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NEW QUESTION # 101
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: B
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 # 102
Which statement accurately describes the difference between a pretrained generative AI model and a fine-tuned generative AI model?
Answer: B
Explanation:
Pretrained generative AI models are trained on massive, diverse datasets to gain foundational knowledge, while fine-tuned models take these pretrained weights and further train them on smaller, specific datasets to improve accuracy for narrow tasks or industries. This process aligns the model's output to specialized styles, domains, or tasks.
Key Differences and Details:
Pretrained Models (Foundational): These models (e.g., GPT-4) learn general language, concepts, and patterns from massive, broad datasets like Common Crawl. They are versatile but may lack expertise in specialized fields.
Fine-tuned Models: By adjusting the weights of a pretrained model on a smaller, labeled dataset, the model is tailored to specific applications, such as medical analysis, legal document review, or a particular brand voice.
Performance Benefits: Fine-tuning improves precision and reduces irrelevant outputs compared to a generic model.
Methodology: While pretraining is unsupervised or self-supervised, fine-tuning often uses supervised learning Reference:
https://www.ibm.com/think/topics/fine-tuning
NEW QUESTION # 103
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
Explanation:
For scanned invoices, the requirement is structured field extraction (invoice number/ID, vendor, totals) from document images or PDFs at scale. The best fit is Azure Document Intelligence because it is purpose- built for document processing and provides prebuilt invoice models that combine OCR with layout/structure understanding to extract common invoice fields into a structured output. Microsoft's invoice model is explicitly designed to analyze invoices (including scanned images) and return key fields and line items in structured form, which directly maps to this scenario.
Azure Vision (B) can perform OCR and basic image analysis, but OCR alone typically returns text without robust invoice-specific field interpretation (e.g., reliably identifying "Invoice ID" vs. "Order ID," totals vs.
subtotals, vendor vs. ship-to). Document Intelligence is optimized for advanced document structure extraction and is therefore the "best" single recommendation.
Azure AI Search (C) focuses on indexing and retrieval/knowledge mining across a corpus; it's not the primary service for extracting invoice fields for downstream processing. Azure Machine Learning (D) could be used to build a custom model, but that adds cost and time compared with a prebuilt invoice extractor designed for this document type.
NEW QUESTION # 104
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
* A generative AI solution is well-suited to predict next-quarter sales trends. Answer: No
* A generative AI solution can summarize lengthy policy documents. Answer: Yes
* A generative AI solution can create product descriptions from product specifications. Answer: Yes
* No - Predicting next-quarter sales trends is primarily a forecasting/predictive analytics problem.
Microsoft differentiates predictive AI (forecasting outcomes from historical patterns) from generative AI (creating content like text, images, or code). While you can use LLMs to assist analysts (explain trends, draft narratives), the core forecasting model is typically traditional ML/time-series methods rather than generative AI as the main engine.
* Yes - Summarization is a classic, high-value generative AI capability. Given a long policy, an LLM can compress it into executive summaries, key obligations, risks, and action items, often with formatting constraints (bullets, sections, "do/don't" lists). Microsoft highlights summarization and analysis as common generative AI use cases in business contexts.
* Yes - Generative AI is well-suited to transform structured inputs (features/specs) into natural- language outputs (product descriptions). This is straightforward "content generation," where you control tone, length, and required fields (benefits, differentiators, disclaimers). Microsoft also points to generating product descriptions and similar marketing/customer-facing text as a practical generative AI scenario.
NEW QUESTION # 105
During AI adoption planning, leadership evaluates workforce readiness, operating models, and governance structures required to support AI at scale. Why is this step critical?
Answer: C
Explanation:
Assessing workforce skills, governance, and operating models ensures the organization can adopt AI responsibly and scale usage effectively.
Reference:
https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/scenarios/ai/plan
NEW QUESTION # 106
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