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NEW QUESTION # 96
You have a business unit that uses an AI solution to process loan applications.
You discover that the solution rejects the application of all applicants that are older than 60 years of age.
Which Microsoft responsible AI principle is this violating?
Answer: B
Explanation:
An AI solution that rejects all loan applications based solely on an applicant being older than a certain age violates the Fairness principle in Microsoft's Responsible AI framework.
Here is a breakdown of why this constitutes a violation:
Violation of the "Fairness" Principle
Microsoft defines fairness as the commitment to treating all individuals and groups equitably, avoiding biases that could lead to unfair discrimination.
Discriminatory Outcome: Automatically rejecting a group based on a protected characteristic (age) is a textbook example of algorithmic bias and "allocation harm," where an AI system unfairly denies resources or opportunities.
Lack of Individual Assessment: The system treats every applicant in a certain age group the same, ignoring individual creditworthiness, financial stability, or income.
Reference:
https://infusedinnovations.com/blog/responsible-ai-fairness
NEW QUESTION # 97
Hotspot Question
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 98
- For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Answer Area
* Using incomplete or poor-quality data during generative AI model training can increase costs. Answer:
Yes
* AI models rely on training data to learn patterns and identify relationships to produce outputs. Answer:
Yes
* Generative AI models trained on non-representative datasets can produce inaccurate or unbalanced results. Answer: Yes
* Yes - Poor-quality or incomplete training data increases cost because it drives more iterations:
additional data cleaning, relabeling, re-training, and re-evaluation to reach acceptable performance. It can also increase operational costs after deployment if the model produces low-quality outputs that require human rework, escalations, or incident handling. In practice, data quality debt becomes model cost debt.
* Yes - Training data is the primary mechanism by which AI models learn statistical patterns and relationships. For generative models, the training corpus shapes language fluency, factual associations, style tendencies, and the kinds of content the model can produce. Without sufficient and appropriate training signals, outputs degrade.
* Yes - If the training dataset is not representative of the real-world population or business context, the model can systematically underperform for certain groups, topics, or edge cases. This can manifest as biased language, missing perspectives, and uneven accuracy, producing "unbalanced" results. That is why Responsible AI practice emphasizes representative data, evaluation across slices, and continuous monitoring.
NEW QUESTION # 99
Drag and Drop Question
Match the Microsoft responsible AI principles to the appropriate descriptions.
To answer, drag the principle from the column on the left to its description on the right. Each principle may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
NEW QUESTION # 100
Select the answer that correctly completes the sentence.
When a generative AI model produces output that seems realistic but contains incorrect information, the behavior is known as __________.
Answer:
Explanation:
Explanation:
model inaccuracy
The scenario describes a model producing plausible-sounding content that is factually wrong -a common generative AI failure mode often referred to as a "hallucination." Since "hallucination" is not offered in the dropdown, the best matching choice is model inaccuracy because the core problem is that the model's output is incorrect even though it appears confident and coherent.
The other options do not fit the definition of the behavior: data leakage is about sensitive information being exposed (for example, proprietary prompts, secrets, or personal data). Prompt injection is an attack technique where a user tries to override system instructions or cause unsafe actions. Overreliance describes a human
/organizational risk -trusting the model too much-rather than the model's intrinsic behavior of generating incorrect facts. Overreliance can be a consequence of this behavior, but it is not what the behavior itself is called.
In practice, you mitigate this kind of inaccuracy by grounding responses in trusted sources (for example, RAG), constraining prompts with explicit requirements, using verification steps (citations, cross-checking, tool-based validation), and adding human review for high-impact use cases.
NEW QUESTION # 101
......
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