Try 10 focused AI-901 questions on Identify AI Concepts and Capabilities, with explanations, then continue with IT Mastery.
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| Field | Detail |
|---|---|
| Exam route | AI-901 |
| Topic area | Identify AI Concepts and Capabilities |
| Blueprint weight | 43% |
| Page purpose | Focused sample questions before returning to mixed practice |
Use this page to isolate Identify AI Concepts and Capabilities for AI-901. Work through the 10 questions first, then review the explanations and return to mixed practice in IT Mastery.
| Pass | What to do | What to record |
|---|---|---|
| First attempt | Answer without checking the explanation first. | The fact, rule, calculation, or judgment point that controlled your answer. |
| Review | Read the explanation even when you were correct. | Why the best answer is stronger than the closest distractor. |
| Repair | Repeat only missed or uncertain items after a short break. | The pattern behind misses, not the answer letter. |
| Transfer | Return to mixed practice once the topic feels stable. | Whether the same skill holds up when the topic is no longer obvious. |
Blueprint context: 43% of the practice outline. A focused topic score can overstate readiness if you recognize the pattern too quickly, so use it as repair work before timed mixed sets.
These questions are original IT Mastery practice items aligned to this topic area. They are designed for self-assessment and are not official exam questions.
Topic: Identify AI Concepts and Capabilities
A team is building a lightweight help-desk chat feature. It will use a generative AI model deployed from Microsoft Foundry, and a web app must collect employee questions, send prompts, and show the replies. Which description best distinguishes the model from the application?
Options:
A. The web app generates replies; the deployed model stores the chat history.
B. The deployed model generates replies; the web app sends prompts and displays responses.
C. The Foundry portal answers users directly; the model formats the user interface.
D. The system prompt displays replies; the web app trains the model.
Best answer: B
Explanation: In a generative AI solution, the deployed model is the component that produces new content from prompts. The application is the client that gathers user input, adds any needed instructions such as a system prompt, sends the request to the model deployment, and displays the model’s response. The app may also manage conversation flow or UI behavior, but it is not the content-generating model. The key distinction is generation versus orchestration and presentation.
Topic: Identify AI Concepts and Capabilities
A developer is building a lightweight Python chat client that will send user prompts to a generative AI model in Microsoft Foundry. Which need maps to the principle that a model deployment must exist first?
Options:
A. Reading responsible AI documentation
B. Comparing model descriptions in the catalog
C. Calling the model from application code
D. Writing a user prompt in the chat UI
Best answer: C
Explanation: In Microsoft Foundry, a deployment makes a selected model available for use by applications. A lightweight client does not call an undeployed catalog model directly; it sends requests to a specific deployed model endpoint or deployment name. Exploring models, drafting prompts, or reviewing guidance can happen before deployment, but application code needs a deployed target to invoke.
Topic: Identify AI Concepts and Capabilities
A generative AI assistant summarizes insurance policy clauses. Some source documents may be outdated, and the model may infer missing context. The product team wants each summary to state uncertainty, identify source limitations, and flag cases that require human review. Which responsible AI concept is this primarily addressing?
Options:
A. Privacy and security
B. Transparency and user disclosure
C. Inclusiveness
D. Fairness
Best answer: B
Explanation: Transparency and user disclosure focus on helping people understand AI system behavior and limitations. In this scenario, the key need is not to make the summary sound more confident, but to tell users when the output may be incomplete, uncertain, or dependent on limited sources. That kind of disclosure reduces over-reliance by signaling when human judgment or review is needed. Accountability may involve assigning review responsibility, but the communication of uncertainty and limitations is primarily a transparency practice.
Topic: Identify AI Concepts and Capabilities
A retail company wants an AI feature that reads customer review comments and identifies the overall sentiment, key phrases, and product names mentioned in each review. Which primary AI workload does this scenario describe?
Options:
A. Computer vision
B. Image generation
C. Speech
D. Text analysis
Best answer: D
Explanation: Text analysis is the primary workload when an AI solution processes written language to find meaning, patterns, or labels. In this scenario, the input is customer review text, and the desired outputs are sentiment, key phrases, and product names. Those are common text analysis tasks because they interpret existing text rather than create images, understand visual content, or process audio. The key clue is that the source data and the results are both centered on language understanding.
Topic: Identify AI Concepts and Capabilities
A team is working in Microsoft Foundry. They have already chosen a generative AI model that supports their chat scenario. Which action best represents deploying the model rather than selecting it?
Options:
A. Choosing a model based on task fit
B. Creating an endpoint for test and app calls
C. Reviewing model descriptions and capabilities
D. Comparing models by supported modalities
Best answer: B
Explanation: Selecting a model means deciding which available model best fits the workload, such as choosing a chat-capable or multimodal model based on the task. Deploying a model is the next implementation step: making that selected model available so it can be tested in Foundry or called by a lightweight application. In fundamentals terms, deployment is about creating a usable model instance or endpoint, not comparing model families or reading capability descriptions. The key distinction is decision versus consumption readiness.
Topic: Identify AI Concepts and Capabilities
A team is testing a Microsoft Foundry chat app before release. During a test, the app returns a table that appears to include confidential employee compensation data. The team needs to diagnose the issue, minimize further exposure, and avoid deleting useful incident evidence. What is the best handling approach?
Options:
A. Lower the model temperature and continue testing
B. Delete the transcript and remove the test prompt
C. Add a disclaimer to warn users about sensitive output
D. Restrict the output and escalate for access review
Best answer: D
Explanation: When an AI system reveals information that should not be broadly visible, the safer handling approach is to reduce exposure and involve the appropriate owner rather than tuning around the symptom. The team should restrict access to the output or transcript, preserve only the evidence needed for investigation, and escalate to the data owner, security team, or responsible AI process. The likely root cause may be overly broad permissions, inappropriate grounding data, or missing controls around what the app can retrieve and display. Changing generation settings or adding a warning does not address unauthorized disclosure. The key takeaway is to treat sensitive AI output as a privacy and security incident, not just a prompt-quality issue.
Topic: Identify AI Concepts and Capabilities
A bank uses one automated loan-screening model for all applicants. The team requires the same scoring rule for every person, uses past approval data as input, and wants to understand why complaints of unfair outcomes may still be valid. Which explanation best fits this situation?
Options:
A. The model needs a larger compute deployment.
B. The issue is only transparency, not fairness.
C. The model must use separate rules for each group.
D. The training data may reflect biased past decisions.
Best answer: D
Explanation: Fairness in AI is not guaranteed by applying the same automated rule to everyone. If the model learns from biased historical approvals, incomplete inputs, or output labels that reflect unfair decisions, it can treat applicants identically in process while still producing unequal or unjust results. The problem is in what the system learned and what outcomes it optimizes, not necessarily in whether the same scoring code runs for each applicant.
The key takeaway is that fairness requires checking inputs, outputs, and impacts, not only confirming that the automation is uniform.
Topic: Identify AI Concepts and Capabilities
A city agency uses Azure Content Understanding in Foundry Tools to extract fields from uploaded inspection videos. The result must include only defects that are visible or audible in the recording, avoid assumptions about causes, and flag uncertain findings for review. Which approach is the best fit?
Options:
A. Generate a complete repair plan for every detected defect
B. Infer likely defect causes from the building age and neighborhood
C. Use image generation to recreate missing inspection views
D. Extract observed defects with evidence and confidence, then review low-confidence items
Best answer: D
Explanation: Extraction results should be grounded in the source content. For video, that means details that are visible in frames, audible in the audio, or otherwise provided by the uploaded source. If a defect is unclear, the safer design is to include evidence such as timestamps or extracted snippets, use confidence indicators when available, and route uncertain items to human review. The system should not add likely causes, missing views, or downstream plans unless those details are explicitly present in the content or supplied by another trusted source. The key takeaway is that extraction identifies information from content; it should not invent or infer facts beyond that content.
Topic: Identify AI Concepts and Capabilities
A city services team wants to analyze resident feedback messages. The solution must find references to named parks, departments, dates, phone numbers, and monetary amounts so staff can route and summarize issues. Which text analysis capability is the best fit?
Options:
A. Sentiment analysis
B. Language detection
C. Entity detection
D. Key phrase extraction
Best answer: C
Explanation: Entity detection is the text analysis capability used to identify and label specific references in unstructured text, such as locations, organizations, dates, phone numbers, and quantities. In this scenario, the team needs to locate structured references inside feedback messages so the information can support routing and summarization. That requirement is different from determining emotional tone, extracting general topics, or identifying the language of the text. The key signal is the need to find named or typed references rather than classify the overall message.
Topic: Identify AI Concepts and Capabilities
A developer is planning a vision feature for a home-assistance app. Users will upload a photo of a room and ask questions such as, “Is there a clear path from the door to the desk?” Which vision capability best matches this need?
Options:
A. Image generation
B. Visual question answering
C. Image classification
D. Object detection only
Best answer: B
Explanation: Visual question answering maps a user’s question to information found in an image. In this scenario, the app must interpret the room layout and answer whether a path is clear, not just label the whole image or locate one object. A multimodal vision-capable model can combine the image input with the user’s text question to produce an image-based answer.
Object detection may help identify items, but by itself it does not answer the user’s specific question about the scene.
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