AI-900 Practice Test 34 — 15 Questions

Question 1

What is 'natural language processing' (NLP) as a category of AI workload?

Accepted Answer

Using AI to process and understand text and speech in human languages. Natural language processing (NLP) is an AI workload that focuses on enabling computers to interpret, understand, and generate human language in both text and speech forms. It combines computational linguistics with statistical machine learning models to perform tasks like sentiment analysis, language translation, and speech recognition. This makes option A the correct definition.

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Programming computers using natural spoken language instead of code

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A network protocol for low-latency language model inference

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Automatically converting speech to a natural-sounding language

Question 2

A retail company has a dataset of customer transaction records with no predefined categories. They want to identify natural groupings of customers based on their purchasing behavior to create targeted marketing campaigns. Which type of machine learning should they use in Azure Machine Learning?

Accepted Answer

Clustering. Clustering is the correct choice because the goal is to discover natural groupings in unlabeled data based on purchasing behavior. Azure Machine Learning provides clustering algorithms like K-Means that automatically partition customers into segments without predefined labels, enabling targeted marketing campaigns.

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Classification

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Regression

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Reinforcement learning

Question 3

A hospital wants to use AI to predict which patients are at high risk of readmission within 30 days of discharge. What type of AI task is this?

Accepted Answer

Classification or regression to predict readmission risk. Predicting readmission risk is a supervised learning task where the model learns from historical patient data (features like age, diagnosis, lab results) to output a risk score. If the output is a continuous probability (e.g., 0.75 risk), it is regression; if it is a discrete category (e.g., high/low risk), it is classification. Both are valid approaches for this predictive scenario.

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Clustering to group similar patients together

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Generative AI to create patient health summaries

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Anomaly detection to find unusual test results

Question 4

An e-commerce website wants to automatically remove the background from product photos uploaded by sellers so that items appear on a consistent plain background. Which Azure Computer Vision capability should they use?

Accepted Answer

Background Removal. Background Removal is the correct capability because it is specifically designed to isolate the foreground subject from the background in an image, producing a transparent or solid-color background. This directly meets the requirement of automatically removing backgrounds from product photos to create a consistent plain background. Azure's Background Removal API uses deep learning models trained on millions of images to segment the primary object from its surroundings.

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Optical Character Recognition (OCR)

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Image Captioning

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Object Detection

Question 5

A company wants to automate the processing of expense reports by extracting printed text from images of receipts. Which Azure Computer Vision capability should they use?

Accepted Answer

OCR (Read API). The OCR (Read API) is the correct Azure Computer Vision capability for extracting printed text from images of receipts. It is specifically designed to detect and extract text from images and documents, supporting both printed and handwritten text, making it ideal for automating expense report processing.

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Object detection

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Semantic segmentation

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Image Analysis (description generation)

Question 6

What is the 'phi' family of models in Azure AI and what makes them distinctive?

Accepted Answer

Small language models from Microsoft Research that achieve strong reasoning performance at compact size. The 'phi' family of models are small language models (SLMs) developed by Microsoft Research that achieve strong reasoning and language understanding performance despite their compact size. They are designed to run efficiently on resource-constrained devices, making them distinctive for edge and offline scenarios where large models are impractical.

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Large multimodal models that process images, audio, and text simultaneously

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Models specialized exclusively for mathematical calculations

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A family of image generation models for creative AI tasks

Question 7

Which of the following is an example of 'anomaly detection' as an AI workload?

Accepted Answer

Automatically identifying fraudulent credit card transactions that deviate from a customer's normal patterns. Anomaly detection identifies data points that deviate significantly from the norm. In this case, fraudulent credit card transactions are detected because they do not match the customer's typical spending patterns, which is a classic use case for anomaly detection in AI workloads.

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Translating customer support emails from Spanish to English

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Generating product descriptions from a list of specifications

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Classifying customer reviews as positive or negative

Question 8

Drag and drop the steps to create a bot with Azure Bot Service into the correct order.

Accepted Answer

Provision Azure Bot Service resource, then develop bot logic, then test the bot locally, then connect to channels.. Creating a bot involves provisioning a resource, developing logic, testing, and connecting channels.

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Develop bot logic, then provision Azure Bot Service resource, then test the bot locally, then connect to channels.

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Provision Azure Bot Service resource, then test the bot locally, then develop bot logic, then connect to channels.

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Connect to channels, then provision Azure Bot Service resource, then develop bot logic, then test the bot locally.

Question 9

Drag and drop the steps to use Azure Speech-to-Text API to transcribe audio into the correct order.

Accepted Answer

Create a Speech resource and obtain subscription key and region, then configure audio input, then initialize a SpeechRecognizer, then call the recognition method and process the result.. Speech-to-text requires a Speech resource, audio input, and API call to return transcription.

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Configure audio input first, then create a Speech resource, then initialize a SpeechRecognizer, then call recognition.

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Create a Speech resource, then initialize a SpeechRecognizer, then configure audio input, then call recognition.

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Create a Speech resource, then call the recognition method, then initialize a SpeechRecognizer, then configure audio input.

Question 10

A logistics company needs to automatically read shipping labels on packages, which include text printed in various fonts and sizes, as well as handwritten addresses. Which Azure Computer Vision capability should they use?

Accepted Answer

Optical Character Recognition (OCR) via the Read API. The Read API is the correct choice because it is specifically designed for extracting printed and handwritten text from images, handling various fonts, sizes, and styles. This makes it ideal for reading shipping labels that contain both machine-printed text and handwritten addresses.

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Dense Captioning

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Image Analysis - Object Detection

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Image Analysis - Tagging

Question 11

A company develops an AI system that screens job applications to recommend candidates for interviews. The system consistently recommends male candidates over equally qualified female candidates. Which Microsoft responsible AI principle is most directly violated?

Accepted Answer

Fairness. The AI system's consistent bias toward male candidates over equally qualified female candidates directly violates the fairness principle, which requires AI systems to treat all people equitably and avoid discrimination based on protected attributes like gender. This is a classic case of algorithmic bias, where the model has learned and perpetuated historical or dataset-driven gender disparities in hiring decisions.

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Reliability and safety

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Privacy and security

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Inclusiveness

Question 12

What is 'recommendation system' as an AI workload and where is it commonly used?

Accepted Answer

AI that predicts user preferences to suggest relevant products, content, or connections. A recommendation system is an AI workload that analyzes historical user behavior, preferences, and item attributes to predict and suggest items a user is likely to be interested in. Option B correctly identifies this as AI that predicts user preferences to suggest relevant products, content, or connections, which is the core definition used in the AI-900 exam.

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An AI that recommends Azure pricing tiers based on an organisation's usage patterns

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A system that recommends when to retrain an AI model based on performance metrics

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AI that recommends the best cloud architecture for a software application

Question 13

Match each Azure AI service to its primary capability.

Accepted Answer

Computer Vision: Extract information from images and videos. Azure AI services are specialized: Computer Vision for images, Speech for audio, Language for text, and Decision for predictive decisions. Common confusions include mixing vision with speech or language with decision.

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Computer Vision: Convert speech to text

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Language: Detect anomalies in data

Question 14

Drag and drop the steps to perform a face detection using Azure Face API into the correct order.

Accepted Answer

Create Azure Face resource and get endpoint/key, then load or reference the image, then send detection request to Face API, then parse the detected face data.. Face detection requires setting up the resource, sending an image, and parsing the detected faces.

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Load or reference the image, then create Azure Face resource and get endpoint/key, then send detection request to Face API, then parse the detected face data.

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Create Azure Face resource and get endpoint/key, then parse the detected face data, then load or reference the image, then send detection request to Face API.

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Send detection request to Face API, then create Azure Face resource and get endpoint/key, then load or reference the image, then parse the detected face data.

Question 15

A data scientist is building a machine learning model to predict the number of daily bike rentals in a city based on weather data and day of the week. The target variable is a continuous integer. Which type of machine learning task is this?

Accepted Answer

Regression. The target variable is the number of daily bike rentals, which is a continuous integer (count). Predicting a continuous numeric value is a regression task. In Azure Machine Learning, regression algorithms such as Linear Regression, Decision Forest Regression, or Poisson Regression are used for this type of problem.

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Classification

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Clustering

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Anomaly Detection