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| Section | Weight | Objectives |
|---|---|---|
| Identify benefits, capabilities, and opportunities for Microsoft AI apps and services | 35–40% | - Microsoft AI ecosystem
|
| Identify an implementation and adoption strategy for Microsoft AI apps and services | 20–25% | - AI adoption strategy
|
| Identify the business value of generative AI solutions | 35–40% | - AI value identification
|
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NEW QUESTION # 56
DRAG DROP - Match the Microsoft responsible AI principles to the appropriate descriptions. NOTE: Each correct match is worth one point.
Answer:
Explanation:
Explanation:
Answer Area
* Protect personal information and apply strong safeguards to keep data secure. Answer: Privacy and security
* Make AI solutions understandable by explaining how and why decisions are made. Answer:
Transparency
* Design AI solutions that are accessible to people of all abilities and experiences. Answer: Inclusiveness
* Involve human oversight in the control of AI solutions. Answer: Accountability Microsoft's Responsible AI principles provide a practical framework for building and deploying trustworthy AI systems.
Privacy and security is the principle focused on protecting personal information and ensuring strong safeguards-such as least privilege access, encryption, DLP controls, secure storage, and auditing-so data remains protected throughout collection, processing, and usage.
Transparency is about making AI systems understandable. This includes communicating when AI is being used, explaining system capabilities and limitations, and providing clarity on how outputs are produced (for example, what data sources are used, what assumptions apply, and why certain recommendations are generated). Transparency reduces confusion and helps users calibrate trust appropriately.
Inclusiveness ensures AI solutions are designed to benefit people of diverse abilities, backgrounds, and experiences. Practically, this includes accessibility (assistive technologies, readable outputs), inclusive UX design, and testing across diverse user groups so the solution does not exclude or disadvantage certain populations.
Accountability emphasizes that humans and organizations remain responsible for AI outcomes. Involving human oversight is a direct expression of accountability: setting governance, defining who owns decisions, establishing escalation paths, and ensuring there are controls for review, intervention, and correction when AI outputs are unsafe or incorrect.
NEW QUESTION # 57
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 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.
NEW QUESTION # 58
Which statement accurately describes the difference between a pretrained generative AI model and a fine- tuned generative AI model?
Answer: C
Explanation:
A pretrained generative AI model is trained initially on a large, broad, and diverse dataset so it learns general language (or multimodal) patterns and capabilities. Fine-tuning then takes that pretrained base and performs additional training on a smaller, task- or domain-specific dataset to specialize behavior- improving performance for a particular use case, tone, style, or domain knowledge representation. That is exactly what option C states, making it the correct answer.
Option A is incorrect because both pretraining and fine-tuning may use labeled or unlabeled data depending on the technique; the distinction is not "labeled vs. unlabeled." Option B is incorrect because a pretrained model is not "faster to train" due to fewer parameters; pretraining is typically the most compute-intensive phase precisely because it's done at large scale, while fine-tuning is smaller but still trains the same model architecture. Option D is reversed: the pretrained model is the general-purpose foundation, while the fine- tuned model is the specialized variant for a specific task or dataset.
NEW QUESTION # 59
- 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 # 60
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 # 61
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