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You will be able to assess your shortcomings and improve gradually without having anything to lose in the actual AI Transformation Leader exam. You will sit through mock exams and solve actual Microsoft AB-731 dumps. In the end, you will get results that will improve each time you progress and grasp the concepts of your syllabus. The desktop-based Microsoft AB-731 Practice Exam software is only compatible with Windows.
NEW QUESTION # 54
- What should you use for each task? To answer, select the appropriate options in the answer area. NOTE:
Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Answer Area
* Extracting structured data from forms and invoices: Answer: Azure Document Intelligence in Foundry Tools
* Summarizing written content from business reports: Answer: Azure Language in Foundry Tools
* Generating descriptive text for uploaded images: Answer: Azure Vision in Foundry Tools These three tasks align to three different Azure AI capability families: document processing, language understanding/generation, and computer vision.
* Forms and invoices are semi-structured documents where the business need is to extract specific fields (IDs, names, totals, dates) reliably into structured output. Azure Document Intelligence is designed for intelligent document processing and includes prebuilt models (such as invoices) as well as custom extraction options, making it the correct choice for structured data extraction from documents.
* Summarizing written business reports is an NLP task focused on compressing long text into key points, themes, and action items. Azure Language provides language processing capabilities (including summarization features within language service capabilities), so it is the best fit for summarization scenarios.
* Generating descriptive text for images (image captioning/description) is a computer vision task.
Azure Vision can analyze uploaded images and return descriptions/captions and other visual insights, which directly matches the requirement to produce descriptive text from images.
NEW QUESTION # 55
- 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
* Azure Vision in Foundry Tools can extract and analyze key phrases from PDF files. Answer: No
* Azure Vision in Foundry Tools can generate images based on natural language descriptions. Answer:
No
* Azure Document Intelligence in Foundry Tools can be used to automate the processing of invoices and credit notes. Answer: Yes
* No - Azure Vision in Foundry Tools focuses on computer vision tasks such as image analysis and OCR (reading text from images and documents). While it can extract text from scanned PDFs via OCR, key phrase extraction is a natural language processing capability provided by Azure Language in Foundry Tools , not Azure Vision. Key phrase extraction analyzes text to identify main concepts, which is a different service family than vision.
* No - Azure Vision can analyze existing images (for example, generate captions/descriptions of an image), but generating new images from a text prompt is a generative model capability (for example, DALL E through Azure OpenAI/Azure AI Foundry model endpoints), not an Azure Vision feature.
Vision describes what it "sees"; it doesn't synthesize new images from natural language.
* Yes - Azure Document Intelligence in Foundry Tools is designed for intelligent document processing
, including automating extraction of structured fields from financial documents. Microsoft provides prebuilt models for invoices and supports custom extraction for similar document types, which makes it suitable for automating workflows involving invoices and credit-note style documents (field extraction, validation, routing).
NEW QUESTION # 56
Your company uses a fine-tuned generative AI solution trained on data that is representative of the general population.
You discover that some of the generated responses include inappropriate or exclusionary language based on ableist assumptions.
You need to prevent the inappropriate responses. Your solution must minimize costs.
What should you do?
Answer: D
Explanation:
Implementing a content moderation filter is a critical strategy for fine-tuned generative AI to prevent the output of inappropriate or exclusionary language, including content rooted in ableist assumptions. These filters serve as a "digital safety layer" that intercepts biased or harmful material before it reaches the user.
How Filters Address Ableism and Exclusion
Contextual Detection: Modern filters using Large Language Models (LLMs) and Natural Language Processing (NLP) can detect subtle discriminatory phrases and slurs that traditional keyword filters might miss.
Policy-Driven Guardrails: Developers can use tools like the Lakera Guard Content Safety template to apply specific policies that flag and block ableist speech in real-time.
Customizable Classifiers: Services such as Azure AI Content Safety allow for the detection of
"abusive, derogatory, or discriminatory language" through trained statistical models.
Personalized Moderation: Emerging tools are being designed specifically to help disabled users filter out ableist hate and harassment tailored to their unique experiences.
Reference:
https://www.lakera.ai/blog/content-moderation
NEW QUESTION # 57
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 # 58
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: A
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 # 59
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