AI-102 Exam Dumps - Microsoft Azure AI Engineer Associate Questions and Answers

Key Knowledge:

To analyze images with Azure OpenAI, you must use a GPT-4 model with vision support (model version vision-preview ).

This requires deploying a GPT-4 model for vision.

Then you use the Chat playground (not completions, not DALL-E) since you want analysis, not generation.

You need to give the assistant instructions in the system message field (e.g., “You are an AI assistant that describes images”).

Finally, you upload an image and prompt the model to describe it.

Correct Sequence:

Create a new deployment, select a GPT-4 model, and set Model version to vision-preview.

Because GPT-4 with vision is required.

Open Chat playground and select the deployed model.

Vision capabilities are accessed through the Chat playground.

In the System message field, enter: “You are an AI assistant that describes images.”

This sets the assistant’s role.

In the Chat session pane, enter a text prompt of “Describe this image,” and upload an image by using the attachment button.

This triggers the model to analyze and describe the image.

Verified Answer (Correct Order):

Create a new deployment, select a GPT-4 model, and set Model version to vision-preview.

Open Chat playground and select the deployed model.

In the System message field, enter: You are an AI assistant that describes images.

In the Chat session pane, enter: Describe this image , and upload an image by using the attachment button.

Microsoft References:

Azure OpenAI vision capabilities

Azure OpenAI Studio overview

Box 1: mcr.microsoft.com/azure-cognitive-services/textanalytics/sentiment

To run the Sentiment Analysis v3 container, execute the following docker run command.

docker run --rm -it -p 5000:5000 --memory 8g --cpus 1 \

mcr.microsoft.com/azure-cognitive-services/textanalytics/sentiment \

Eula=accept \

Billing={ENDPOINT_URI} \

ApiKey={API_KEY} is the endpoint for accessing the Text Analytics API. https:// < your-custom-subdomain > .cognitiveservices.azure.com

Box 2: https://contoso.cognitiveservices.azure.com

{ENDPOINT_URI} is the endpoint for accessing the Text Analytics API: https:// < your-custom-subdomain > .cognitiveservices.a The endpoint for accessing the Text Analytics API. zure.com

Video Indexer supports multiple Person models per account. Once a model is created, you can use it by providing the model ID of a specific Person model when uploading/indexing or reindexing a video. Training a new face for a video updates the specific custom model that the video was associated with.

Note: Video Indexer supports face detection and celebrity recognition for video content. The celebrity recognition feature covers about one million faces based on commonly requested data source such as IMDB, Wikipedia, and top LinkedIn influencers. Faces that aren ' t recognized by the celebrity recognition feature are detected but left unnamed. Once you label a face with a name, the face and name get added to your account ' s Person model. Video Indexer will then recognize this face in your future videos and past videos.

Box 1: Yes

http://localhost:5000/status : Also requested with GET, this verifies if the api-key used to start the container is valid without causing an endpoint query.

Box 2: Yes

The command saves container and LUIS logs to output mount at C:\output, located on container host

Box 3: Yes

http://localhost:5000/swagger : The container provides a full set of documentation for the endpoints and a Try it out feature. With this feature, you can enter your settings into a web-based HTML form and make the query without having to write any code. After the query returns, an example CURL command is provided to demonstrate the HTTP headers and body format that ' s required.

To perform sentiment analysis, we specify TextAnalytics, not ContentModerator.

Possible SKU names include: ' F0 ' , ' F1 ' , ' S0 ' , ' S1 ' , ' S2 ' , ' S3 ' , ' S4 ' , ' S5 ' , ' S6 ' , ' S7 ' , ' S8 '

Possible location names include: westus, eastus

Box 1: GeographyV2

The prebuilt geographyV2 entity detects places. Because this entity is already trained, you do not need to add example utterances containing GeographyV2 to the application intents.

Box 2: Email

Email prebuilt entity for a LUIS app: Email extraction includes the entire email address from an utterance. Because this entity is already trained, you do not need to add example utterances containing email to the application intents.

Box 3: Machine learned

The machine-learning entity is the preferred entity for building LUIS applications.

You are building a chatbot that will use an Azure OpenAI model.

You need to deploy the model in your Azure subscription.

Key Steps to Deploy an Azure OpenAI Model for a Chatbot:

Apply for access to Azure OpenAI

Azure OpenAI is a gated service and requires approval before you can provision resources.

Provision an Azure OpenAI resource

After approval, you must provision an Azure OpenAI resource in your subscription.

Deploy the GPT model

Since the chatbot requires natural language understanding and conversation, you deploy a GPT model (e.g., GPT-4, GPT-3.5).

Actions That Are Not Required for this Question:

Provision Azure API Management → Not needed for basic deployment (used only for advanced API management scenarios).

Deploy the embeddings model → Used for search/RAG, not necessary for chatbot baseline.

Deploy the DALL-E model → Used for image generation, not chatbot text responses.

Correct Order:

Apply for access to Azure OpenAI.

Provision an Azure OpenAI resource.

Deploy the GPT model.

Verified Answer (Sequence):

Apply for access to Azure OpenAI

Provision an Azure OpenAI resource

Deploy the GPT model

Microsoft References:

Quickstart: Get started with Azure OpenAI

Deploying models in Azure OpenAI

Comprehensive Detailed Explanation

The scenario:

You are working with Azure AI Video Indexer.

You need to add a custom brand/logo and also configure an exclusion so that this brand is not indexed.

In Video Indexer’s REST API for brands, the configuration JSON includes:

" enabled " : true/false → Determines whether the brand should be indexed.

Setting " enabled " : false means the brand is excluded.

The other properties in the dropdown ( state , tags , useBuiltin ) are not correct in this context for configuring exclusions.

Therefore, to exclude a custom brand:

" enabled " : false

Correct Answer:

Attribute: " enabled "

Value: false

Microsoft References

Video Indexer – Brands customization

Azure Video Indexer API – Brands