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  1. Home
  2. Microsoft Certification
  3. AB-731 Exam
  4. Microsoft.AB-731.v2026-10-16.q88 Dumps
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Question 6

Your company has an AI solution that uses a prebuilt Azure OpenAI model to generate content. You need to reduce the cost of the solution while minimizing the impact on the quality of the generated output. Which two actions should you perform? (Select TWO.) NOTE: Each correct selection is worth one point.

Correct Answer: C,D
To reduce Azure OpenAI costs with minimal quality loss, you target the biggest cost drivers: token usage and model price per token (or throughput unit). C (Optimize the prompts) is a best practice because shorter, clearer prompts reduce unnecessary input tokens and often reduce output length by tightening instructions and formatting. Prompt optimization can preserve or even improve quality by removing ambiguity, adding constraints, and using compact context (for example, only the most relevant grounding passages). Lower token consumption directly lowers cost while maintaining response usefulness.
D (Switch to an alternate model) is also effective because different models have different price/performance tradeoffs. Moving from a premium model to a more cost-efficient model (or a smaller variant) can significantly reduce spend. You can minimize quality impact by validating outputs on representative scenarios and using a tiered approach (cheap model by default, expensive model only for complex cases).
The other options are less aligned to the goal. A (Fine-tune) typically increases cost (training and ongoing evaluation) and is not the first-line cost reducer. B (Content moderation) is primarily a safety control; it can add overhead and doesn't directly reduce token costs. E (Decrease hosting hours) applies to capacity-based hosting scenarios, but the question states a prebuilt Azure OpenAI model for content generation-cost reduction is best achieved by prompt/token optimization and selecting the right model.
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Question 7

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:
Answer Area
* A manufacturer can use Azure Vision in Foundry Tools to identify product defects on an assembly line. Answer: Yes
* A logistics company can use Azure Vision in Foundry Tools to recognize package shipping labels. Answer: Yes
* The HR department at your company can only use Azure Vision in Foundry Tools to extract written content from Microsoft Word files. Answer: No Azure Vision in Foundry Tools provides computer vision capabilities to analyze images, including identifying visual features and reading text with OCR. Because it is designed to "analyze images" and support vision scenarios, it can be applied to manufacturing quality inspection use cases where the goal is to detect anomalies/defects from images captured on a production line. This aligns with statement 1 being Yes .
Statement 2 is also Yes because recognizing shipping labels is fundamentally text extraction from images (often plus some layout/field parsing). Azure Vision supports optical character recognition (OCR) to read printed text from images, and Microsoft documentation explicitly notes OCR can extract text from images such as product labels and similar real-world text surfaces-making shipping labels a direct fit.
Statement 3 is No because it is incorrectly restrictive. Azure Vision is not limited to extracting written content from Word documents, nor is OCR restricted to Word files. Vision capabilities apply broadly to images (and, depending on the capability, various document/image inputs) for tasks like image analysis and text recognition. HR could use it for many scenarios such as extracting text from scanned images, photos, or other visual inputs-not "only" Word files.
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Question 8

Your company uses generative AI to assist with content creation and customer interactions.
You need to evaluate whether Azure Machine Learning can add value to the current customer management.
For which use case should you use Machine Learning?

Correct Answer: A
Azure Machine Learning (Azure ML) adds critical value by providing predictive intelligence that complements the creative capabilities of generative AI. While generative AI automates content and interactions, Azure ML identifies which customers are likely to stop using services (churn), allowing for proactive rather than reactive management.
Key Value Additions to Customer Management
Churn Prediction: Azure ML models analyze behavioral metrics, transaction history, and support interactions to assign a "churn risk score" to each customer.
Proactive Retention: By identifying at-risk customers 10-11 months before renewal, teams can intervene with targeted strategies before a customer decides to leave.
Synergy with Generative AI: Azure ML identifies who is likely to leave, and generative AI can then be used to create personalized outreach (e.g., custom emails or special offers) specifically tailored to address that customer's predicted pain points.
Efficient Resource Allocation: Businesses can focus high-touch retention efforts and marketing spend on high-value customers flagged as high-risk, rather than using a "one-size-fits-all" approach.
Insight into Churn Drivers: Azure ML helps discover why customers leave (e.g., price sensitivity or poor support) by identifying the most influential factors in the prediction model.
Reference:
https://vskumar.blog/2025/05/15/empowering-enterprises-with-azures-generative-ai-and-machine-learning-10-use-cases-solutions
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Question 9

Your company uses a generative AI solution.
You need to improve the quality of responses by using grounding.
Which statement accurately describes how grounding improves accuracy and relevancy?

Correct Answer: C
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Question 10

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

Correct Answer:

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.
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