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NEW QUESTION # 20
Which of the following models is called a black box as the outcomes cannot be directly linked to the model architecture and explained?
Answer: A
Explanation:
The correct answer is A. Neural network . Neural networks, especially deep neural networks, are often described as black box models because their internal decision-making process can be difficult to interpret directly. These models learn through many interconnected layers, weights, activation functions, and hidden representations. Although they may produce highly accurate predictions, it is often hard to clearly explain how a specific input led to a specific output in simple human-understandable terms.
Computer vision is not the best answer because it is an AI application area, not a specific model type. Support vector machines can also be complex in some cases, but neural networks are the most commonly associated with black box behavior in AI explainability discussions. Unsupervised learning is a learning approach, not a specific black box model. "Semi unsupervised learning" is not a standard primary machine learning category.
Because neural networks are widely known for limited transparency and difficult interpretability, the correct answer is A .
NEW QUESTION # 21
Which of the following is the CORRECT stage of the Data and AI Analytics Business Model Maturity Index?
Answer: D
Explanation:
The correct answer is E. All of the above because the Data and AI Analytics Business Model Maturity Index describes how organizations progress in their ability to use data, analytics, and AI for business value creation.
Business Monitoring is a valid stage because organizations first use data to observe performance, track metrics, and understand what is happening in the business. Business Insights is also a correct stage because analytics then helps organizations explain why things are happening and identify patterns, opportunities, and risks.
Business Optimization is another valid stage because mature organizations use analytics and AI to improve processes, decisions, resources, customer experiences, and operational outcomes. Cultural Transformation is also part of maturity because long-term AI and data success requires a shift in mindset, leadership behavior, decision-making culture, and enterprise-wide adoption of data-driven practices.
Since all listed options represent stages or maturity areas in the Data and AI Analytics Business Model Maturity Index, the correct answer is E. All of the above .
NEW QUESTION # 22
Which of the following is NOT a type of machine learning?
Answer: E
Explanation:
The correct answer is D. Restricted Learning because it is not commonly recognized as a standard type of machine learning. The main learning approaches include supervised learning, unsupervised learning, semi- supervised learning, reinforcement learning, and transfer learning. Supervised learning uses labeled datasets to train models for prediction or classification. Unsupervised learning uses unlabeled data to discover patterns, clusters, or hidden structures. Semi-supervised learning combines a small amount of labeled data with a larger amount of unlabeled data. Transfer learning reuses knowledge from a pre-trained model and adapts it to a new related task.
"Restricted Learning" is not a standard machine learning category in this context. Although some specific technical terms may include the word "restricted," such as restricted Boltzmann machines, that does not make
"restricted learning" a recognized general type of machine learning. Therefore, the option that is NOT a type of machine learning is D. Restricted Learning .
NEW QUESTION # 23
Which of the following is a common supervised learning model/algorithm?
Answer: E
Explanation:
The correct answer is D. All of the above because Naive Bayes classifier, Support Vector Machine, and linear regression are all commonly used supervised learning algorithms. Supervised learning uses labeled training data, where the model learns the relationship between input features and known output labels or target values.
Naive Bayes is a supervised classification algorithm commonly used for text classification, spam detection, sentiment analysis, and document categorization. Support Vector Machine is also a supervised learning algorithm used for classification and regression tasks by finding an optimal boundary or hyperplane between classes. Linear regression is a supervised learning model used for predicting continuous numeric values, such as sales, prices, demand, or costs, based on input variables.
Since all three listed options are valid examples of supervised learning models or algorithms, the most complete and correct answer is D. All of the above .
NEW QUESTION # 24
Which of the following is a step for the Value Engineering Framework?
Answer: E
Explanation:
The correct answer is E. All of the above because the Value Engineering Framework focuses on identifying, delivering, and expanding measurable business value from data and AI initiatives. "Define value creation" is a key step because organizations must first clarify the business problem, expected outcomes, success metrics, stakeholders, and value drivers before investing in an AI solution.
"Realize value creation" is also correct because value must be converted from a planned objective into actual operational or financial impact. This may involve deploying the solution, measuring results, improving processes, reducing cost, increasing revenue, improving risk management, or enhancing customer outcomes.
"Scale value creation" is correct because successful AI initiatives should not remain limited to isolated pilots.
Organizations need to scale proven use cases across teams, business units, workflows, and enterprise platforms to maximize return on investment and long-term impact. Since all three options represent steps in value engineering, the best answer is E. All of the above .
NEW QUESTION # 25
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