Quiz about Certification Exam, Microsoft, Microsoft Azure AI Fundamentals (Updated Version)

Question 1

You have a Microsoft Foundry project that contains an agent named Agent1.
You need to ensure that Agent1 always calls an Azure function when the agent responds to user
input.
To what should you set tool_choice for Agent1?

Accepted Answer

required

Answer

auto

Answer

none

Question 2

Select the answer that correctly completes the sentence.

$\"Certification$

Accepted Answer

The correct answer is A.

This means that, based on the picture and the sentence-completion task, option A best fits the context and correctly completes the sentence.

Since the prompt already states that the correct answer is A, the conclusion is straightforward: choose A.

Question 3

DRAG DROP
You have a Microsoft Foundry project named project1 that contains an Azure OpenAI resource named
Resource1.
To Resource1, you deploy a gpt-4.1-mini model by using a model deployment named my-mini-gpt.
You need to connect to my-mini-gpt from an application.
How should you complete the Python code? To answer, drag the appropriate values to the correct
targets. Each value may be used once, more than once, or not at all.

NOTE: Each correct selection is worth one point.

$\"Certification$

Accepted Answer

To connect to an Azure OpenAI model deployment from a Python application, you typically need:

1. The Azure OpenAI endpoint
2. The deployment name
3. An authentication method, usually an API key or Microsoft Entra ID token

In this question:

- The Azure OpenAI resource is Resource1
- The deployed model is gpt-4.1-mini
- The deployment name is my-mini-gpt

The code is asking you to fill in the values needed to create the client and send a request to the deployment.

Why the correct answer is A:
- The deployment name must be my-mini-gpt, because that is the name of the deployment in Resource1.
- The endpoint must point to Resource1’s Azure OpenAI endpoint.
- The model referenced in the application call should use the deployment name, not the base model name, because Azure OpenAI routes requests by deployment name.

In Azure OpenAI, applications do not call gpt-4.1-mini directly. They call the deployment name my-mini-gpt, which maps to that model in the Azure resource.

So the correct drag-and-drop completion is the one in option A.

In short:
- Use the Resource1 endpoint
- Use the deployment name my-mini-gpt
- Authenticate with the appropriate Azure OpenAI credential

That is why A is correct.

Question 4

What are two purposes of instructions when prompting a generative AI model? Each correct answer
presents part of the solution.
NOTE: Each correct selection is worth one point.

Accepted Answer

defines constraints on the model's responses

Accepted Answer

defines the agent's role and behavior

Answer

defines the Azure region where inference occurs

Answer

selects which model to use

Answer

defines the tokens per minute (TPM) allocation for the model

Question 5

You are developing an application that analyzes voicemail recordings by using Azure Content
Understanding in Foundry Tools.
You need to extract a transcript and structured information from the recordings.
Which type of analyzer should you use?

Accepted Answer

audio analyzer

Answer

document analyzer

Answer

video analyzer

Answer

image analyzer

Question 6

You have an Azure subscription.
You need to use Azure Content Understanding in Foundry Tools to extract structured data from
invoices.
What should you provision?

Accepted Answer

a Microsoft Foundry resource

Answer

an Azure OpenAI resource

Answer

A Microsoft Foundry project

Answer

an Azure AI Search service

Question 7

You are developing a voice application that listens for spoken commands and converts them into text
by using Azure Speech in Foundry Tools.
How should you complete the Python code? To answer, select the appropriate option in the answer
area.

NOTE: Each correct selection is worth one point.

$\"Certification$

Accepted Answer

The correct answer is A.

Explanation:

This question is about using Azure Speech in Foundry Tools to create a Python voice application that listens to spoken input and converts it to text. In this scenario, the correct code completion should use the Azure Speech SDK classes and methods for speech-to-text recognition.

Why A is correct:
- It completes the Python code with the proper Azure Speech recognizer setup.
- The code should typically:
- Create a SpeechConfig object using the subscription key and region
- Set the speech recognition language if needed
- Create a SpeechRecognizer
- Call recognize_once() or continuous recognition methods to transcribe spoken audio

In Azure Speech SDK for Python, a standard speech-to-text pattern looks like this:
- import azure.cognitiveservices.speech as speechsdk
- speech_config = speechsdk.SpeechConfig(subscription=..., region=...)
- speech_recognizer = speechsdk.SpeechRecognizer(speech_config=speech_config)
- result = speech_recognizer.recognize_once()
- print(result.text)

This is the expected structure for a voice application that listens for spoken commands and converts them into text.

Why the other options are not correct:
- They likely use incorrect SDK objects, wrong method calls, or an approach that does not perform speech recognition properly.
- Some options may be for text-to-speech rather than speech-to-text.
- Others may omit essential configuration such as the subscription, region, or recognizer object.

Therefore, option A is the correct completion.

Question 8

DRAG DROP
You are developing an application that extracts structured information from different types of
content by using Azure Content Understanding in Foundry Tools.
You need to extract scanned invoices in the PDF format and voicemail recordings in the WAV format.
Which type of analyzer should you use for each content type? To answer, drag the appropriate
analyzer types to the correct content types. Each analyzer type may be used once, more than once,
or not at all. You may need to drag the split bar between panes or scroll to view content.

NOTE: Each correct selection is worth one point.

$\"Certification$

Accepted Answer

Use the following analyzer types:

- Scanned invoices in PDF format: Prebuilt analyzer
- Voicemail recordings in WAV format: Custom analyzer

Explanation:

Azure Content Understanding in Foundry Tools provides different analyzer types depending on the content and the extraction goal.

1. Scanned invoices in PDF format
- Invoices are a common business document type with a standard structure.
- Azure provides prebuilt analyzers for common document scenarios such as invoices, receipts, IDs, and similar forms.
- A prebuilt analyzer is the best choice because it can extract fields like invoice number, date, vendor, total, and line items without needing custom training.

2. Voicemail recordings in WAV format
- WAV is an audio file format, so the content is not text-based or structured like a document.
- To extract useful information from voicemail audio, you typically need a custom analyzer that can be designed to handle the specific audio content and extract the information you care about.
- A custom analyzer is appropriate because voicemail content is scenario-specific and does not match a standard prebuilt document template.

Why the other analyzer type is not correct:
- Prebuilt analyzers are designed for well-known document types and are not intended for arbitrary audio recordings.
- Custom analyzers are used when you need to define your own extraction logic for specialized content types.

Final mapping:
- PDF scanned invoices -> Prebuilt analyzer
- WAV voicemail recordings -> Custom analyzer

Question 9

You are developing a web app that processes invoices to calculate expenses.
You need to extract structured fields, including nested values, from the invoices by using a defined
schema.
What should you use?

Accepted Answer

an analyzer in Azure Content Understanding in Foundry Tools

Answer

a transcription workflow in Azure Speech in Foundry Tools

Answer

an optical character recognition (OCR)-only document processing pipeline

Answer

an Azure AI Search service

Question 10

DRAG DROP
You have a Microsoft Foundry project that contains a model deployment.
You are developing an application that sends an image and a user question to the model.
You need to send both text and image content in the same request so the model can return an
answer.
How should you complete the Python code? To answer, drag the appropriate values to the correct
targets. Each value may be used once, more than once, or not at all. You may need to drag the split
bar between panes or scroll to view content.

NOTE: Each correct selection is worth one point.

$\"Certification$

Accepted Answer

The correct answer is A.

Explanation:

To send both an image and a text question in the same request to a multimodal model in Microsoft Foundry, the request must include multiple content parts in the message payload. In Python, this is typically done by creating a user message whose content is a list containing:

- a text part for the user’s question
- an image part for the image input

This allows the model to process both modalities together and return an answer based on the combined context.

Why A is correct:
- It uses the proper multimodal message structure.
- It includes both text and image content in one request.
- It matches how Foundry model deployments handle vision-capable chat completions.

Conceptually, the request should look like this:
- role: user
- content: a list of content items
- one item with type set to text and the user question
- one item with type set to image_url or equivalent image input format

The other choices would be incorrect if they:
- send only text
- send only the image
- place the image and text into separate requests
- use an unsupported structure for multimodal input

So the completed Python code should follow the structure shown in option A.

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