A utility company wants to deploy an agent that automates responses to customer inquiries about power outages. The agent will use data from the outage management system. The system contains real-time outage information, but also includes historical outage records from the past decade. What should the team do to ensure the agent provides accurate responses?

Include all historical records so the agent can answer questions about past outages.

Filter the data to include only current and ongoing outages.

Let the agent decide which data to use based on the customer's question.

Use only the most recent month of outage data.

A media company wants to deploy an agent that recommends content to users based on their viewing history. The viewing history is stored in a data warehouse with a star schema. The team wants to integrate data from a new recommendation engine that uses a different data model. What should the team do to ensure the agent can use both data sources effectively?

Convert the warehouse data to match the new engine's model exactly.

Keep the two data sources separate and have the agent query both independently.

Use the new engine's data model and ignore the existing warehouse.

Create a unified data model that maps both schemas to a common structure.

A bank is developing an AI agent to approve small business loans. The agent will use historical loan performance data, current economic indicators, and applicant credit scores. The bank's risk committee requires that the agent's decisions be explainable and that the data be no more than one day old. What is the primary challenge the architect must address?

Selecting an AI model that is fast enough to process applications in real time.

Preventing the agent from accessing applicant credit scores due to privacy concerns.

Balancing the need for timely data with the need for explainable decisions.

Ensuring the historical loan data is stored in a cloud data warehouse.

When reviewing data for an AI solution, what does 'data accuracy' refer to?

The data correctly reflects the real-world values it represents

The data is stored in a secure and compliant manner

The data is collected from multiple sources

The data is available 24/7 without any downtime

A telecommunications company wants to deploy an agent that analyzes customer call logs to identify common service issues. The call logs are stored in a data lake and are updated continuously. The agent will generate a daily summary of top issues. The team notices that some call logs are missing the issue category field. What should the team do to ensure the agent's analysis is reliable?

Use only the call logs that have the issue category field populated.

Fill in missing issue categories using a classification model trained on complete records.

Exclude all call logs that are missing the issue category.

Ignore the missing field and analyze only the available data.

Microsoft Certified:Agentic AI Business Solutions Architect

Domain 1Objective 1

Analyze Requirements for AI-powered Business Solutions AB-100 Practice Questions (Page 9)

Part of the Plan AI-powered business solutions domain, which accounts for 25–30% of the AB-100 exam. Microsoft does not publish an official question count, but from its 100-minute exam (~40–65 total, ~10–20 in this domain), expect 3–7 from this objective — we provide 51 practice questions to prepare you well beyond it. (estimate)

51questions here

11free pages

9concepts

25–30%of the exam

Questions 41–45

41application · medium
A utility company wants to deploy an agent that automates responses to customer inquiries about power outages. The agent will use data from the outage management system. The system contains real-time outage information, but also includes historical outage records from the past decade. What should the team do to ensure the agent provides accurate responses?
Select an answer first
42expert · hard
A media company wants to deploy an agent that recommends content to users based on their viewing history. The viewing history is stored in a data warehouse with a star schema. The team wants to integrate data from a new recommendation engine that uses a different data model. What should the team do to ensure the agent can use both data sources effectively?
Select an answer first
43expert · hard
A bank is developing an AI agent to approve small business loans. The agent will use historical loan performance data, current economic indicators, and applicant credit scores. The bank's risk committee requires that the agent's decisions be explainable and that the data be no more than one day old. What is the primary challenge the architect must address?
Select an answer first
44foundation · easy
When reviewing data for an AI solution, what does 'data accuracy' refer to?
Select an answer first
45application · medium
A telecommunications company wants to deploy an agent that analyzes customer call logs to identify common service issues. The call logs are stored in a data lake and are updated continuously. The agent will generate a daily summary of top issues. The team notices that some call logs are missing the issue category field. What should the team do to ensure the agent's analysis is reliable?
Select an answer first

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Analyze Requirements for AI-powered Business Solutions AB-100 Practice Questions (Page 9)

Questions 41–45

When reviewing data for an AI solution, what does 'data accuracy' refer to?

Keep studying — Plan AI-powered business solutions