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질문 # 56
A company has multiple AI models that support generation of sales transactions.
Each release of the models must be reviewed by a security and compliance team before being deployed to the production environment. The security and compliance team must have access to prior versions to properly determine potential exposures introduced.
You need to recommend a solution to evaluate the impact of each deployment to production. The solution must enhance business continuity.
What should you recommend?
정답：A
설명：
Comprehensive and Detailed Explanation From Agentic AI Business Solutions Topics:
The correct answer is C. Implement version control for all the AI system components .
This question is not only about model approval. It is about creating a deployment process that allows the organization to:
* review every release before production
* compare current and prior versions
* evaluate the impact of changes
* improve business continuity if a deployment introduces risk
That makes version control for all AI system components the strongest answer.
Why C is correct
The requirement says the security and compliance team must have access to prior versions to determine exposures introduced by each release. That means the organization must be able to track, compare, and potentially roll back not just the model itself, but the broader AI solution over time.
In real enterprise AI deployments, "AI system components" usually include:
* models
* prompts
* orchestration logic
* configuration files
* policies
* connectors
* inference code
* evaluation assets
* deployment definitions
If only the model is versioned, the team may miss exposure introduced by surrounding components. For example:
* a prompt change could create unsafe outputs
* a policy/configuration change could expose sensitive data
* an orchestration update could alter transaction behavior
* a connector change could affect compliance boundaries
That is why full AI system version control is the best answer. It gives security and compliance teams complete visibility into what changed across releases.
It also enhances business continuity because version control supports:
* rollback to known-good versions
* change auditing
* release comparison
* traceability
* controlled recovery from faulty deployments
From an agentic AI business solutions perspective, this is the most robust governance pattern because AI outcomes are rarely determined by the model alone. They are determined by the entire solution stack.
Why the other options are less appropriate
A). Create a central model registry that uses version history
A model registry is useful, and version history helps, but this option is too narrow. The question asks about evaluating the impact of each deployment and enhancing business continuity. In enterprise AI systems, impact is often caused by more than just the model artifact. A model registry does not necessarily capture all surrounding components that affect production behavior.
B). Establish a promotion process by using a quality gate
A quality gate is valuable for approval workflows, but it does not by itself satisfy the need for deep access to prior versions across the system. It controls promotion, but it does not fully provide historical traceability and rollback coverage for all AI system components.
D). Track model retirement schedules to prevent service disruptions
This may support lifecycle planning, but it does not address the core requirement of comparing releases, reviewing prior versions, and evaluating exposure introduced by each deployment.
Expert reasoning
This question combines three ideas:
* security/compliance review
* access to prior versions
* business continuity
When those appear together, the strongest answer is typically the one that provides end- to-end traceability and rollback across the whole solution , not just a single artifact.
That is why version control for all AI system components is the best recommendation.
So the correct choice is:
A nswer: C
질문 # 57
Hotspot Question
A company has a Microsoft Copilot Studio agent for customer support. You are reviewing and validating the following prompts:
- A prompt that has instructions to";help the customer as best you ca";
- A prompt that helps retrieve product information from a knowledge
base
You need to ensure that the agent delivers consistent and accurate responses.
What should you do for each prompt? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
질문 # 58
A company has a Microsoft Copilot Studio agent that uses custom connectors to interact with enterprise APIs.
You need to recommend an application lifecycle management (ALM) process to ensure that the connectors are deployed consistently across development, test, and production environments and meet governance and traceability requirements.
What should you recommend?
정답：B
질문 # 59
Hotspot Question
You are designing an AI strategy for Microsoft Dynamics 365 finance and operations apps. You are evaluating the use of Microsoft Copilot Studio to provide in-app help and guidance based on generative AI general knowledge.
You need to recommend which knowledge sources to include in the generative help and guidance agent. The solution must minimize the risk of generating inaccurate responses.
What should you recommend? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
Box 1: Must be uploaded to the agent
Custom knowledge sources
Box 2: Must be enabled for the agent
AI general knowledge
To implement a generative AI agent for in-app help in Dynamics 365 Finance and Operations while minimizing inaccuracies, you must configure the agent in Microsoft Copilot Studio by uploading specific knowledge sources and enabling general AI knowledge.
1. Upload Custom Knowledge Sources
To ensure the agent provides accurate, organization-specific guidance, upload your internal documentation directly to the agent
2. Enable General AI Knowledge
To allow the agent to use its own broad generative AI knowledge for general inquiries:
Open Microsoft Copilot Studio and select the Dataverse environment linked to your Finance and Operations apps.
Navigate to Agents and open the specific agent named Copilot for finance and operations apps.
On the Overview tab, find the Knowledge section and set Allow the AI to use its own general knowledge to Enabled.
Publish the changes to make this capability available in the D365 F&O sidecar.
Reference:
https://arpideas.com/en/articles/knowledge-hub/building-smart-ai-agents-with-microsoft-copilot- studio
질문 # 60
A company has an Al agent that automates the review of customer feedback stored in a cloud database.
You plan to generate monthly reports from the agent's output to provide insights into customer sentiment and guide product development and marketing.
You need to ensure that the data ingested by the agent is clean and suitable for the intended use.
What should you do to prepare the data?
정답：B
설명：
The requirement is to make sure the data ingested by the agent is clean and suitable for the intended use, which is producing monthly sentiment insights to guide product development and marketing.
The best answer is C. Identify and address biased data.
Why C is correct:
For sentiment analysis and reporting, biased data can distort conclusions and produce misleading recommendations Data preparation should include checking for skew, unfair representation, missing segments, and other quality issues that affect downstream decisions This aligns with responsible AI and sound analytics practice
질문 # 61
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