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NEW QUESTION # 21
Your company plans to use generative AI to help project managers and engineers work with construction blueprints stored as PDF files. You need to recommend a generative AI solution that processes both images and text, summarizes building design, answers questions, and extracts information such as locations of electrical, heating, and plumbing systems. What should you recommend?
Answer: D
Explanation:
Construction blueprints in PDFs often contain a mix of text, symbols, linework, and diagrams . The requirements include understanding both visual layout (where systems are located) and textual annotations , producing summaries, and answering Q & A. That combination requires a multimodal generative AI approach-models that can reason over images and text together. Therefore, A is best.
OCR alone (B) can extract printed text, but it won't reliably interpret diagram geometry, symbols, or spatial relationships (e.g., "electrical riser is on the east core near gridline B-4"). Text completion (C) is too generic and doesn't address image understanding. Document summarization (D) is only one requirement (summary) and still depends on first extracting/understanding both visual and textual elements.
A multimodal solution can ingest the PDF pages as images (or rendered page images) plus extracted text, then answer questions grounded in both modalities. In practice, you may combine OCR and layout extraction with a multimodal LLM so the model can reference drawing regions, legends, callouts, and system diagrams to produce accurate explanations and field extractions.
NEW QUESTION # 22
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:
NEW QUESTION # 23
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 connectors enable you to index data from multiple sources to make the data available in Copilot. Answer: Yes
* You can build a custom Microsoft 365 Copilot connector when the available connectors do NOT meet your data integration requirements. Answer: Yes
* To use Microsoft 365 Copilot connectors, you need a Microsoft Copilot Studio license. Answer: No
* Yes - Microsoft 365 Copilot connectors (including synced connectors) are designed to bring external data into Microsoft Graph so it can be semantically indexed and surfaced in Microsoft 365 Copilot experiences. Microsoft explicitly states that synced connectors ingest/crawl content into Microsoft Graph where it's indexed and then available for Copilot prompts and citations.
* Yes - When Microsoft-provided connectors don't meet integration needs, organizations can create custom connectors (often referred to as Microsoft Graph connectors / custom connector development) to connect other data sources. This is a common extensibility path to index line-of-business repositories and make that content discoverable via Copilot and Microsoft Search.
* No - Using Microsoft 365 Copilot connectors does not require a Copilot Studio license. Connectors are generally configured and managed in Microsoft 365 admin/search experiences, and Microsoft's licensing guidance indicates that users can view connector data in Microsoft 365 Copilot and Microsoft Search with valid Microsoft 365/Office 365 licensing-Copilot Studio licensing is about building agents in Copilot Studio, not a prerequisite to use connectors.
NEW QUESTION # 24
Your company has an AI solution that uses a prebuilt Azure OpenAI model to generate content.
You need to reduce the cost of solution while minimizing the impact on the quality of the generated output.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: C,E
Explanation:
To reduce costs for a prebuilt Azure OpenAI model while maintaining high output quality, you should implement a combination of prompt optimization, caching, and model selection strategies.
[C]
1. Optimize Prompt Engineering
Reducing the number of tokens processed is the most direct way to lower costs.
Prompt Compression: Use tools like LLMLingua to remove redundant text from prompts without losing semantic meaning.
Task Decomposition: Break complex tasks into smaller, focused prompts to improve clarity and reduce expensive retries.
Shorten Context: Summarize long conversation histories or external RAG (Retrieval-Augmented Generation) documents before sending them to the model.
2. Implement Caching Mechanisms
[A]
3. Use Model Routing and Tiering
Matching request complexity to model capability can reduce costs by 30-60%.
*-> Model Cascading: Use a small, cheap model (e.g., GPT-4o-mini) for simple tasks like classification or basic formatting. Route only complex reasoning tasks to high-end models like GPT-4o.
Reasoning Effort Control: For newer models like GPT-5, adjust the reasoning_effort parameter to
"minimal" for straightforward tasks to save on compute.
Reference:
https://ekaivakriti.com/blog/how-to-reduce-openai-costs-by-60-percent/
NEW QUESTION # 25
A marketing team wants to automatically create product descriptions and campaign email drafts.
Which generative AI capability best meets this business need?
Answer: D
Explanation:
Natural language content generation is correct because Natural language content generation enables the creation of product descriptions, emails, blogs, and other written materials using prompts. This directly supports marketing automation and content scaling.
References:
https://learn.microsoft.com/en-us/training/modules/understand-foundations-generative-ai- business-leaders/3-explore-business-value-generative-ai-solutions
https://www.microsoft.com/en-us/power-platform/blog/power-automate/generative-ai-prompts-to- automate-content-processing/
NEW QUESTION # 26
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