Microsoft Developing AI Apps and Agents on Azure : AI-103 exam

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Microsoft AI-103 Exam Syllabus Topics:

Microsoft Developing AI Apps and Agents on Azure Sample Questions:

1. You have an application named App1 that uses Azure Speech in Foundry Tools to transcribe live calls.
Transcript segments often contain both English and Spanish. App1 sends each segment to Azure Translator in Foundry Tools to translate to another language.
Sometimes, mixed-language segments result in incomplete or incorrect translations.
You need to reduce translation errors. The solution must ensure that the entire transcript is translated successfully.
What should you do before sending the segments to Translator?

A) Split the mixed-language segments into single-language segments and translate each segment separately.
B) Specify English as the source language in the translation request for all the segments.
C) Use document translation to translate the entire transcript as a single document.
D) Enable automatic language detection for the translation request.

2. You are creating an image-editing workflow in a Microsoft Foundry project.
The workflow must meet the following requirements:
- Ensure that background objects can be removed by applying a mask-
based inpainting edit.
- Preserve the original lighting and style of the edited images.
- Use the built-in image editing controls, NOT a custom model.
You need to ensure that image edits apply exclusively inside the masked area.
How should you configure the workflow?

A) Enable mask_inpainting and supply both the input image and a mask indicating which part of the image to modify.
B) Enable image_to_image mode and a high-strength value to regenerate the full image based on the prompt.
C) Set generation mode to image_variation and provide the original image as a reference.
D) Enable text_to_image mode and a prompt describing the desired background removal.

3. You plan to configure an evaluation in Microsoft Foundry for a Retrieval Augmented Generation (RAG) chat app.
You need to provide scores for groundedness, relevance, and harmful content categories.
Which two evaluation categories can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A) AI quality (AI assisted) metrics
B) AI quality (NLP) metrics
C) similarity evaluators
D) fluency evaluator
E) risk and safety metrics

4. Hotspot Question
You develop a test method to verify the results retrieved from a call to the Azure Vision in Foundry Tools API. The call is used to analyze the existence of company logos in images. The call returns a collection of brands named brands.
You have the following code segment:

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

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

Solutions: