Exam AB-731 Demo, AB-731 New Study Notes

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Microsoft AI Transformation Leader Sample Questions (Q17-Q22):

NEW QUESTION # 17
What is considered a best practice when forming an AI adoption team in an enterprise environment?

Answer: D

Explanation:
Enterprise AI adoption succeeds when it is cross-functional from the start . Option A is best practice because AI impacts legal risk, privacy, security, compliance, workforce processes, and business strategy-not just technology. Including leadership ensures alignment to priorities and funding; including business units ensures use cases and success metrics are real; including legal/compliance ensures responsible AI and regulatory obligations are addressed early. This prevents rework and reduces the chance of deploying solutions that are misaligned with policy or unacceptable risk.
Options B and C delay governance and business alignment, which often leads to "build first, govern later" failure modes-solutions that work technically but cannot be approved or scaled due to privacy/security gaps or unclear accountability. Option D over-optimizes for vendor selection without ensuring the organization has defined responsible AI requirements, target use cases, and operating model. Procurement is important, but it is not the primary driver of a successful adoption team. The most sustainable approach is a representative adoption team that integrates business, technical, and governance stakeholders from day one.


NEW QUESTION # 18
Your company uses a non-reasoning generative AI model to create textual content. You discover that the model's responses are inconsistent and do NOT meet expectations. You need to improve the prompts. What should you do? More than one answer choice may achieve the goal. Select the BEST answer.

Answer: B,C

Explanation:
When a non-reasoning generative AI model produces inconsistent outputs, the most reliable improvement is to make the prompt more specific, constrained, and demonstrative of what "good" looks like.
A is correct because adding high-quality examples is a form of few-shot prompting. Examples act like
"training wheels" at inference time: they show the model the desired structure, tone, level of detail, formatting rules, and boundaries. This reduces ambiguity and variance, especially for tasks like marketing copy, summaries, policy text, or customer replies. The more your examples resemble real target outputs (including edge cases), the more consistent the model's completions become.
B is correct because adding context, relevant source material, and explicit expectations narrows the model's degrees of freedom. Including the intended audience, purpose, constraints (length, voice, banned claims), and trusted reference content (approved facts, product specs, policy excerpts) helps the model stay aligned and reduces hallucinations and off-brand language. This is also where you specify acceptance criteria such as
"must include 3 bullet points," "use UK English," or "cite only provided text." C is not best: technical jargon can confuse or bias output if it's not aligned to the task; clarity beats jargon. D is not best: a single concise requirement is usually under-specified and often increases variability.


NEW QUESTION # 19
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 # 20
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:

Explanation:
Box 1: Yes
Yes - Larger datasets can increase the cost of generative AI solution that uses an Azure Machine Learning workspace.
larger datasets can increase the cost of a generative AI solution using an Azure Machine Learning (AML) workspace. While the AML workspace itself has no additional charge, larger datasets increase expenses across several underlying Azure services, particularly through higher storage, compute, and data transfer requirements.
Box 2: Yes
Yes - The cost of consuming Azure OpenAI models is primarily identified by the number of input and output tokens processed.
The cost of consuming Azure OpenAI models is primarily determined by the number of input and output tokens processed.
This consumption-based, pay-as-you-go model calculates costs based on the total volume of text (or image/audio data) sent to the model (input) and the text generated by the model (output), usually billed per 1,000 or 1 million tokens, depending on the model and pricing page updates.
Box 3: No
No - The cost of custom generative AI solutions always remains the same regardless of the model version or capability used he cost of custom generative AI solutions varies significantly based on the model's version, complexity, and capability. While a basic chatbot might range from $40,000 to $150,000, advanced enterprise-grade platforms with high-risk reasoning can exceed $400,000 to
$500,000+.
Reference:
https://www.doit.com/blog/optimizing-ml-costs-with-azure-machine-learning
https://www.finout.io/blog/azure-openai-pricing
https://medium.com/@dejanmarkovic_53716/custom-ai-solutions-cost-guide-2025-pricing- insights-revealed-cf19442261ec


NEW QUESTION # 21
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
* Microsoft 365 Copilot helps users create and analyze content in Microsoft 365 apps. Answer: Yes
* Microsoft Copilot Studio can only be used to customize Microsoft 365 Copilot. Answer: No
* Microsoft Security Copilot uses AI to assign sensitivity labels to documents. Answer: No
* Yes - Microsoft 365 Copilot is built into Microsoft 365 apps (such as Word, Excel, PowerPoint, Outlook, and Teams) to help users draft, summarize, rewrite, and analyze work content. This includes creating documents and presentations, summarizing emails and meetings, and analyzing information in productivity workflows. That is its primary value proposition, so the statement is true.
* No - Copilot Studio is not limited to customizing Microsoft 365 Copilot. It is used to build and manage agents (conversational experiences) and extend Copilot experiences by connecting to data and actions, creating custom topics/behaviors, and integrating business processes. While it can be used to extend Microsoft 365 Copilot (for example, via declarative agents and other extensibility paths), it is broader than "only customizing Microsoft 365 Copilot," so the statement is false.
* No - Assigning sensitivity labels to documents is primarily a Microsoft Purview Information Protection capability (manual labeling, default labeling, auto-labeling) rather than a core function of Microsoft Security Copilot. Security Copilot is focused on security operations (incident investigation, threat hunting, response guidance). Although AI can support security workflows, the specific act of assigning sensitivity labels to documents is not what Security Copilot is designed to do by default, making the statement false.


NEW QUESTION # 22
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