AI-102 Dumps With Exact Questions and Answers

The requirement is to process images of forms with Azure Form Recognizer (now part of Azure AI Document Intelligence) while ensuring that the image files are not stored in the cloud.

When sending images directly to the Form Recognizer REST API endpoint, you have two options:

Send the file as binary data in the request body (for local file upload).

Send a URL reference (if the file is stored in Azure Blob Storage or another public location).

Since the requirement explicitly states that the image files must not be stored in the cloud, you cannot send a URL. Instead, you must send the raw binary content of the file directly in the request body.

Option A – raw image binary

Correct. This allows you to directly send the local image file to the API without uploading/storing it in the cloud.

Option B – form URL encoded

Incorrect. Form Recognizer does not accept URL-encoded form data.

Option C – JSON

Incorrect. JSON is only used when providing the API with the URL of a cloud-stored document, not when sending the actual file.

Correct Answer: A. raw image binary

Form Recognizer REST API – Analyze a document

Quickstart: Extract layout and text – Document Intelligence"If your document is available locally, pass the file as binary data in the request body."

Microsoft References

To build a product support chatbot from a product support manual with minimal development effort and cost, use Custom question answering (CQA) in Azure AI Language. CQA lets you ingest files (including manuals/FAQs), automatically extract Q&A, author answers where needed, and expose a ready-to-call endpoint that plugs straight into Azure Bot Service—no fine-tuning or complex retrieval stack required.

Not A (Phi-3 with fine-tuning): fine-tuning adds cost/complexity and isn’t necessary for a manual-based Q&A bot.

Not C (GPT-4 with grounding via Azure AI Search): powerful but more engineering and cost (indexing, RAG orchestration) than needed.

Not D (Document Intelligence): focuses on OCR/structured data extraction, not conversational Q&A.

References (Microsoft Docs):

Custom question answering overview and scenario (create a conversational layer over your data; supports manuals/FAQs).

CQA supports unstructured documents and quick setup in Language Studio/Azure AI Foundry.

The agent will reason over the uploaded file.●○

The code will create a run and check the output.○●

The code will create an agent and enable a file search.○●

 The agent will reason over the uploaded file.

Yes: The code includes file = project_client.agents.upload_file_and_poll(file_path="./data/file1.zip", purpose=FilePurpose.AGENTS). The upload_file_and_poll method with FilePurpose.AGENTS suggests that the file (file1.zip) is uploaded for the agent to process, and agents in the Azure AI Foundry Agent Service are typically designed to reason over uploaded data. This indicates the agent will reason over the uploaded file.

 The code will create a run and check the output.

No: The provided code snippet does not include any explicit call to create a run (e.g., a method like create_run or start_run) or check the output (e.g., a method to retrieve or poll run results). The code only uploads a file and creates a vector store, but it does not initiate or monitor a run.

 The code will create an agent and enable a file search.

No: The code does not contain a method to create an agent (e.g., create_agent) or explicitly enable a file search. The create_vector_store_and_poll method creates a vector store, which is typically used for indexing data for search, but there is no indication of enabling a file search or creating an agent in this snippet.