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Microsoft AI-103 Exam Syllabus Topics:

Microsoft Developing AI Apps and Agents on Azure Sample Questions:

1. A copilot must answer multi-part questions that depend on earlier turns in the conversation. You want the system to break each complex question into focused subqueries, run them in parallel, and semantically rerank the results before composing an answer. Which Azure AI Search capability provides this?

A) Agentic retrieval
B) Classic keyword search with a scoring profile
C) A single vector query with a high similarity threshold
D) Increasing the top-k value on a single keyword query

2. You build a chatbot that uses the Azure OpenAI GPT-4 model to generate song lyrics.
You need to ensure that responses do NOT contain lyrics from popular songs that might have been ingested during model training.
Which Azure AI Content Safety API should you use?

A) Protected material text detection
B) Groundedness detection
C) Analyze Text
D) Custom categories

3. You are building a voice agent for a pharmacy. It must accept spoken questions, reply with synthesised speech, and accurately recognise specialised medicine names that a general speech model often mishears. Which capability should you configure?

A) Azure AI Document Intelligence on the audio stream
B) A custom speech model for speech-to-text within the agent
C) Only the default text-to-speech voice
D) Azure Translator to convert the audio

4. In Azure AI Studio, you use Completions playground with the GPT-35 Turbo model.
You have a prompt that contains the following code.
function F(n)
{
var f = [0, 1];
for (var i = 2; i < n; i++) f[i] = f[i-1] + f[i-2];
return f;
}
You need the model to create an explanation of the code. The solution must minimize costs.
What should you do?

A) Set the temperature parameter to 1.
B) Change the model to GPT-4-32k.
C) Add // what does function F do? to the prompt.
D) Add function F(explanation) to the prompt.

5. You have a custom named entity recognition (NER) project in Azure Language in Foundry Tools for support tickets. The schema for the project contains an entity type named ContactInfo.
In tagged training files, ContactInfo is used for phone numbers, email addresses, and social media handles.
Model evaluation shows low precision for ContactInfo, including false positives in which nearby text is extracted as ContactInfo.
You need to improve the precision of the project.
What should you do before retraining the model?

A) Lower the confidence threshold for ContactInfo.
B) Add more support tickets as training data and label more ContactInfo entities.
C) Trigger an auto-labeling job.
D) Replace ContactInfo by using Phone, Email, and SocialMedia entities. Relabel every matching span.

Solutions: