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| Section | Weight | Objectives |
|---|---|---|
| Identify benefits, capabilities, and opportunities for Microsoft's AI apps and services | 35–40% | - Microsoft Copilot Studio capabilities - Microsoft 365 Copilot features and use cases - Aligning Microsoft AI tools to business requirements - Azure AI services and Azure AI Foundry tools |
| Identify the business value of generative AI solutions | 35–40% | - Foundational concepts of generative AI - Selecting generative AI solutions for business needs - Differences between generative AI and other AI types - Business benefits, ROI, and efficiency gains |
| Identify an implementation and adoption strategy for Microsoft's AI apps and services | 20–25% | - Organizational readiness and change management - Adoption planning and scaling AI solutions - Measuring success and continuous improvement - Responsible AI principles and governance |
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NEW QUESTION # 113
Select the answer that correctly completes the sentence.
Prompt engineering is the process of __________.
Answer:
Explanation:
Explanation:
crafting clear instructions to guide generative AI solutions in generating context-appropriate content.
Prompt engineering is fundamentally about how you communicate intent to a generative AI model so it produces outputs that meet business expectations. The best completion is "crafting clear instructions to guide generative AI solutions in generating context-appropriate content" because it captures the practical, day-to- day discipline: shaping the input (prompt) with the right task framing, constraints, context, and output format.
In real deployments, prompt engineering includes specifying the role and objective (for example, "act as a customer support agent"), providing the necessary context (product details, policy excerpts, audience), adding explicit requirements (tone, length, must/must-not statements), and defining structured output (JSON fields, bullet sections, headings). It can also include adding examples (few-shot prompting), clarifying what to do when information is missing, and instructing the model to cite only provided sources or to ask follow-up questions. These techniques reduce ambiguity, improve consistency, and lower the risk of hallucinations or off-brand responses.
The other options are not accurate definitions. "Integrating AI-powered tools into business workflows" describes solution adoption/integration, not prompt engineering. "Identifying and fixing errors in AI- generated content" is review/editing or quality assurance. "Designing, developing, and training generative AI models" is model development/ML engineering. Prompt engineering operates without changing model weights ; it's about steering model behavior through well-constructed instructions and context.
NEW QUESTION # 114
A marketing team wants to automatically create product descriptions and campaign email drafts.
Which generative AI capability best meets this business need?
Answer: C
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 # 115
Your company plans to use generative AI to help summarize and analyze internal business documents. You need to recommend a solution to prevent generative AI from accessing confidential or classified information.
What should you include in the recommendation?
Answer: B
Explanation:
Preventing generative AI from accessing confidential or classified information is primarily a data access and classification control problem. The most effective broad solution is data governance (B) -the framework of policies and controls that ensures sensitive content is identified, classified, protected, and access is restricted using least privilege. Data governance includes information classification/sensitivity labels, access control reviews, secure sharing practices, data loss prevention (DLP), and auditing-controls that directly limit what data is available to the AI through permission trimming and policy enforcement.
An information barrier (A) policy is more specific: it's intended to prevent communication and collaboration between defined user groups (often for regulatory/ethical walls). It does not comprehensively address document classification or restrict AI access to sensitive files across the tenant. A data retention policy (C) governs how long content is kept and when it's deleted; it's not a primary access-prevention mechanism. Communication monitoring (D) is a detection/oversight control; it can help identify risky activity, but it does not itself prevent access to confidential content by the AI.
Therefore, to prevent AI from surfacing confidential/classified data during summarization and analysis, you should recommend data governance as the primary control layer.
NEW QUESTION # 116
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: B
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 # 117
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Box: Batch API
An organization that runs continuous, large-scale workloads with Azure OpenAI models should choose the _______ pricing model.
An organization that runs continuous, large-scale, non-time-sensitive workloads with Azure OpenAI models should choose the Batch API pricing model.
This approach provides significant cost savings and operational advantages for high-volume, asynchronous tasks.
For continuous, non-urgent workloads, the Batch API provides the best balance of cost- effectiveness and throughput, often allowing organizations to cut their AI inference costs in half while maximizing scalability.
Reference:
https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/batch?view=foundry-classic
NEW QUESTION # 118
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