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NEW QUESTION # 51
Select the most CORRECT risk-scoring methodology function statement for prospective risk.
Answer: B
Explanation:
The correct answer is C because prospective risk is forward-looking. It focuses on estimating future model risk by using the most current risk condition, present indicators, and existing risk posture of the model. In AI governance and model risk management, prospective risk assessment helps organizations anticipate possible future issues such as performance degradation, bias, drift, compliance exposure, operational failure, or business impact before those risks become actual problems.
Option A is not the most correct because analyzing historical model performance is more closely linked with retrospective risk assessment. Historical performance can support risk analysis, but it does not fully define prospective risk. Option B is not accurate because "upcoming model performance" is not directly available for analysis; future performance must be predicted, not already analyzed. Option E is incorrect because A and B are not both accurate statements. Therefore, the most correct statement is C. Prospective risk leverages the most current risk of the model to predict the overall model risk for future cycles .
NEW QUESTION # 52
Which of the following is a CORRECT statement for Few-shot learning?
Answer: A
Explanation:
The correct answer is D. a and b only because few-shot learning is a machine learning technique that allows a model to learn or adapt to a new task using only a small number of labeled examples. It is especially useful when collecting large labeled datasets is expensive, slow, or difficult. Instead of requiring thousands or millions of labeled records, few-shot learning depends on prior knowledge learned by the model and applies that knowledge to new examples with limited supervision.
Statement A is correct because few-shot learning is recognized as a machine learning approach. Statement B is also correct because the core idea of few-shot learning is learning from very limited labeled data. Statement C is not correct because learning from unlabeled data is more closely associated with unsupervised learning or semi-supervised learning, not the standard definition of few-shot learning. Therefore, the correct answer is D.
a and b only .
NEW QUESTION # 53
Which of the following is the CORRECT first step in the Machine Learning lifecycle?
Answer: C
Explanation:
The correct answer is B. Business understanding . The first step in the machine learning lifecycle is to understand the business problem, objective, expected outcome, and success criteria. Before collecting data, selecting algorithms, or preparing models, the organization must clearly define what problem the ML solution is intended to solve and how success will be measured. This may include identifying business goals such as cost reduction, revenue improvement, risk mitigation, customer experience improvement, operational efficiency, or decision automation.
Data understanding comes after business understanding because data exploration should be guided by the business objective. Algorithm use understanding is also not the first step because choosing or evaluating algorithms should happen only after the problem, data, and intended outcome are clear. Options D and E are incorrect because the question asks for the single first step. Therefore, the correct first step in the machine learning lifecycle is B. Business understanding .
NEW QUESTION # 54
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: E
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 # 55
Choose the CORRECT reasons. We want to study AI to automate things, because
Answer: D
Explanation:
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 .
NEW QUESTION # 56
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