Microsoft Developing AI Apps and Agents on Azure Sample Questions:

1. 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.

2. You have a customer support agent that uses the Microsoft Foundry Agent Service.
Sometimes, customers return to a session days later to continue the same support case, and the agent must resume with the full historical context. The agent must provide the following:
- Multi-turn continuity within the session
- Cross-session continuity for the same case
- Access to the full interaction history, including user messages,
agent messages, tool calls, and tool outputs
You need to ensure that the agent automatically reloads the complete history on each new turn.
What should you do?

A) Create and reuse a conversation by storing the conversation's ID and supplying the ID on subsequent requests.
B) Persist only the final model response stored in the client application and prepend the response to future prompts.
C) Enable memory summarization on the agent definition to persist the context automatically.

3. You have an Azure subscription.
You plan to build an app that will use the Azure OpenAI DALL-E model.
You need to deploy the model.
What should you use?

A) the Azure SDK for JavaScript and Azure Machine Learning Studio
B) the Azure SDK for Python and PowerShell cmdlets
C) the Azure portal and Microsoft Graph API
D) Azure AI Studio and Azure Command-Line Interface (CLI)

4. You plan to build an agent that will combine and process multiple files uploaded by users.
You are evaluating whether to use the Azure AI Agent Service to develop the agent.
What is the maximum size of each file that can be uploaded to the service?

A) 512 MB
B) 10 MB
C) 1 GB
D) 256 MB

5. Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
Users report that some responses omit required regulatory clauses, even when the clauses are present in the retrieved content.
You need to improve response completeness.
Solution: You increase the value of the temperatureparameter.
Does this meet the goal?

A) Yes
B) No

Solutions: