2026 Agentic Cloud Architect 面试准备

Agentic Cloud Architect 面试准备

一、先掌握 Architect 的核心思维

面试的时候不要一上来就说：

“Use Copilot Studio.”

Architect 应该先回答：

Why this agent? What business problem does it solve? What data does it need? What actions can it perform? How do we secure it? How do we evaluate it? How do we deploy and monitor it? How do we prove ROI?

Business Problem
       ↓
Agent Type
       ↓
Orchestration
       ↓
Model
       ↓
Grounding / Data
       ↓
Tools / Actions
       ↓
Security & Governance
       ↓
ALM / Deployment
       ↓
Evaluation / Observability
       ↓
ROI / Adoption

二、Agentic AI Architecture

Q1. What is an AI agent?

An AI agent is a system that can understand user intent, reason about the task, use tools or enterprise data, execute actions, and potentially operate autonomously toward a business goal.

Chatbot	Agent
Answer questions	Achieve goals
Mostly conversational	Reason + act
Limited actions	Can invoke tools
Mostly deterministic	Dynamic orchestration
User drives workflow	Agent can drive workflow

例如：

Chatbot

“What is the status of order 123?”

返回：

Order 123 is shipped.

Agent

用户：

“Please check order 123, determine whether it is delayed, notify the customer and create a support case if necessary.”

Understand request
      ↓
Get order information
      ↓
Check shipment status
      ↓
Determine delay
      ↓
Generate customer communication
      ↓
Send communication
      ↓
Create case if required

三、如何选择 Agent Platform

Q2. When would you choose Copilot Studio?

I would choose Copilot Studio when the organization needs a low-code or no-code business agent, especially when the agent needs to work with Power Platform, Dataverse, Dynamics 365, Power Automate and enterprise connectors.

Business users
Low-code
Dataverse
Dynamics 365
Power Platform
Enterprise connectors
Conversational agents

资料中明确有：

Low-code AI business solution using Copilot Studio

以及：

Dataverse as a centralized source for AI systems.

四、什么时候使用 Microsoft Foundry？

Q3. When would you choose Microsoft Foundry instead?

推荐回答

I would choose Microsoft Foundry when the solution requires more developer control over models, agent orchestration, evaluation, tracing, custom logic, and integration with Azure services.

尤其是：

Developer-oriented
        ↓
Custom models
        ↓
Custom orchestration
        ↓
Agent evaluation
        ↓
Tracing
        ↓
Azure services

可以把两个平台记成：

Copilot Studio → Business / Low-code Agent

Microsoft Foundry → Developer / Custom Agent

五、Agent Architecture 选择

资料中出现了：

Single agent
Multi-agent
Task agent
Autonomous agent
Computer Use
MCP

Q4. When should you use a multi-agent architecture?

推荐回答

当一个 Agent 同时负责很多不同 domain/task，并且：

reasoning 很复杂
response 很慢
task 可以拆分
不同 domain 需要不同 specialization

例如：

                Supervisor Agent
                       |
        +--------------+--------------+
        |              |              |
    Sales Agent    Finance Agent   Support Agent
        |              |              |
     CRM            ERP            Customer Service

优势：

Separation of concerns
Specialized reasoning
Easier maintenance
Independent evaluation
Potential parallel execution

资料中有单 Agent + 单 Prompt 导致：

incomplete results
domain-specific reasoning problems
slow response

这是典型的 Agent architecture optimization 场景。

六、Grounding

这是 Agent Architect 必须非常熟悉的概念。

Q5. What is grounding?

Grounding means providing the model with trusted enterprise context or data so that its response is based on relevant business information rather than only the model's pretrained knowledge.

例如：

User
 ↓
Agent
 ↓
Grounding
 ├── Dataverse
 ├── Azure AI Search
 ├── SharePoint
 └── Enterprise APIs
 ↓
LLM
 ↓
Answer

Q6. How would you improve inaccurate agent responses?

优先检查：

1. Data quality
2. Grounding sources
3. Retrieval quality
4. Prompt/instructions
5. Model capability
6. Evaluation metrics

资料中明确出现：

Ensure data ingested by the agent is clean and suitable for intended use.

答案是识别和处理 biased data。

所以面试可以说：

Before changing the model, I would first validate the quality, relevance, completeness and bias of the grounding data.

七、Dataverse 为什么重要？

Multiple internal and external data sources → centralized source for AI systems → grounding + analytics

答案是：

Microsoft Dataverse。

面试回答：

Dataverse can serve as the business data layer for Power Platform and Dynamics 365 solutions, providing a centralized source for agents, applications, grounding and analytics.

架构：

ERP
CRM
External Systems
SharePoint
       ↓
   Dataverse
       ↓
+------+-------+-------+
|      |       |       |
Agent  D365   Power BI Apps

八、Tools / Actions / Connectors

Q7. How does an agent interact with external systems?

可以通过：

Connectors
APIs
Power Automate
Agent flows
MCP
Custom tools

面试回答：

I separate the reasoning layer from the action layer. The agent decides what needs to be done, while tools and APIs execute the actual business operations.

这是非常重要的 Architect 思维。

Agent
  ↓
Reason
  ↓
Tool selection
  ↓
API / Connector / Flow
  ↓
Business system

九、MCP

资料虽然只直接出现 MCP 作为选项，但这是 Agentic Architect 面试很可能深入的问题。

Q8. What is MCP?

推荐回答

Model Context Protocol is a standardized protocol for exposing tools, resources and context to AI applications.

面试重点：

Agent
 ↓
MCP
 ↓
Tools / Resources
 ↓
Enterprise systems

优势：

Standardized tool interface
Reusable integrations
Decouples agent from individual implementations

十、Computer Use

资料中有非常典型的一题：

Agent needs to simulate user interactions across third-party apps and websites, such as clicking buttons, entering text and extracting information from screens.

答案：

Computer Use in Copilot Studio。

面试回答：

I would use Computer Use when an application does not expose a suitable API or connector and the agent needs to interact with the UI like a human.

例如：

Agent
 ↓
Computer Use
 ↓
Open website
 ↓
Click button
 ↓
Enter data
 ↓
Read screen

但 Architect 必须补一句：

If a reliable API is available, I would prefer the API over UI automation because it is generally more reliable and maintainable.

十一、Security & Governance

这是 Cloud Architect 面试的重点。

Q9. How do you prevent an agent from accessing sensitive data?

DLP policies in Power Platform。

面试回答：

I would apply defense in depth rather than relying on the agent itself.

包括：

Identity
 ↓
RBAC
 ↓
Dataverse security
 ↓
DLP policies
 ↓
Connector restrictions
 ↓
Data classification
 ↓
Audit
 ↓
Monitoring

十二、DLP

Q10. What is the purpose of DLP?

DLP policies control which connectors can be used together and help prevent business data from being transferred through unauthorized services.

例如：

Business data
      ↓
Approved connector
      ↓
Agent

而不是：

Dataverse
   ↓
Unapproved connector
   ↓
External service

十三、Azure Policy

另一个必须掌握。

如果面试官问：

How do you enforce Azure resource governance?

回答：

Azure Policy.

尤其：

Approved regions
Required tags
Allowed resource types
Compliance
Continuous evaluation

资料中也有 Azure OpenAI resources 只能部署在 approved regions，并持续进行 compliance verification 的场景。

十四、Responsible AI

资料中有一个非常重要的 bias scenario。

Q11. An AI solution generates different results based on customer traits. How would you address bias?

资料答案：

Modify system instructions。

面试不要只说答案。

推荐回答：

I would first identify where the bias is introduced. I would review the system instructions, grounding data, evaluation results and model behavior. If the issue is caused by instructions, I would update the system instructions and then run evaluations to verify that the change reduces the bias without degrading other metrics.

十五、AI Evaluation

这是 Agent Architect 与普通 Cloud Architect 的重要区别。

你必须能回答：

How do you know an agent is actually good?

不能只说：

Users like it.

需要建立 Evaluation Framework。

                   Evaluation
                       |
       +---------------+---------------+
       |               |               |
   Quality          Safety        Business
       |               |               |
 Groundedness      Toxicity       Task success
 Relevance         Bias           Resolution
 Coherence         Security       Productivity

资料中出现了：

AI-assisted evaluation

以及 GPT-4o 作为 judge，并返回 numeric score。

面试可以说：

I would combine automated evaluation with human review for high-impact scenarios.

十六、Observability

Q12. How would you monitor an agent?

推荐架构：

Agent
  |
  +---- Application Insights
  |
  +---- Log Analytics
  |
  +---- Copilot Studio analytics
  |
  +---- Tracing / evaluation

监控：

Latency
Token usage
Errors
Tool calls
Conversation outcomes
Escalations
User adoption
Response quality

十七、Application Insights

资料里专门有：

Monitor telemetry in near-real-time Download transcripts Monitor usage and performance

这是你面试可以主动讲的：

Application Insights is useful when I need detailed telemetry and near-real-time operational monitoring.

同时：

Copilot Studio analytics is useful for agent usage, conversations, outcomes and performance.

十八、ALM

Q13. How would you deploy an agent across Dev, Test and Production?

推荐架构：

Development
     ↓
Source Control
     ↓
Build / Validation
     ↓
Test
     ↓
Quality Gate
     ↓
Production

对于 Copilot Studio / Power Platform：

Use Solutions and Power Platform deployment pipelines.

资料明确强调：

Agents + connectors should be included in a solution
Managed solutions should be deployed to production

十九、为什么 Production 使用 Managed Solution？

面试回答：

Managed solutions provide better control over production components and help prevent direct modification of deployed components.

原则：

DEV
 ↓
Unmanaged solution
 ↓
TEST
 ↓
Managed solution
 ↓
PROD

二十、Custom Connector ALM

资料中有：

custom connector must be deployed consistently across environments

答案是：

Add the custom connector to the solution.

面试回答：

I would make the connector part of the solution so that it can participate in the ALM lifecycle rather than rebuilding it manually in each environment.

二十一、ROI / ROAI

Agentic Architect 一定要懂。

Q14. How do you calculate the ROI of an AI agent?

不要只说：

Cost savings - AI cost.

推荐框架：

Current Process
      ↓
Baseline
      ↓
AI-enabled Process
      ↓
Benefits
      ↓
Costs
      ↓
ROAI

Business drivers 可以包括：

Reduced case resolution time
Increased employee productivity
Reduced manual work
Reduced operational cost
Increased conversion
Increased revenue

资料中的 ROAI 场景明确使用：

Reduced average case resolution time + Increased employee productivity。

二十二、TCO vs Pricing Calculator

这个很容易被问。

Azure Pricing Calculator

用于：

估算未来 Azure workload cost

例如：

Estimated API calls
+
Model usage
+
Storage
+
Compute
=
Estimated Azure cost

资料中的 sentiment-analysis ROAI 场景答案就是 Azure Pricing Calculator。

TCO Calculator

用于：

On-premises vs Azure

所以：

Future Azure cost → Pricing Calculator

On-premises → Azure migration comparison → TCO Calculator

二十三、Business Adoption

这个是你上传的 Fabrikam Case Study 特别重要的点。

问题不是：

Agent 能不能工作？

而是：

Users actually use it or not?

Architect 应该设计：

Agent Deployment
       ↓
User Adoption
       ↓
Usage Analytics
       ↓
Feedback
       ↓
Agent Improvement
       ↓
Higher Adoption
       ↓
Business Value

指标：

Active users
Conversations
Returning users
Resolution rate
Escalation rate
Satisfaction
Task completion
Monthly adoption growth

二十四、Human-in-the-loop

这是 Agentic AI Architect 非常重要的一点。

例如：

Agent fails twice → escalate to human.

架构：

User
 ↓
Agent
 ↓
Attempt 1
 ↓
Attempt 2
 ↓
Failure
 ↓
Human Representative

面试回答：

Autonomous does not mean uncontrolled. For high-risk, ambiguous or failed scenarios, I would define explicit escalation boundaries.

二十五、Autonomous Agent vs Task Agent

资料有 fraud detection 场景：

Human analyst must make final decision.

答案：

Task agent generates fraud risk scores for human review.

面试可以总结：

Autonomous Agent

Agent
 ↓
Decision
 ↓
Action

Task Agent

Agent
 ↓
Analyze
 ↓
Recommend
 ↓
Human decision

适合：

Fraud
Financial decisions
Compliance
High-impact business decisions

二十六、Architect Scenario 面试题

Scenario 1

Your company wants an AI agent that accesses CRM data, answers questions and creates follow-up tasks. What architecture would you propose?

答题结构

Copilot Studio
      ↓
Dataverse
      ↓
Dynamics 365
      ↓
Connectors / Power Automate
      ↓
Application Insights
      ↓
Power Platform ALM

回答：

I would use Copilot Studio for the conversational agent, Dataverse as the business data layer, connectors or Power Automate for actions, and Application Insights plus Copilot Studio analytics for observability. I would deploy the solution through Power Platform ALM.

Scenario 2

The agent gives inaccurate answers. What would you do?

回答：

Check data quality
      ↓
Check grounding
      ↓
Check retrieval
      ↓
Check instructions
      ↓
Evaluate model
      ↓
Run evaluation again

关键句：

I would not immediately replace the model. I would first determine whether the problem is caused by data quality, retrieval, instructions or model capability.

Scenario 3

The agent is too slow.

Model latency
Number of tool calls
Sequential vs parallel execution
Prompt size
Retrieval latency
Number of agents
Unnecessary orchestration

如果复杂任务可以拆分：

Consider a multi-agent architecture where specialized agents can work independently or in parallel.

Scenario 4

The business wants the cheapest possible AI solution.

不要直接说：

Use the smallest model.

应该说：

I would optimize total business value rather than simply minimizing infrastructure cost.

考虑：

Model cost
+
Infrastructure
+
Integration
+
Operations
+
Maintenance
-
Business benefit

最终比较：

Cost per successful business outcome.

这才是 Architect 思维。

二十七、你面试时可以使用的万能回答框架

以后面试官给你一个 Agent 场景，你可以按照这个顺序回答：

Business

What business outcome are we trying to achieve?

Agent

What type of agent is appropriate?

Data

What trusted data does the agent need?

Model

What model capability is required?

Tools

What actions and APIs does the agent need?

Security

What identity, RBAC, DLP and data protection controls are required?

Reliability

What happens when the agent fails?

Human

Where should human-in-the-loop be introduced?

Evaluation

How do we measure quality and safety?

Observability

How do we monitor telemetry and performance?

ALM

How do we move from DEV → TEST → PROD?

ROI

How do we prove the solution creates business value?

二十八、最后给你一张面试 Cheat Sheet

Topic	面试关键词	首选思路
Low-code Agent	Business Agent	Copilot Studio
Custom Agent	Developer	Microsoft Foundry
Enterprise data	Grounding	Dataverse / Search
Power Platform governance	Approved connectors	DLP
Azure governance	Approved regions	Azure Policy
UI automation	Click/type/read screen	Computer Use
External tools	Standardized integration	MCP
ALM	Dev/Test/Prod	Solutions + deployment pipelines
Production	Prevent direct editing	Managed solution
Telemetry	Near-real-time	Application Insights
Agent analytics	Usage/outcomes	Copilot Studio analytics
Model evaluation	Quality	AI-assisted evaluation
Bad grounding	Accuracy	Data quality + grounding
Complex reasoning	Architecture	Multi-agent
Human decision	High risk	Human-in-the-loop
Future Azure cost	ROAI	Pricing Calculator
On-prem → Azure	TCO	TCO Calculator
Business value	ROAI	Productivity + cost/time savings
Adoption	Business success	Usage / outcome metrics
Bias	Responsible AI	Instructions + evaluation/data review