Last Attempt AI-102 Questions

The app must:

Identify road signs and extract text from images

This is an OCR (Optical Character Recognition) task.

Azure AI Vision provides OCR capabilities to detect and extract text from images (including road signs).

Correct choice: Azure AI Vision.

Analyze the text to identify well-known locations

Once text is extracted, you need to detect if it refers to well-known places (e.g., cities, landmarks).

This is a Named Entity Recognition (NER) problem.

Azure AI Language service includes entity recognition for identifying locations, people, organizations, etc.

Correct choice: Azure AI Language.

Other options:

Azure AI Document Intelligence → Focused on structured documents (invoices, forms), not road signs.

Azure AI Search → Useful for indexing and search, not text extraction or entity recognition.

Correct Answer Matrix:

Extract the text → Azure AI Vision

Identify well-known locations → Azure AI Language

Azure AI Vision – OCR text extraction

Azure AI Language – Named Entity Recognition (NER)

Microsoft References

For consistent, deterministic answers about policy PDFs with minimal effort, use Azure AI Language – Question Answering (Custom Question Answering). You can ingest PDFs into a knowledge base and define/curate the exact answers. The service then returns the same answer for the same question every time (subject to your KB content and configuration), avoiding the variability of generative models and custom ML. Microsoft Azure+2

Microsoft References:

What is Custom Question Answering? (Language service). Microsoft Learn

Azure AI Language – Question Answering product page. Microsoft Azure

Azure AI Language overview (includes Question Answering feature)

Step 1: Export the model using the Export for containers (GZIP) option.

Export versioned app's package from LUIS portal

The versioned app's package is available from the Versions list page.

Sign on to the LUIS portal.

Select the app in the list.

Select Manage in the app's navigation bar.

Select Versions in the left navigation bar.

Select the checkbox to the left of the version name in the list.

Select the Export item from the contextual toolbar above the list.

Select Export for container (GZIP).

The package is downloaded from the browser.

Step 2: Select v1.1 of app1.

A trained or published app packaged as a mounted input to the container with its associated App ID.

Step 3: Run a contain and mount the model file.

Run the container, with the required input mount and billing settings.

When deploying an Azure OpenAI model using the Microsoft.CognitiveServices/accounts/deployments resource in an ARM template, the throughput for a Standard deployment is set on the SKU object. Specifically:

Use the capacity field of the SKU to specify the per-deployment rate.

Provide the integer value that represents the requests per minute (RPM) you want the deployment to handle.

Therefore, to meet the requirement of 600 requests per minute, complete the template as:

"sku": {

"name": "Standard",

"capacity": 600

}

This uses a single deployment SKU with a defined capacity. The ARM schema for accounts/deployments explicitly includes sku.capacity for resources that support scale (Azure OpenAI deployments do), and Azure OpenAI quota/rate-limit guidance confirms that rate limits are assigned per deployment.

Microsoft Azure AI Solution References

ARM template—Microsoft.CognitiveServices/accounts/deployments (2023-05-01): property reference showing sku.capacity. Microsoft Learn

Resource type reference—latest accounts/deployments: confirms SKU usage on deployments. Microsoft Learn

Azure OpenAI quota and rate limits: explains assigning deployment-level limits (RPM/TPM). Microsoft Learn