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NEW QUESTION # 95
An organization is deploying generative AI solutions and wants to ensure systems are explainable, auditable, and accountable to stakeholders. Why is this focus critical when implementing AI?
Answer: B
Explanation:
Transparency and accountability are core Responsible AI principles that ensure AI systems can be understood, governed, and trusted by users and stakeholders.
Reference:
https://www.microsoft.com/en-us/ai/responsible-ai
NEW QUESTION # 96
- Select the answer that correctly completes the sentence.
The Analyst agent in Microsoft 365 Copilot __________.
Answer:
Explanation:
Explanation:
uses structured data and provides insights by using text, charts, tables, and other visuals.
The Analyst agent in Microsoft 365 Copilot is positioned as a "data analysis" reasoning agent that helps users work through structured information (for example, tables, spreadsheets, and other dataset-like inputs) and then produces analytical outputs . The best completion is the option stating it "uses structured data and provides insights by using text, charts, tables, and other visuals," because that describes the hallmark outcome of analyst-style work: summarizing patterns, highlighting key drivers, and presenting results in formats that business users can act on. Analyst-style assistance typically includes exploring the data, identifying trends and anomalies, comparing segments, and explaining findings clearly-often accompanied by tables and visual representations that make the insights easier to consume.
The other dropdown options align to different use cases: "compiles background research for a new market or initiative" describes a research-oriented agent, "generate audio summaries" is a media summarization function, and "answer employee FAQs" describes a conversational knowledge assistant. Analyst is the one most directly associated with structured-data interpretation and producing a mix of narrative plus analytical artifacts (tables/charts) to communicate conclusions.
NEW QUESTION # 97
Hotspot Question
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 98
Your company uses generative AI to assist with content creation and customer interactions.
You need to evaluate whether Azure Machine Learning can add value to the current customer management.
For which use case should you use Machine Learning?
Answer: D
Explanation:
Azure Machine Learning (Azure ML) adds critical value by providing predictive intelligence that complements the creative capabilities of generative AI. While generative AI automates content and interactions, Azure ML identifies which customers are likely to stop using services (churn), allowing for proactive rather than reactive management.
Key Value Additions to Customer Management
Churn Prediction: Azure ML models analyze behavioral metrics, transaction history, and support interactions to assign a "churn risk score" to each customer.
Proactive Retention: By identifying at-risk customers 10-11 months before renewal, teams can intervene with targeted strategies before a customer decides to leave.
Synergy with Generative AI: Azure ML identifies who is likely to leave, and generative AI can then be used to create personalized outreach (e.g., custom emails or special offers) specifically tailored to address that customer's predicted pain points.
Efficient Resource Allocation: Businesses can focus high-touch retention efforts and marketing spend on high-value customers flagged as high-risk, rather than using a "one-size-fits-all" approach.
Insight into Churn Drivers: Azure ML helps discover why customers leave (e.g., price sensitivity or poor support) by identifying the most influential factors in the prediction model.
Reference:
https://vskumar.blog/2025/05/15/empowering-enterprises-with-azures-generative-ai-and-machine-learning-10-use-cases-solutions
NEW QUESTION # 99
Your company manages an online catalog of office supplies. You plan to use a generative AI solution to create product descriptions for your company's website. The solution must ensure descriptions can be posted immediately after creation, enable selection/inclusion of product details, and be fast and simple for non- technical staff. What is the best type of solution to use? Select the BEST answer.
Answer: C
Explanation:
The task is high-volume content generation with consistent structure and immediate publishing: product descriptions that reliably include chosen product attributes (brand, specs, materials, dimensions, use cases) and can be produced quickly by non-technical staff. The best fit is a fine-tuned LLM (D) because fine-tuning can standardize tone, format, and completeness against your catalog schema, reducing variability and minimizing manual editing before posting. With a fine-tuned model, you can strongly enforce style guidelines (length, voice, prohibited claims), and you can template prompts so staff only supply product fields and get publish-ready copy.
Option A is not best: Azure Machine Learning is excellent for predictive models but is unnecessary for straightforward text generation. B (Researcher) is optimized for multistep research across work data + web, not deterministic product copy generation. C (interactive agent) can help collect requirements, but it's more complexity than needed; the core need is consistent text generation from structured product data, which fine- tuning addresses directly while keeping user interaction simple (fill fields # generate description).
NEW QUESTION # 100
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