AI-900 Timed Practice Test 3 — 10 Questions

Question 1

A developer is using Azure OpenAI Service to classify customer support tickets into categories such as 'Billing', 'Technical Issue', and 'Account Management'. The developer provides three labeled examples for each category in the prompt to improve the model's accuracy. What technique is the developer applying?

Accepted Answer

Few-shot learning. Few-shot learning is the correct technique because the developer is providing a small number of labeled examples (three per category) directly in the prompt to guide the model's output without updating the model's weights. This approach leverages the model's in-context learning ability, where the examples act as a pattern for the model to follow when classifying new tickets.

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Fine-tuning

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Prompt engineering

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Retrieval-augmented generation

Question 2

A developer wants to use Azure OpenAI to generate text that follows a specific style, such as formal business letters. They provide three examples of the desired output format in the prompt and then ask the model to generate a new letter. Which technique is the developer using?

Accepted Answer

Few-shot learning. The developer is using few-shot learning, a technique where a prompt includes several examples (in this case, three formal business letters) to guide the model's output style and format without updating the model's weights. This approach leverages the model's in-context learning ability to generalize from the provided examples, making it ideal for tasks requiring specific stylistic adherence.

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Zero-shot learning

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Fine-tuning

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Temperature scaling

Question 3

What is the Azure AI Vision background removal feature used for?

Accepted Answer

Automatically separating foreground subjects from the background in images. Azure AI Vision background removal is designed to automatically separate foreground subjects from the background in images, producing a mask or a cut-out of the primary object. This feature uses deep learning models to identify and isolate the main subject, enabling further processing like compositing or analysis without the background.

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Blurring the background to create depth of field effects

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Identifying what type of background (indoor/outdoor) is in an image

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Replacing backgrounds in video calls

Question 4

A marketing team uses Azure OpenAI Service to generate ad copy. They notice the model sometimes uses offensive language. Which Azure OpenAI feature should they use to automatically block such content?

Accepted Answer

Enabling content filtering. Option C is correct because Azure OpenAI Service includes built-in content filtering that automatically detects and blocks offensive or harmful language in both prompts and completions. This feature uses AI-based classifiers to enforce responsible AI policies without requiring manual configuration of model parameters.

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Setting the temperature parameter to 0.0

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Using the frequency_penalty parameter

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Configuring the max_tokens parameter

Question 5

A retail chain uses ceiling-mounted cameras to monitor shelf inventory. They need to identify and locate individual products (e.g., a specific brand of cereal) within an image and count how many are present. Which Azure Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection is the correct capability because it not only identifies the presence of a specific product (e.g., a brand of cereal) within an image but also localizes each instance by drawing bounding boxes around them, enabling an accurate count. Image classification would only label the entire image as containing cereal without locating individual boxes, while OCR and semantic segmentation serve different purposes (text extraction and pixel-level labeling, respectively).

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Image classification

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Optical character recognition (OCR)

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Semantic segmentation

Question 6

A museum wants to create an app that allows visitors to take a photo of a painting and receive information about the artist, year, and style. The app needs to identify the painting from a database of thousands of artworks. Which Azure Computer Vision capability is most suitable?

Accepted Answer

Image classification. Image classification is the correct choice because the app needs to assign a single label (the specific painting) to the entire photo. Azure Computer Vision's image classification models are trained to recognize and categorize entire images into predefined classes, which matches the requirement of identifying a painting from a database of thousands of artworks based on the visual content of the photo.

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Optical Character Recognition (OCR)

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Object detection

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Face detection

Question 7

A museum wants to automatically generate descriptive tags for its digital art collection. They need to identify objects, themes, and artistic styles in the images without any custom training. Which Azure Computer Vision feature should they use?

Accepted Answer

Azure AI Computer Vision Image Analysis. Azure AI Computer Vision Image Analysis provides pre-built models that can automatically generate descriptive tags for images, identifying objects, themes, and artistic styles without any custom training. This feature uses a set of thousands of recognizable objects, living beings, scenery, and actions, making it ideal for the museum's requirement to tag digital art without custom model development.

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Azure AI Custom Vision

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Azure AI Face service

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Azure AI Form Recognizer

Question 8

A medical research team needs to analyze CT scans to identify and outline the exact boundaries of lung nodules. Which Azure Computer Vision capability should they use?

Accepted Answer

Semantic Segmentation. Semantic segmentation is the correct capability because it classifies each pixel in an image, enabling precise delineation of object boundaries. For CT scans, this allows the model to outline the exact shape and contour of lung nodules, which is essential for medical analysis. Image classification and object detection only provide labels or bounding boxes, not pixel-level boundaries.

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Image Classification

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Object Detection

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Optical Character Recognition (OCR)

Question 9

What is opinion mining (also called aspect-based sentiment analysis) in Azure AI Language?

Accepted Answer

Identifying sentiment toward specific aspects or topics mentioned in text. Opinion mining, also known as aspect-based sentiment analysis, in Azure AI Language goes beyond general sentiment to identify sentiment (positive, negative, neutral, or mixed) toward specific aspects or topics mentioned in the text. For example, in a product review like 'The battery life is great but the screen is too dim,' it would detect positive sentiment toward 'battery life' and negative sentiment toward 'screen.' Option B correctly captures this core functionality.

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Identifying who expressed an opinion in a text

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Translating opinions from one language to another

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Detecting politically biased content in news articles

Question 10

What is Azure Machine Learning?

Accepted Answer

A cloud platform for building, training, deploying, and monitoring ML models. Azure Machine Learning is a comprehensive cloud-based platform that provides end-to-end capabilities for the machine learning lifecycle, including building, training, deploying, and monitoring models. It supports various frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and offers features like automated ML, pipelines, and MLOps integration. This distinguishes it from pre-built AI services or specialized infrastructure offerings.

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A pre-built AI service for specific tasks like vision or language

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A database service optimized for storing ML training data

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A GPU-only service for deep learning training