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NEW QUESTION # 57
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: D
Explanation:
Data governance is essential to prevent generative AI (GenAI) from accessing confidential or classified information, as it provides the necessary framework, policies, and controls to manage data security throughout the AI lifecycle. Effective governance involves a combination of data classification, strict access controls, and active monitoring to ensure only authorized, non- sensitive data is used.
Reference:
https://dacodes.com/blog/data-governance-best-practices-for-generative-ai
NEW QUESTION # 58
Your company purchases Microsoft 365 Copilot for its sales department. The sales department needs to find and summarize information across internal documents quickly. From which two data sources can the sales department obtain results by default? (Select TWO.)
Answer: C,E
Explanation:
By default, Microsoft 365 Copilot is grounded in your organization's Microsoft 365 data through Microsoft Graph , and it respects the user's existing permissions. For "find and summarize information across internal documents," the most direct default document repositories in Microsoft 365 are SharePoint (team sites and shared libraries) and OneDrive (a user's work files). That is why C (Microsoft SharePoint) and D (Microsoft OneDrive) are the correct selections. Microsoft explicitly describes Copilot as accessing organizational content via Microsoft Graph, including user documents and related work content.
The other options are not "by default" sources. A (on-premises file share) is not automatically part of Microsoft Graph unless you integrate/migrate content or use connectors to make it discoverable in Microsoft
365 experiences. B (custom CRM) similarly requires an integration approach (for example, Microsoft 365 Copilot connectors / Graph connectors) to index and expose that data for Copilot to use. E (Microsoft Sway) is not a primary default content source for Copilot's document grounding and is not typically referenced as a core internal document repository compared to SharePoint/OneDrive.
So, for immediate, out-of-the-box internal document discovery and summarization within Microsoft 365, SharePoint + OneDrive are the default answer.
NEW QUESTION # 59
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The correct answer is establish a champions program for your company. A champions program helps scale AI adoption because it creates a network of motivated employees who learn AI capabilities, share practical examples, support peers, and identify meaningful use cases within their departments. This is more effective than relying only on awareness campaigns or self-paced courses because champions create continuous peer-led adoption and practical reinforcement. Performance metrics are useful for measuring adoption, but metrics alone do not build confidence or capability. A champions program supports change management by turning early adopters into local advocates who can demonstrate value in real workflows, reduce resistance, gather feedback, and help employees understand how AI tools apply to their daily work.
NEW QUESTION # 60
- 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
* Prompt engineering changes how a generative AI model was trained. Answer: No
* Prompt engineering focuses on designing clear, concise, and context-rich instructions. Answer: Yes
* Effective prompt engineering involves maximizing the number of tokens used in each request. Answer:
No
* No - Prompt engineering does not modify model weights or retrain the model. It is an inference-time technique: you steer outputs by improving the instructions and context you send to the model. Changing how a model was trained would involve pretraining, fine-tuning, or other training methods-separate from prompt engineering.
* Yes - The primary goal of prompt engineering is to reduce ambiguity and variability by providing clear instructions, the right context, and explicit output constraints. This often includes specifying role and task, providing necessary facts or grounding text, defining format (bullets, JSON, headings), and adding examples (few-shot) when helpful. Well-constructed prompts improve consistency, relevance, and usefulness of outputs.
* No - Good prompt engineering does not mean "use as many tokens as possible." In fact, using unnecessary tokens can increase cost and may degrade quality by adding noise. Effective prompts are typically as short as possible but as long as necessary: enough context to achieve accuracy and alignment, but not bloated. Token discipline matters because most pricing is token-based and long contexts can dilute attention over what's most important.
NEW QUESTION # 61
HOTSPOT - Select the answer that correctly completes the sentence.
You use __________ to train a model that will forecast product demand based on historical sales data.
Answer:
Explanation:
Explanation:
Azure Machine Learning
Forecasting product demand from historical sales data is a predictive analytics / machine learning use case.
It typically requires selecting an appropriate forecasting approach (for example, regression, tree-based methods, or time-series models), preparing and splitting historical data, training and validating the model, tuning hyperparameters, and then deploying the model for ongoing inference. The Microsoft service designed to support that end-to-end ML lifecycle is Azure Machine Learning , which is why it correctly completes the sentence.
Azure Machine Learning provides the tooling and infrastructure to: manage datasets, run training jobs on scalable compute, track experiments, compare model performance, register models, and operationalize them through managed endpoints and pipelines. This makes it well-suited for iterative forecasting work, where you may retrain on new data regularly, monitor drift, and update models as product lines, promotions, or seasonality patterns change.
The other options do not directly fit "train a model" for forecasting. Azure AI Search is an indexing/retrieval service used to search and ground generative AI responses, not for training predictive models. Azure OpenAI provides access to large language and multimodal models for generative tasks (drafting, summarizing, Q & A) and is not the primary platform for building classical forecasting models. Microsoft Foundry is a broader platform experience for building and governing AI apps and agents, but the specific service for training a forecasting model on historical sales data is Azure Machine Learning.
NEW QUESTION # 62
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