This is the free Microsoft AI-103 practice question bank —
30 of 57 total questions, each with a full explanation, free to
read with no signup required. Updated 2026-07-09.
Every answer is verified against official Microsoft documentation —
see our methodology.
Question 1
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
Users report that some responses omit required regulatory clauses, even when the clauses are present in the retrieved content.
You need to improve response completeness.
Solution: You increase the value of the max_tokens parameter.
Does this meet the goal?
A. Yes
B. No
Show Answer
Correct Answer: B
Explanation: Increasing max_tokens only increases the maximum allowed output length. The issue is that required regulatory clauses are being omitted despite being present in the retrieved context, which is a content selection/grounding problem rather than an output-length limit. Raising max_tokens does not ensure those clauses will be included.
Sources:
https://www.freecram.net/question/Microsoft.MS-102.v2026-06-01.q246/note-this-question-is-part-of-a-series-of-questions-that-present-the-same-scenario-each-question-in
Question 2
Case Study -
This is a case study. Case studies are not timed separately from other exam sections. You can use as much exam time as you would like to complete each case study. However, there might be additional case studies or other exam sections. Manage your time to ensure that you can complete all the exam sections in the time provided. Pay attention to the Exam Progress at the top of the screen so you have sufficient time to complete any exam sections that follow this case study.
To answer the case study questions, you will bed to reference information that is provided in the case. Case studies and associated questions might contain exhibits or other resources that provide more information about the scenario described in the case. Information provided in an individual question does not apply to the other questions in the case study.
A Review Screen will appear at the end of this case study. From the Review Screen, you can review and change your answers before you move to the next exam section. After you leave this case study, you will NOT be able to return to it.
To start the case study -
To display the first question in this case study, select the “Next” button. To the left of the question, a menu provides links to information such as business requirements, the existing environment, and problem statements. Please read through all this information before answering any questions. When you are ready to answer a question, select the “Question” button to return to the question.
Overview -
Company Information -
Contoso, Ltd is a multinational retail company that builds, deploys, and manages generative AI and agent-based solutions by using Microsoft Foundry.
Existing Environment -
Identity Environment -
Contoso uses Microsoft Entra ID for identity management, authentication, and authorization capabilities that enable agents to access organizational resources and services.
Contoso recently formed a new AI engineering team named Agent1Dev Team to optimize and maintain existing AI solutions.
The team collaborates with solution architects, DevOps engineers, and security engineers to design, implement. monitor, and secure AI applications.
Contoso also has a team named Agent1Test Team that is responsible for validating AI solutions before the solution deployments.
Generative Environment -
Contoso has a Microsoft Foundry deployment that contains two projects named Project1 and Project2.
Project1 -
Project1 contains a customer support agent named Agent1 that assists customers with product inquiries and troubleshooting requests.
Agent1 has the following configurations:
Agent1 uses a base model deployment.
A safety evaluation pipeline is NOT enabled.
Tool invocation approval workflows are NOT enabled.
Conversation memory constraints are NOT configured.
Agent1 interacts with customers by using digital support channels and answers general questions about Contoso products.
Project1 is deployed to an Azure region located in the European Union (EU).
Agent1Dev Team will use Project1 to optimize and maintain Agent1.
Project2 -
Project2 contains a deployed video generation model. The marketing department at Contoso has access to Project2 and plans to use the model to develop a video creation solution.
Development of the solution is incomplete.
Data Environment -
Contoso stores product-related information in Azure resources that support AI applications.
The Azure environment contains an Azure Blob Storage account named storage1 that stores product detail sheets for all the Contoso products.
The product sheets include specifications, feature descriptions, and product support information that Agent1 can use to answer customer questions. The product sheets are stored in the PDF format.
Problem Statements -
Contoso identifies the following issues:
Agent1 has only general knowledge of the Contoso products.
A recent chat interaction with Agent1 was analyzed for sentiment. The results of the analysis have NOT been processed yet.
Agent1 does NOT use the detailed product information in the product sheets stored in storage1 when responding to customer questions.
The finance department at Contoso reports that vendor invoices must be reviewed manually to ensure that the invoices match the terms defined in the vendor contracts. The invoices contain tables, logos, and varied layouts that make the documents difficult to process consistently.
Requirements -
Planned Changes -
Contoso plans to implement the following changes:
Implement a solution for Project1 that analyzes the vendor invoices by evaluating both the visual layout and the textual content of the invoices, so that the invoice details can be verified against the vendor contract terms.
Update the base model deployment used by Agent1 and standardize the model version to ensure continuity and consistent responses.
Enable Agent1 to retrieve and use the detailed product information from the product sheets stored in storage1.
Implement an indexing solution for the product sheets that Agent1 can use to answer customer questions.
Complete the development of the video creation solution.
Technical Requirements -
Contoso identifies the following technical requirements:
The model deployment used by Agent1 must support scalable, high-throughput generative AI workloads and dynamically scale to handle variable customer support traffic, without requiring reserved throughput capacity.
The product sheets must be processed by using an indexing pipeline that enables semantic and vector search, so that Agent1 can retrieve the relevant product information.
Responses generated by using the product sheet information must be relevant, complete, and accurate.
Agent1 must be able to use the product sheets to answer natural language questions about product details.
The model version used by Agent1 must remain consistent to ensure stable responses.
The data processed by the model must remain within the EU.
Security and Compliance Requirements
Contoso identifies the following security and compliance requirements:
API keys must NOT be used to access Foundry-deployed models.
Access to the Azure resources must follow the principle of least privilege.
The developers at Contoso must authenticate to Microsoft Foundry resources by using Microsoft Entra authentication.
Access to Project1 must be assigned to the members of Agent1Dev Team by using a security group named SC_Agent1_Dev.
Access to Project1 must be assigned to the members of Agent1Test Team by using a security group named SC_Agent1_Test.
Agent1 must never reveal customer information, even if a document that contains customer data is added erroneously to the product sheet repository in storage1.
The product sheets might contain images that include embedded text. Agent1 must be protected from malicious instructions potentially hidden within the images.
Business Requirements -
Contoso identifies the following business requirements:
Users that interact with Agent1 must have a personalized experience in future interactions, including the ability for Agent1 to retain conversation context and recall relevant information from previous interactions.
Agent1 must answer questions only about the products sold by Contoso.
You need to recommend a solution to support the planned changes and technical requirements for Agent1 to use the product information stored in storage1.
What should you include in the recommendation?
A. Azure Translator in Foundry Tools
B. Grounding with Bing Search
C. Azure AI Search
D. Azure Document intelligence in Foundry Tools
Show Answer
Correct Answer: C
Explanation: The requirement is to process PDFs into an indexing pipeline that supports semantic and vector search so the agent can retrieve relevant product information for natural language questions. Azure AI Search provides indexing, vector and semantic search, and is the standard retrieval layer for RAG with Azure AI Foundry. Azure Document Intelligence extracts document content but is not the retrieval/indexing solution. Bing Search grounds on web content, not private Blob Storage, and Translator is unrelated.
Question 3
Case Study -
This is a case study. Case studies are not timed separately from other exam sections. You can use as much exam time as you would like to complete each case study. However, there might be additional case studies or other exam sections. Manage your time to ensure that you can complete all the exam sections in the time provided. Pay attention to the Exam Progress at the top of the screen so you have sufficient time to complete any exam sections that follow this case study.
To answer the case study questions, you will bed to reference information that is provided in the case. Case studies and associated questions might contain exhibits or other resources that provide more information about the scenario described in the case. Information provided in an individual question does not apply to the other questions in the case study.
A Review Screen will appear at the end of this case study. From the Review Screen, you can review and change your answers before you move to the next exam section. After you leave this case study, you will NOT be able to return to it.
To start the case study -
To display the first question in this case study, select the “Next” button. To the left of the question, a menu provides links to information such as business requirements, the existing environment, and problem statements. Please read through all this information before answering any questions. When you are ready to answer a question, select the “Question” button to return to the question.
Overview -
Company Information -
Contoso, Ltd is a multinational retail company that builds, deploys, and manages generative AI and agent-based solutions by using Microsoft Foundry.
Existing Environment -
Identity Environment -
Contoso uses Microsoft Entra ID for identity management, authentication, and authorization capabilities that enable agents to access organizational resources and services.
Contoso recently formed a new AI engineering team named Agent1Dev Team to optimize and maintain existing AI solutions.
The team collaborates with solution architects, DevOps engineers, and security engineers to design, implement. monitor, and secure AI applications.
Contoso also has a team named Agent1Test Team that is responsible for validating AI solutions before the solution deployments.
Generative Environment -
Contoso has a Microsoft Foundry deployment that contains two projects named Project1 and Project2.
Project1 -
Project1 contains a customer support agent named Agent1 that assists customers with product inquiries and troubleshooting requests.
Agent1 has the following configurations:
Agent1 uses a base model deployment.
A safety evaluation pipeline is NOT enabled.
Tool invocation approval workflows are NOT enabled.
Conversation memory constraints are NOT configured.
Agent1 interacts with customers by using digital support channels and answers general questions about Contoso products.
Project1 is deployed to an Azure region located in the European Union (EU).
Agent1Dev Team will use Project1 to optimize and maintain Agent1.
Project2 -
Project2 contains a deployed video generation model. The marketing department at Contoso has access to Project2 and plans to use the model to develop a video creation solution.
Development of the solution is incomplete.
Data Environment -
Contoso stores product-related information in Azure resources that support AI applications.
The Azure environment contains an Azure Blob Storage account named storage1 that stores product detail sheets for all the Contoso products.
The product sheets include specifications, feature descriptions, and product support information that Agent1 can use to answer customer questions. The product sheets are stored in the PDF format.
Problem Statements -
Contoso identifies the following issues:
Agent1 has only general knowledge of the Contoso products.
A recent chat interaction with Agent1 was analyzed for sentiment. The results of the analysis have NOT been processed yet.
Agent1 does NOT use the detailed product information in the product sheets stored in storage1 when responding to customer questions.
The finance department at Contoso reports that vendor invoices must be reviewed manually to ensure that the invoices match the terms defined in the vendor contracts. The invoices contain tables, logos, and varied layouts that make the documents difficult to process consistently.
Requirements -
Planned Changes -
Contoso plans to implement the following changes:
Implement a solution for Project1 that analyzes the vendor invoices by evaluating both the visual layout and the textual content of the invoices, so that the invoice details can be verified against the vendor contract terms.
Update the base model deployment used by Agent1 and standardize the model version to ensure continuity and consistent responses.
Enable Agent1 to retrieve and use the detailed product information from the product sheets stored in storage1.
Implement an indexing solution for the product sheets that Agent1 can use to answer customer questions.
Complete the development of the video creation solution.
Technical Requirements -
Contoso identifies the following technical requirements:
The model deployment used by Agent1 must support scalable, high-throughput generative AI workloads and dynamically scale to handle variable customer support traffic, without requiring reserved throughput capacity.
The product sheets must be processed by using an indexing pipeline that enables semantic and vector search, so that Agent1 can retrieve the relevant product information.
Responses generated by using the product sheet information must be relevant, complete, and accurate.
Agent1 must be able to use the product sheets to answer natural language questions about product details.
The model version used by Agent1 must remain consistent to ensure stable responses.
The data processed by the model must remain within the EU.
Security and Compliance Requirements
Contoso identifies the following security and compliance requirements:
API keys must NOT be used to access Foundry-deployed models.
Access to the Azure resources must follow the principle of least privilege.
The developers at Contoso must authenticate to Microsoft Foundry resources by using Microsoft Entra authentication.
Access to Project1 must be assigned to the members of Agent1Dev Team by using a security group named SC_Agent1_Dev.
Access to Project1 must be assigned to the members of Agent1Test Team by using a security group named SC_Agent1_Test.
Agent1 must never reveal customer information, even if a document that contains customer data is added erroneously to the product sheet repository in storage1.
The product sheets might contain images that include embedded text. Agent1 must be protected from malicious instructions potentially hidden within the images.
Business Requirements -
Contoso identifies the following business requirements:
Users that interact with Agent1 must have a personalized experience in future interactions, including the ability for Agent1 to retain conversation context and recall relevant information from previous interactions.
Agent1 must answer questions only about the products sold by Contoso.
You need to configure an indexing pipeline for Agent1 to retrieve the relevant product information in storage1. The solution must meet the technical requirement.
Which two built-in skills should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Azure OpenAI Embedding
B. Entity Recognition
C. Text Split
D. Merge
E. Language Detection
F. key phrase extraction
Show Answer
Correct Answer: A, C
Explanation: To support semantic and vector search, the indexing pipeline should first split PDF content into manageable chunks using the Text Split skill, then generate vector embeddings for those chunks using the Azure OpenAI Embedding skill. This enables retrieval-augmented generation with relevant product information. The other listed skills enrich content but are not required to build a vector index for semantic retrieval.
Question 4
You have an application named App1 that uses Azure Speech in Foundry Tools to transcribe live calls.
Transcript segments often contain both English and Spanish. App1 sends each segment to Azure Translator in Foundry Tools to translate to another language.
Sometimes, mixed-language segments result in incomplete or incorrect translations.
You need to reduce translation errors. The solution must ensure that the entire transcript is translated successfully.
What should you do before sending the segments to Translator?
A. Use document translation to translate the entire transcript as a single document.
B. Split the mixed-language segments into single-language segments and translate each segment separately.
C. Enable automatic language detection for the translation request.
D. Specify English as the source language in the translation request for all the segments.
Show Answer
Correct Answer: B
Explanation: Azure Translator expects a single source language per translation request. Mixed-language text in one segment can lead to poor or incomplete translations because automatic detection identifies only one source language for the request. Splitting transcript segments into single-language portions before translation allows each portion to be translated accurately and completely. Document translation does not resolve mixed-language content within a segment, and forcing English as the source causes Spanish text to be mistranslated.
Question 5
You are creating an agent workflow in a Microsoft Foundry project to support natural voice interactions.
The agent must receive continuous audio input, convert the input into text for reasoning, and then return spoken responses to a user. The workflow must meet the following requirements:
Support turn-taking dynamics, where the agent begins to generate the speech output before the user finishes speaking.
Operate with low latency to maintain conversational experience.
You need to enable both speech to text and text to speech in a real-time agent interaction.
What should you do?
A. Use batch transcription to convert the audio input and return text responses from the agent.
B. Use real-time speech to text for incoming audio and text to speech for agent responses.
C. Use an embeddings model to encode the audio, and then decode the audio into text and speech.
D. Use speech translation to convert the audio into another language and return the translated text.
Show Answer
Correct Answer: B
Explanation: A real-time voice agent requires streaming speech-to-text for incoming audio and text-to-speech for outgoing responses to achieve low latency and support conversational turn-taking. Batch transcription introduces too much delay, embeddings do not perform transcription or speech synthesis, and speech translation is intended for language translation rather than enabling natural voice interactions.
Question 6
You are building a speech processing solution in Microsoft Foundry for a customer support platform.
The platform will transcribe live phone calls, so that supervisors at your company can view call transcripts and detect issues while the calls are in progress. The call audio will arrive as a continuous stream from the telephony system.
You need to ensure that the call transcripts appear within only a few seconds of the audio stream.
What should you do?
A. Use text to speech by using a custom neural voice.
B. Use speech translation to generate the transcripts into multiple languages.
C. Run a batch transcription job on recorded audio files.
D. Use real-time speech to text to process streaming audio input.
Show Answer
Correct Answer: D
Explanation: Real-time speech to text is designed for low-latency transcription of streaming audio, making transcripts available within seconds. Batch transcription is intended for prerecorded audio, text-to-speech generates audio rather than transcribing it, and speech translation focuses on translating speech rather than providing the required real-time transcription.
Question 7
You have a Microsoft Foundry project that contains an agent.
The knowledge source for the agent is a set of scanned PDF troubleshooting guides stored in Azure Blob Storage. The guide pages contain two-column layouts and tables.
You use Azure Content Understanding in Foundry Tools to process the PDFs.
You plan to ingest the processed content into an index for Retrieval Augmented Generation (RAG) and store extracted fields for downstream automation.
Stakeholders must be able to verify where each extracted field value came from in the original PDF and route low-reliability extractions for manual review.
You need to ensure that the Content Understanding document analyzer output includes a per-field confidence score and source grounding to locations within the source document.
What should you do?
A. Set enableSegment to true.
B. Provide labeled samples.
C. Enable estimateFieldSourceAndConfidence.
D. Configure the analyzer to use generative extraction for all fields.
Show Answer
Correct Answer: C
Explanation: The option specifically intended to include per-field confidence scores and source grounding (such as page location/bounding boxes) in Azure Content Understanding output is estimateFieldSourceAndConfidence. This enables verification of extracted values against the original PDF and supports routing low-confidence extractions for manual review. The other options do not provide both per-field confidence and source provenance.
Question 8
You have a Microsoft Foundry project that contains an agent.
The agent uses Azure AI Search as the retriever.
You plan to ingest PDF into an Azure AI Search index to ensure that the agent can ground responses in texts in both documents and embedded images.
Users require citations that link to the source files.
You need to ensure that during indexing, the images are extracted into a structure that can be used as input for the built-in optical character recognition (OCR) skill.
Which indexing approach should you use?
A. an indexer to extract image data into a normalized_images collection
B. a Shaper skill to restructure the OCR input
C. a skillset to run the OCR skill directly against the content field of the index
D. the outputFieldMappings parameter to write image data to a searchable field
Show Answer
Correct Answer: A
Explanation: The built-in OCR skill expects image inputs. Azure AI Search indexers can extract embedded images from PDFs into the normalized_images collection, which is the standard input for the OCR skill. This also preserves the document context needed for source citations. A Shaper skill only reshapes data, OCR cannot run directly on a text content field, and outputFieldMappings control where processed outputs are written rather than extracting images.
Question 9
You have a Microsoft Foundry project that contains an agent.
You need to process mixed-format documents that contain scanned text, tables, and multicolumn layouts. The extracted content must preserve the document structure and be converted into the Markdown format for downstream reasoning.
What should you configure first?
A. an Azure Language in Foundry Tools text analysis model deployment
B. a generative chat completion request
C. an Azure OpenAI Responses API call that uses a multimodal model
D. an Azure Content Understanding in Foundry Tools analyzer
Show Answer
Correct Answer: D
Explanation: Azure Content Understanding analyzers are designed to ingest mixed-format documents, extract text while preserving structure (including tables and multi-column layouts), and produce structured outputs such as Markdown suitable for downstream reasoning. Language text analysis, chat completions, or a direct multimodal Responses API call are not the initial document-structure extraction component.
Question 10
You have an app named App1 that uses a Microsoft Foundry multimodal model deployment.
App1 runs optical character recognition (OCR) on uploaded images and appends the OCR output to the prompt as additional context.
Some uploaded images contain embedded text.
You need to prevent potentially malicious instructions from being processed by the model.
What should you use?
A. image moderation
B. prompt shields for documents
C. protected material text
D. prompt shields for user prompts
Show Answer
Correct Answer: B
Explanation: Prompt shields for documents are designed to detect and mitigate prompt injection attacks originating from untrusted document content, including OCR-extracted text appended to prompts. Since the embedded text comes from uploaded images and is treated as document content, this is the appropriate protection. Image moderation detects unsafe visual content, protected material text relates to copyrighted content, and prompt shields for user prompts target direct user prompt injection rather than third-party document content.
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