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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Microsoft Certified: AI Transformation Leader |
| Exam Number: | AB-731 |
| Real Exam Qty: | 40-60 |
| Exam Format: | Case studies, Multiple choice, Drag and drop, Build list |
| Passing Score: | 700 / 1000 |
| Exam Duration: | 45 minutes |
| Certificate Validity Period: | 1 year (renewable) |
| Related Certifications: | Microsoft Certified: AI Transformation Leader |
| Exam Price: | USD 99 |
| Available Languages: | English |
| Sample Questions: | Microsoft AB-731 Sample Questions |
| Exam Way: | Online or at a test centre |
| Pre Condition: | No formal prerequisites. Recommended: familiarity with Microsoft 365 services, Azure AI services, and experience with adoption or change management in a business context. This certification is designed for business decision-makers at all levels; no coding is required. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ab-731 |
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NEW QUESTION # 45
Your company manages a website that publishes daily news articles. You need to recommend an AI solution that can analyze text and identify the main people, locations, and companies mentioned in the articles. What should you include in the recommendation?
Answer: C
Explanation:
The requirement is to analyze text and identify "people, locations, and companies" mentioned in news articles. This is a classic Named Entity Recognition (NER) / entity extraction scenario, which falls under natural language processing. Azure Language in Foundry Tools is the correct choice because it provides text analytics capabilities that detect and categorize entities in unstructured text-commonly including Person
, Location , and Organization . This enables downstream experiences such as topic tagging, search filters (e.
g., "all articles mentioning Company X"), trend dashboards (top people/places mentioned this week), and improved content discovery.
The other options do not match the requirement. Content Safety focuses on moderating harmful or policy- violating content (for example, hate, violence, self-harm, sexual content) and is not the primary tool for extracting named entities. Azure Vision is for analyzing images and performing OCR; it would only be relevant if the articles were images or scans, but the task here is entity extraction from text articles. Azure Speech is for speech-to-text, text-to-speech, and audio analysis; it would be used if your input were audio recordings rather than written articles.
Therefore, to identify key entities (people, locations, companies) from daily news article text, the best recommendation is Azure Language in Foundry Tools .
NEW QUESTION # 46
An organization is deploying generative AI solutions and wants to ensure systems are explainable, auditable, and accountable to stakeholders. Why is this focus critical when implementing AI?
Answer: D
Explanation:
Transparency and accountability are core Responsible AI principles that ensure AI systems can be understood, governed, and trusted by users and stakeholders.
Reference:
https://www.microsoft.com/en-us/ai/responsible-ai
NEW QUESTION # 47
Your company plans to implement a proof of concept (PoC) agent that uses Azure OpenAI.
The solution must start small and provide flexibility to scale usage as demand grows.
Which pricing model should you use?
Answer: C
Explanation:
The Standard (On-Demand) tier is the best choice for this scenario because it follows a pay-as- you-go consumption model. This allows a company to start a Proof of Concept (PoC) with virtually zero upfront cost or commitment, paying only for the tokens processed. As demand grows, the service provides the flexibility to scale without needing to manage complex capacity planning early on.
Reference:
https://azure.microsoft.com/en-us/products/ai-foundry/models/openai
NEW QUESTION # 48
Your company has a Microsoft 365 subscription and uses Microsoft 365 Copilot Chat.
Some users need to build and use declarative agents that can access work data.
Which type of license should you recommend for the users?
Answer: C
Explanation:
To use declarative agents that access work data (such as SharePoint or Graph connectors), users generally need a Microsoft 365 Copilot add-on license.
While Microsoft 365 Copilot Chat is included in most business subscriptions at no extra cost, its native capabilities are primarily limited to web-grounding and basic instructions. Accessing organizational data via agents typically requires one of the following licensing paths:
1. Microsoft 365 Copilot Add-on License
This is the most direct method. It grants full access to:
Declarative agents grounded in tenant data without additional usage fees.
Copilot Studio for authoring and managing these agents.
Embedded Copilot features in Word, Excel, Outlook, and Teams.
2. Pay-As-You-Go (Consumption) Model
If users do not have a full Copilot add-on license, organizations can enable metered usage (consumption-based billing).
Usage-based billing: Interactions with agents that access tenant data (SharePoint, connectors) consume "Copilot credits".
Requirement: This requires an Azure subscription and a billing policy set up in the Microsoft 365 admin center.
Reference:
https://support.microsoft.com/en-gb/topic/how-copilot-chat-works-with-and-without-a-microsoft-365-copilot-license-5810b659-fbe0-48ee-9fe6-d731fe86cdeb
NEW QUESTION # 49
Your company creates a custom Azure Machine Learning model that uses a generative AI assistant. The model initially delivers strong results. However, six months later, the model predictions become noticeably less accurate. What is a possible cause of the issue?
Answer: A
Explanation:
A common reason models degrade after being successful in production is data drift (also called concept drift). Over time, the distribution of input data changes -for example, customer behavior shifts, product catalog changes, seasonality changes, new categories appear, sensors get recalibrated, or business processes evolve. When the model sees data that differs from what it was trained on, its predictions can become less accurate. This is exactly what option A describes and is the most likely "six months later" cause.
Option B is not a primary explanation for reduced predictive accuracy. More compute can improve throughput
/latency, but it does not inherently improve correctness of predictions. If anything, compute constraints typically cause timeouts or slower responses, not a systematic accuracy drop.
Option C (trained incorrectly) would usually manifest earlier-poor performance from the start-unless the
"incorrectness" is that the model was trained on a snapshot that later became stale (which again maps back to drift). The correct operational response is to monitor for drift, validate performance regularly, and retrain
/refresh the model using newer representative data and updated features/labels.
NEW QUESTION # 50
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