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NEW QUESTION # 14
Your company receives thousands of scanned invoices each month.
You need to recommend an AI solution that can automatically extract key details, such as invoice numbers, vendor names, and total amounts.
What is the best solution to recommend? More than one answer choice may achieve the goal.
Select the BEST answer.
Answer: C
NEW QUESTION # 15
- 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
* Prompt engineering changes how a generative AI model was trained. Answer: No
* Prompt engineering focuses on designing clear, concise, and context-rich instructions. Answer: Yes
* Effective prompt engineering involves maximizing the number of tokens used in each request. Answer:
No
* No - Prompt engineering does not modify model weights or retrain the model. It is an inference-time technique : you steer outputs by improving the instructions and context you send to the model.
Changing how a model was trained would involve pretraining, fine-tuning, or other training methods- separate from prompt engineering.
* Yes - The primary goal of prompt engineering is to reduce ambiguity and variability by providing clear instructions , the right context , and explicit output constraints . This often includes specifying role and task, providing necessary facts or grounding text, defining format (bullets, JSON, headings), and adding examples (few-shot) when helpful. Well-constructed prompts improve consistency, relevance, and usefulness of outputs.
* No - Good prompt engineering does not mean "use as many tokens as possible." In fact, using unnecessary tokens can increase cost and may degrade quality by adding noise. Effective prompts are typically as short as possible but as long as necessary : enough context to achieve accuracy and alignment, but not bloated. Token discipline matters because most pricing is token-based and long contexts can dilute attention over what's most important.
NEW QUESTION # 16
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Answer:
Explanation:
Explanation:
The correct selections are Yes, Yes, Yes. Lack of trust is a common AI adoption barrier because users and leaders may worry about security, privacy, hallucinations, inaccurate outputs, and unclear accountability. If people do not trust the system, they will avoid using it or over-escalate decisions outside the intended workflow. AI adoption also fails when organizations overlook high-quality data. Poor, outdated, incomplete, or poorly governed data weakens grounding, reduces output quality, and damages user confidence. Finally, adoption is strongest when business and compliance stakeholders are involved early. Business stakeholders ensure the solution solves real problems and delivers measurable value, while compliance, legal, risk, and privacy teams ensure the solution aligns with policy and regulatory obligations.
NEW QUESTION # 17
Your company is reviewing a new AI solution before deploying it. The company wants to ensure that the solution follows Microsoft responsible AI principles.
What is the best approach to achieve the goal? More than one answer choice may achieve the goal. Select the BEST answer.
Answer: B
Explanation:
The correct answer is B. Testing the AI solution to identify and mitigate unfair or inconsistent outcomes directly supports Microsoft responsible AI principles, especially fairness, reliability and safety, accountability, and inclusiveness. Before deployment, organizations should evaluate whether the system behaves consistently across different user groups, avoids discriminatory outcomes, and produces safe and reliable outputs.
Automatically approving or rejecting loan applications increases risk unless strong governance and human oversight are in place. Collecting personal data can increase privacy exposure if it is not necessary and controlled. Prioritizing model performance alone is also insufficient because a technically accurate model can still produce biased, unsafe, or non-transparent outcomes. Responsible AI requires evaluation, mitigation, oversight, and monitoring.
NEW QUESTION # 18
Your company is building a portfolio of AI-powered business solutions. Company executives want to understand how Microsoft responsible AI principles can support the company ' s long-term goals. Which benefit best demonstrates the importance of responsible AI? Select the BEST answer.
Answer: B
Explanation:
Responsible AI is fundamentally about earning and maintaining trust while scaling AI across the enterprise. Option C is the best answer because responsible AI practices (fairness, reliability and safety, privacy and security, transparency, accountability, and inclusiveness) reduce reputational, legal, and operational risk and make adoption sustainable over time. When stakeholders trust that AI is governed, tested, and monitored, the organization can expand AI usage confidently across business units.
The other options are incorrect because they make absolute or counterproductive claims. A is false:
responsible AI does not "guarantee" accuracy; it reduces risk and improves assurance, but no model can be guaranteed correct in all contexts. B is the opposite of reality: responsible AI increases the importance of data protection and governance; it does not reduce the need for them. D is also incorrect: responsible AI requires clear ownership and oversight, especially from leadership, because accountability is a core principle. In short, responsible AI matters because it builds stakeholder confidence and provides guardrails that support long- term, scalable, and compliant AI adoption-exactly what executives care about when investing in an AI portfolio.
NEW QUESTION # 19
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