View Full Microsoft AB-100 Exam Dumps and Practice Test Dumps.

 

Question 61

Which component helps an agent decide which tool to use?

1. Agent orchestration
2. File compression
3. DNS
4. Firewall rules

Correct Answer: 1

Explanation

Agent orchestration coordinates the steps an agent takes to complete a task. It can help determine which tools, knowledge sources, or actions should be used based on the user’s request and the available capabilities. In more complex solutions, orchestration can also manage sequencing, responses, errors, and handoffs between specialized agents. Architects should define clear responsibilities and permissions for each component. Good orchestration helps prevent unnecessary tool calls and keeps the agent focused on the intended business objective.

Question 62

What should an architect define before an agent performs business actions?

1. Screen size
2. Permissions and boundaries
3. Font style
4. Desktop theme

Correct Answer: 2

Explanation

Permissions and boundaries should be established before an agent is allowed to perform business actions. The architect should identify which operations the agent needs, which systems it can access, and which actions require approval. Least privilege should be applied so that the agent cannot perform unnecessary operations. The design should also include authentication, authorization, monitoring, and error handling. Clearly defined boundaries reduce the potential impact of unexpected behavior and help ensure that the agent operates within the organization’s business and security requirements.

Question 63

Which approach can improve an agent’s response to a specific business scenario?

1. Relevant grounding
2. More screen memory
3. Faster keyboard input
4. Additional printers

Correct Answer: 1

Explanation

Relevant grounding provides an AI agent with information related to the business scenario. Instead of relying only on general model knowledge, the agent can retrieve information from approved organizational sources. The architect should ensure that these sources are accurate, relevant, current, and accessible to the intended users. Permissions must also be respected during retrieval. Grounding does not automatically guarantee correct responses, so evaluation and monitoring remain important. The quality of the underlying knowledge source has a direct impact on the usefulness of grounded responses.

Question 64

An AI solution must process sensitive customer information. Which requirement should receive attention?

1. Application color
2. Data protection and privacy
3. Monitor resolution
4. Office layout

Correct Answer: 2

Explanation

Sensitive customer information requires appropriate data protection and privacy controls. The architect should determine what information the AI solution needs, where it is processed, who can access it, and how it is stored or transmitted. Additional considerations may include retention, auditing, encryption, data residency, and organizational compliance requirements. AI applications should not receive unrestricted access to sensitive information. The architecture should limit data exposure to what is necessary for the business scenario while maintaining appropriate security and governance throughout the solution lifecycle.

Question 65

What can an AI agent use to perform a business operation?

1. Tool or action
2. Wallpaper
3. Screen saver
4. Printer driver

Correct Answer: 1

Explanation

Tools and actions provide agents with capabilities beyond generating text. They can allow an agent to retrieve information, call an API, update a record, send information, or perform another supported business operation. Architects should define the purpose and permissions of each tool and ensure that inputs are validated. Sensitive actions may also require user confirmation or additional authorization. Tools should be selected according to the business requirement and should not expose capabilities that the agent does not need to complete its assigned tasks.

Question 66

Which metric is useful when evaluating AI response speed?

1. File size
2. Latency
3. Screen resolution
4. Number of folders

Correct Answer: 2

Explanation

Latency measures the time required for an AI system to respond or complete a requested operation. It is an important performance metric because users may expect responses within a specific time range. Architects should consider model processing, retrieval, tool calls, network communication, and other components that contribute to overall latency. Performance requirements should be defined according to the business scenario. Monitoring latency after deployment can help identify bottlenecks and determine whether model selection, orchestration, or architecture changes are necessary.

Question 67

A company wants different agents to specialize in sales, finance, and customer support. Which design is suitable?

1. Multi-agent architecture
2. Single static document
3. Manual spreadsheet
4. Network switch

Correct Answer: 1

Explanation

A multi-agent architecture can assign different responsibilities to specialized agents. A sales agent, finance agent, and customer-support agent can each be designed around a focused business domain while an orchestration mechanism coordinates their interaction when required. This can make complex solutions easier to organize and maintain. The architect should establish clear responsibilities, communication methods, access permissions, and escalation paths. The additional complexity of multiple agents should be justified by the business requirements rather than introduced simply because multiple agents are technically possible.

Question 68

Which practice helps ensure that an AI response is based on trusted information?

1. Removing all data
2. Using approved knowledge sources
3. Disabling validation
4. Allowing unrestricted web content

Correct Answer: 2

Explanation

Approved knowledge sources provide controlled information that can be used to ground an AI response. Organizations should identify authoritative sources and define how those sources are maintained, secured, and updated. Using trusted information can reduce the risk of responses being based on irrelevant or outdated material. Architects should also ensure that source permissions are respected and that users receive only information they are authorized to access. Knowledge governance, retrieval testing, and ongoing monitoring should complement the selection of approved sources.

Question 69

What is a key purpose of an AI agent’s system instructions?

1. Define expected behavior and constraints
2. Increase storage capacity
3. Configure printers
4. Change operating system settings

Correct Answer: 1

Explanation

System instructions establish important behavioral guidance for an AI agent. They can define its role, objectives, response expectations, restrictions, and other boundaries. Clear instructions help keep the agent aligned with the intended business scenario. However, instructions should not be treated as the only security mechanism. Sensitive operations still require appropriate identity, authorization, tool restrictions, and monitoring. Architects should test instructions against normal, ambiguous, and adversarial requests to determine whether the agent consistently follows the intended behavior.

Question 70

A business wants an agent to summarize customer feedback and identify common themes. Which capability is most relevant?

1. Generative AI and language analysis
2. Network routing
3. Device enrollment
4. File encryption only

Correct Answer: 1

Explanation

Generative AI and language analysis can help process customer feedback, summarize comments, and identify recurring themes. The architect should determine whether the solution needs simple classification, summarization, sentiment analysis, or a combination of capabilities. Data quality and privacy should also be considered because customer feedback may contain sensitive information. Evaluation should verify that important themes are identified accurately and that summaries do not introduce unsupported conclusions. The selected capability should match the business requirement rather than using a more complex model unnecessarily.

Question 71

What should be considered when an AI agent calls an external API?

1. Authentication and authorization
2. Screen brightness
3. Keyboard layout
4. Office furniture

Correct Answer: 1

Explanation

When an AI agent calls an external API, authentication and authorization are essential considerations. The architect should determine how the agent or connected service proves its identity and what permissions are required. Credentials, secrets, tokens, and certificates must be protected appropriately. The design should also address API limits, errors, timeouts, data handling, and monitoring. Least privilege should be applied to the API permissions. Secure integration ensures that the agent can perform its intended operation without receiving unnecessary access to external systems.

Question 72

Which approach can help prevent an agent from exposing restricted information?

1. Disable all authentication
2. Apply data access controls
3. Give every user administrator rights
4. Remove audit logs

Correct Answer: 2

Explanation

Data access controls help ensure that an agent retrieves and exposes information only to authorized users. The architecture should consider both the user’s permissions and the permissions of the agent or connected service. Grounding sources should be configured so that restricted information is not unnecessarily available to the agent. Additional controls such as auditing, monitoring, and least privilege can further reduce risk. Access should be tested with different user roles to verify that the agent does not reveal information beyond the intended authorization boundary.

Question 73

A solution architect wants to compare two AI models before selecting one for production. What should be used?

1. Model evaluation
2. Office seating plan
3. Printer testing
4. File renaming

Correct Answer: 1

Explanation

Model evaluation allows architects to compare models against defined requirements before selecting one for production. Evaluation criteria can include accuracy, response quality, latency, cost, safety, groundedness, and task completion. The same test scenarios should be used when meaningful comparisons are required. Business requirements should determine which metrics matter most. The architect should also consider operational factors such as availability, integration requirements, and governance. A model should be selected based on evidence from relevant evaluation criteria rather than on model size alone.

Question 74

What does prompt injection attempt to influence?

1. The AI system’s instructions or behavior
2. The monitor’s brightness
3. The network cable length
4. The printer cartridge

Correct Answer: 1

Explanation

Prompt injection attempts to influence an AI system by placing instructions in user input or retrieved content that conflict with the intended behavior of the system. An attacker may try to make an agent ignore its original instructions, reveal information, or perform unauthorized actions. Architects should treat external and retrieved content as potentially untrusted. Controls can include limiting tool permissions, separating trusted instructions from untrusted content, validating outputs, restricting sensitive actions, and monitoring unusual behavior. Security testing should specifically include prompt-injection scenarios.

Question 75

Which factor can affect the reliability of an AI agent?

1. Knowledge quality
2. Keyboard brand
3. Monitor size
4. Office lighting

Correct Answer: 1

Explanation

Knowledge quality can significantly affect an agent’s reliability, particularly when the agent uses grounding or retrieval. If source information is inaccurate, incomplete, outdated, or irrelevant, the agent may generate responses that are not useful or trustworthy. Architects should establish processes for source validation, updates, access control, and removal of obsolete information. Retrieval quality should also be evaluated to ensure that relevant information reaches the model. Reliable AI therefore depends not only on the model but also on the quality and management of the information supplied to it.

Question 76

Why is fallback handling important in an agent?

1. It manages unsupported or failed requests
2. It increases screen size
3. It changes file formats
4. It removes authentication

Correct Answer: 1

Explanation

Fallback handling provides a defined response when an agent cannot understand a request, complete an action, or find suitable information. Instead of producing an unsupported response, the agent can ask for clarification, redirect the user, provide supported options, or escalate to a person. Architects should identify common failure conditions and design appropriate fallback paths. Good fallback behavior improves reliability and user experience. It also helps communicate the boundaries of the AI solution instead of allowing users to assume that every request can be completed automatically.

Question 77

An organization wants to reduce the risk of unauthorized AI actions. Which principle should guide permissions?

1. Least privilege
2. Maximum privilege
3. Anonymous access
4. Shared administrator access

Correct Answer: 1

Explanation

Least privilege means granting only the permissions necessary for an agent to perform its intended responsibilities. This principle is particularly important for AI agents because they may interact with multiple systems and tools. Excessive permissions can increase the impact of incorrect decisions, compromised credentials, or malicious input. Architects should identify required operations and assign narrowly scoped permissions. Regular access reviews can verify that permissions remain appropriate. Least privilege should be applied across the agent identity, tools, APIs, data sources, and connected business applications.

Question 78

What should an architect consider when designing an AI solution for high user demand?

1. Scalability
2. Logo design
3. Keyboard type
4. Office decoration

Correct Answer: 1

Explanation

Scalability determines whether an AI solution can handle increasing workloads while maintaining acceptable performance and reliability. Architects should consider expected request volume, concurrent users, model capacity, retrieval workloads, API limits, latency, and cost. The solution should be tested under realistic load conditions before production deployment when appropriate. Monitoring can help identify capacity problems after deployment. Scalability requirements may also influence model selection, architecture, caching, orchestration, and service choices. The design should support expected growth without creating unnecessary operational complexity.

Question 79

Which approach is useful when an AI solution must require human approval for sensitive decisions?

1. Human-in-the-loop workflow
2. Fully autonomous execution
3. Anonymous access
4. Unrestricted tool use

Correct Answer: 1

Explanation

A human-in-the-loop workflow places a person at an appropriate decision point in the AI process. This can be useful when an action has significant financial, legal, operational, or customer impact. The architect should define which decisions require approval, what information the reviewer receives, and what happens when approval is denied. The workflow should also provide auditing and clear accountability. Human approval should be combined with technical controls, testing, and appropriate permissions rather than being treated as the only protection for sensitive AI operations.

Question 80

What is an important consideration when deploying an AI agent to production?

1. Governance and monitoring
2. Desktop wallpaper
3. Keyboard color
4. Office furniture

Correct Answer: 1

Explanation

Governance and monitoring are important after an AI agent reaches production. Governance establishes how the solution is controlled, reviewed, secured, and maintained, while monitoring provides visibility into performance, usage, failures, and potentially unsafe behavior. Architects should define ownership, access controls, evaluation procedures, incident handling, and change management. Production deployment should not be treated as the end of the AI lifecycle. Continuous monitoring and governance help organizations identify changes in business requirements, data quality, model behavior, and operational risks over time.