Microsoft Developing AI Apps and Agents on Azure Sample Questions:

1. You are building a chatbot.
You need to ensure that the chatbot can classify user input into separate categories. The categories must be dynamic and defined at the time of inference.
Which service should you use to classify the input?

A) Azure AI Language custom text classification
B) Azure OpenAI text summarization
C) Azure AI Language custom named entity recognition (NER)
D) Azure OpenAI text classification

2. Drag and Drop Question
You have a Microsoft Foundry project that processes procurement documents submitted by suppliers.
You need to implement two pipelines by using Azure Content Understanding in Foundry Tools.
The solution must meet the following requirements:
- Include a pipeline named Pipeline1 that supports cost-effective,
high-volume processing of standalone PDF invoices.
- Include a pipeline named Pipeline2 that supports cross-document
validation by using multi-step reasoning and reference data.
How should you configure each pipeline? To answer, drag the appropriate configurations to the correct pipelines. Each configuration may be used once, more than once, of 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.

3. 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 speech translation to convert the audio into another language and return the translated text.
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 batch transcription to convert the audio input and return text responses from the agent.

4. 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) No
B) Yes

5. You configure Azure AI Content Safety to moderate user-uploaded images in a generative application. During testing, a colleague claims an image returned a Violence severity score of 3.
Why is this result impossible?

A) Image moderation always returns a binary blocked or allowed flag, never a severity
B) Severity 3 is reserved for the Self-Harm category only
C) Content Safety does not moderate images, only text
D) The image content model returns only severity values 0, 2, 4, and 6

Solutions: