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NEW QUESTION # 74
Your company uses a generative AI solution.
You need to improve the quality of responses by using grounding.
Which statement accurately describes how grounding improves accuracy and relevancy?
Answer: C
NEW QUESTION # 75
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:
Explanation:
Box 1: No
A text-to-image generator (like DALL-E or Midjourney) takes textual descriptions and synthesizes them into visual imagery. It does not perform language translation workflows; that task requires natural language processing (NLP) or large language models (LLMs).
Box 2: No
Predictive analytics models rely on historical data and machine learning algorithms to forecast future trends, behaviors, or numerical outcomes (such as click-through rates or future sales).
They do not create or synthesize brand-new creative artifacts like marketing text or ad copies; that is the role of Generative AI.
Box 3: Yes
Generative AI chatbots (like those built using custom LLMs or Copilot Studio) excel at maintaining dynamic, human-like context. They can handle highly personalized conversations based on user profile inputs and dynamically synthesize relevant product recommendations.
NEW QUESTION # 76
You need to recommend a service that supports indexing information and knowledge mining by extracting insights from documents.
What should you recommend?
Answer: D
Explanation:
Document Intelligence in Foundry Tools (formerly part of Azure AI Services) is a powerful, cloud- based service designed to automate data processing by extracting structured information, key- value pairs, tables, and text from unstructured documents like PDFs, images, and forms.
As part of the Azure AI Foundry ecosystem, it is designed for knowledge mining and accelerating document-heavy workflows, allowing you to convert raw files into actionable data for downstream analytics.
Reference:
https://azure.microsoft.com/en-in/products/ai-foundry/tools/document-intelligence
NEW QUESTION # 77
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The correct answer is data security. The principle of least privilege means users, apps, and services receive only the minimum access required to perform their work. Microsoft 365 Copilot respects existing Microsoft 365 permissions and does not grant users new access to content they are not already authorized to view. That behavior is a security control because it helps prevent unauthorized access to company information. Data classification is about labeling or categorizing information by sensitivity.
Data compliance focuses on meeting policy, legal, or regulatory requirements. Data loss prevention helps prevent sensitive information from being shared inappropriately. Least privilege is primarily an access-control practice, so the best category is data security.
NEW QUESTION # 78
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: B
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 # 79
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