To pass the Microsoft
AI-103 exam, focus on hands-on Azure AI
development, not just memorizing services. AI-103 is Microsoft's new
certification centered on Azure AI Foundry, Generative AI, AI Agents, RAG, and
multimodal AI applications. Braindump2go has assembled to take you through 90 Q&As
to your AI-103 Exam preparation: https://www.braindump2go.com/ai-103.html
QUESTION 1
You have a Microsoft Foundry project that contains a model deployment.
You have an application that calls the deployment by using the Azure OpenAI v1 API and DefaultAzureCredential.
The developers at your company receive HTTP 403 errors when they send inference requests, even after running az login.
You need to ensure that the developers can perform model inference. The solution must follow the principle of least privilege.
Which role-based access control (RBAC) role should you assign to the developers?
A. Cognitive Services User
B. Cognitive Services OpenAI User
C. Contributor
D. Cognitive Services Data Reader
Answer: B
QUESTION 4
You have a Microsoft Foundry project named Project1 that contains an agent. The agent uses an OpenAPI 3.0 specification to call an external weather service.
The weather service requires a key to be passed in an HTTP header. The key value is stored as a connection in Project1.
You need to ensure that the key value from the connection is included automatically whenever the OpenAPI tool is invoked.
What should you configure in the OpenAPI specification?
A. a header parameter defined for each operation
B. an Azure Key Vault connection
C. an API key security scheme
D. a Bearer token security scheme
Answer: C
QUESTION 5
You have a Microsoft Foundry project that serves a high-volume chat app.
Most requests are simple FAQs, but some require advanced reasoning.
You need to reduce costs and latency for common queries, without degrading the quality of the responses to complex questions.
What should you do?
A. Route all the requests to a smaller model.
B. Use a model cascade that routes the requests to different models.
C. Increase the value of the max_tokens parameter for all the requests.
D. Route all the requests to the most capable model.
Answer: B
QUESTION 6
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
Answer: A
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.
Answer: C
QUESTION 8
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.
Answer: D
QUESTION 9
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.
Answer: B
QUESTION 10
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.
Answer: B
QUESTION 11
You have a Microsoft Foundry project named Project1.
Project1 contains an application that processes PDF vendor invoices.
You need to configure Azure Document Intelligence in Foundry Tools to generate a Markdown output that preserves the sections and table structure of the PDFs. The solution must minimize development effort.
What should you do?
A. Configure output=figures when you analyze the PDF.
B. Configure content=markdown when you analyze the document.
C. Increase the confidence threshold.
D. Set the output_content_format=ContentFormat.MARKDOWN value.
Answer: D
QUESTION 12
You have a Microsoft Foundry project that ingests scanned PDF invoices stored in Azure Blob Storage. Each invoice contains printed fine items and has a table-based layout.
Extracted results are stored as structured JSON and used as grounding data for an agent in a Retrieval Augmented Generation (RAG) solution.
You need to create a single analyzer that meets the following requirements:
- Extracts the invoice number, invoice date, vendor name, and total amount across varying templates
- Returns confidence scores so that results with confidence below 0.80 can be routed for supervisor review
What should you use?
A. a Foundry agent that has groundedness guardrails enabled to extract invoice fields and confidence scores
B. a custom Azure Content Understanding in Foundry Tools analyzer that defines the required fields as the extracted fields and the returned confidence scores for routing
C. the Azure Content Understanding in Foundry Tools prebuilt-layout analyzer
D. the Azure Content Understanding in Foundry Tools prebuilt-documentSearch analyzer and search.score from the Azure AI Search results for routing
Answer: B
QUESTION 13
You have a Microsoft Foundry project that uses Azure AI Search to ground an agent in internal documentation.
After a recent content update, users report that the agent’s answers have become less accurate.
You need to identify whether the retrieved content is negatively influencing the model’s generated responses.
Which observability signal should you review?
A. indexer status and failure history
B. latency breakdown traces
C. prediction drift metrics
D. groundedness evaluation metrics
Answer: D
QUESTION 14
You have an Azure subscription that contains an Azure OpenAI resource.
You need to build an app that will suggest product names from a given product description.
Which model should you use?
A. Whisper
B. DALL-E
C. embeddings
D. GPT-4
Answer: D