AI-900 Practice Test 24 — 15 Questions

Question 1

A nature conservation organization wants to create an app that automatically identifies different species of birds from photos uploaded by birdwatchers. They have thousands of labeled images of bird species. Which Azure service should they use to train a custom model?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Azure Custom Vision. Azure Custom Vision is the correct choice because it allows you to train a custom image classification model using your own labeled dataset of bird species. Unlike the pre-built Computer Vision Image Analysis service, Custom Vision specializes in fine-grained classification tasks where you need to distinguish between dozens or hundreds of visually similar categories, such as different bird species.

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Azure Computer Vision Image Analysis

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Azure Face API

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Azure Form Recognizer

Question 2

What is 'Azure Machine Learning environments' and why are they important for reproducibility?

Accepted Answer

Versioned software configurations (Python packages, dependencies) ensuring reproducible ML runs. Azure Machine Learning environments are versioned software configurations that specify the Python packages, dependencies, and runtime settings needed to execute a training script. They are critical for reproducibility because they ensure that every run uses the exact same software stack, eliminating variability from package version mismatches or missing dependencies.

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The physical Azure data centre locations where model training takes place

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Development, staging, and production deployment targets for Azure ML models

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The security boundaries that isolate different ML projects in the same Azure subscription

Question 3

What does the 'read' operation in Azure AI Vision do?

Accepted Answer

Extracts printed and handwritten text from images and documents. The 'read' operation in Azure AI Vision is specifically designed to extract printed and handwritten text from images and documents using Optical Character Recognition (OCR) technology. It returns the detected text along with bounding box coordinates and confidence scores, making it suitable for digitizing documents, processing forms, and extracting text from photos.

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Reads and describes what's happening in a video

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Reads and verifies digital signatures in documents

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Reads metadata (EXIF data) embedded in image files

Question 4

A retail company uses security cameras to monitor shelves. They want to identify whether a customer is holding a specific product (e.g., a green detergent bottle) and also determine the location of that product within the camera frame. 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 (like a green detergent bottle) in an image but also returns bounding box coordinates that indicate the product's location within the camera frame. This dual output—classification plus localization—directly matches the requirement to both recognize the object and determine its position.

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Image classification

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Optical character recognition (OCR)

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Semantic segmentation

Question 5

A healthcare organization needs to extract specific data elements (such as patient names, medication dosages, and dates) from unstructured doctors' notes. Which Azure Cognitive Service is best suited for this task?

Accepted Answer

Text Analytics. Text Analytics (now part of Azure AI Language) is the correct service because it provides pre-built entity extraction capabilities specifically designed to identify and extract named entities like people (patient names), quantities (medication dosages), and dates from unstructured text. This aligns directly with the requirement to extract specific data elements from doctors' notes without needing custom model training.

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Language Understanding (LUIS)

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Translator Text

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Speech

Question 6

What is 'product recognition' in Azure AI Vision for retail scenarios?

Accepted Answer

Identifying retail products and checking shelf placement compliance using computer vision. Product recognition in Azure AI Vision for retail scenarios is specifically designed to identify retail products and check shelf placement compliance using computer vision. It uses object detection and image analysis to recognize products in images or video streams, then compares their placement against a predefined planogram to ensure items are correctly stocked and positioned. This capability helps retailers automate inventory management and optimize shelf layouts.

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Scanning product barcodes to look up inventory information

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Generating product descriptions from images for e-commerce listings

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Detecting counterfeit or damaged products in a manufacturing quality line

Question 7

What is 'background removal' in Azure AI Vision and what is it used for?

Accepted Answer

Automatically separating the foreground subject from the image background. Background removal in Azure AI Vision uses deep learning models to automatically detect and separate the primary foreground subject (e.g., a person, object, or animal) from the rest of the image. The service outputs either a cut-out image with a transparent background or a binary mask, enabling downstream tasks like compositing, product catalog creation, or privacy-focused image processing. This is a core computer vision capability, not related to audio, metadata, or image sharpness.

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Removing background noise from audio in video recordings

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Deleting metadata embedded in image files before uploading to Azure

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Removing blurry or out-of-focus areas from photographs

Question 8

What is 'custom speech' in Azure AI Speech and when would you use it?

Accepted Answer

Fine-tuning speech recognition for domain-specific vocabulary, accents, or noisy environments. Custom speech in Azure AI Speech allows you to fine-tune the speech recognition model to better understand domain-specific vocabulary (e.g., medical or legal terms), unique accents, or noisy environments. By providing audio data and transcription text, you train the model to improve accuracy for your specific use case, which is not achievable with the base recognition model.

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Creating a custom voice persona that sounds different from the standard Azure voices

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Configuring speech recognition to only accept voice commands from authorised users

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Building a custom programming language for writing speech processing scripts

Question 9

What is precision in the context of binary classification model evaluation?

Accepted Answer

The proportion of positive predictions that are actually correct. Precision measures the accuracy of positive predictions: it is the ratio of true positives to the sum of true positives and false positives. Option B correctly defines this as 'the proportion of positive predictions that are actually correct,' which is the standard definition used in Azure Machine Learning's classification metrics.

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The proportion of actual positives that the model correctly identified

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The overall proportion of all predictions that are correct

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The number of decimal places in the model's confidence score

Question 10

A data scientist trains a regression model to predict house prices. The model achieves very low error on the training data but significantly higher error on a held-out test set. Which problem does this scenario best describe?

Accepted Answer

Overfitting. The scenario describes overfitting, where the model learns the training data too well, including noise and outliers, resulting in very low training error but poor generalization to new data. In Azure Machine Learning, this is often detected by comparing training and validation metrics; a large gap indicates overfitting. The correct answer is B.

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Underfitting

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High bias

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High variance

Question 11

A data scientist wants to group customers into segments based on purchasing behavior without using any labeled examples. Which type of machine learning is this?

Accepted Answer

Unsupervised learning. Unsupervised learning is the correct choice because the data scientist has no labeled examples and wants to discover hidden patterns or groupings in the data. Clustering algorithms, such as K-Means or DBSCAN, are used to segment customers based solely on their purchasing behavior features, without any predefined categories.

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Supervised learning

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Reinforcement learning

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Semi-supervised learning

Question 12

What is 'AI for accessibility' and what Azure AI services support it?

Accepted Answer

Using speech, vision, and language AI to remove barriers for people with disabilities. 'AI for accessibility' refers to using AI technologies—specifically speech, vision, and language services—to create inclusive solutions that remove barriers for people with disabilities. Azure AI services such as Azure Cognitive Services (e.g., Computer Vision for image descriptions, Speech-to-Text for real-time captioning, and Translator for language translation) directly enable these accessibility scenarios, aligning with Microsoft's commitment to inclusive design.

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Making AI services accessible to small businesses through affordable pricing

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Providing accessible APIs with clear documentation for developer communities

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Ensuring AI applications work on low-bandwidth connections in developing regions

Question 13

What is 'Azure AI Foundry' and what is its primary purpose?

Accepted Answer

A unified enterprise platform for building, evaluating, and deploying AI applications with the full development lifecycle. Azure AI Foundry is a unified enterprise platform that provides an integrated environment for building, evaluating, and deploying AI applications across the full development lifecycle. It combines tools for data preparation, model training, evaluation, and deployment, enabling teams to manage AI projects from ideation to production within a single interface.

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A physical Microsoft facility for AI hardware manufacturing

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A subscription tier that includes all Azure AI services at a fixed monthly price

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A training platform specifically for AI engineers at Microsoft

Question 14

A developer uses Azure OpenAI to generate product descriptions. The outputs often repeat the same phrases multiple times within a single description. Which parameter should the developer increase to reduce this repetition?

Accepted Answer

Frequency penalty. The frequency penalty parameter reduces repetition by penalizing tokens that have already appeared in the generated text. Increasing this value discourages the model from reusing the same phrases, making the output more diverse and less repetitive.

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Temperature

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Presence penalty

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Max tokens

Question 15

What is 'active learning' in Azure Machine Learning data labelling?

Accepted Answer

Strategically selecting the most informative examples for human labelling to maximise learning efficiency. Active learning in Azure Machine Learning data labelling is a technique where the model identifies the data points it is most uncertain about and prioritizes those for human review. This strategic selection maximizes the learning efficiency of the model by ensuring that each labelled example provides the highest possible information gain, reducing the total number of labels needed.

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Having users actively participate in model training by rating AI responses

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A training approach where the model actively searches the internet for additional training data

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Continuous model training that runs actively in the background as new data arrives