architecture of the ML technology platform is equally important because ML solutions require data pipelines, infrastructure, compute resources, model deployment environments, monitoring, security, scalability, and integration with enterprise systems.
Since all three areas are important parts of ML solution architecture coverage, the best answer is E .
NEW QUESTION # 31
What type of learning is used when a model is trained with labeled data?
Answer: B
Explanation:
The correct answer is B. Supervised Learning . Supervised learning is the machine learning approach used when a model is trained with labeled data. Labeled data means each training example includes both the input and the correct output or target label. The model studies these examples and learns the relationship between the input features and the expected result. After training, it can make predictions or classifications on new data.
Unsupervised learning is incorrect because it uses unlabeled data and focuses on finding hidden patterns, clusters, or structures without predefined answers. Reinforcement learning is incorrect because it involves an agent learning through actions, rewards, and penalties in an environment. Semi-supervised learning is also not the best answer because it uses a mix of labeled and unlabeled data. Support Vector refers to part of the Support Vector Machine method, not a learning type by itself. Therefore, the correct learning type for labeled data is B. Supervised Learning .
NEW QUESTION # 32
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