AI-900 Practice Test 17 — 15 Questions

Question 1

A data scientist is training a binary classification model to detect fraudulent transactions. The dataset contains only 1% fraudulent transactions. The model achieves 99% accuracy on the test set, but when deployed, it fails to detect most actual fraud cases. Which metric would best reveal this issue?

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

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positive cases correctly identified. In this highly imbalanced dataset (1% fraud), a model can achieve 99% accuracy by simply predicting 'non-fraud' for every transaction, which yields zero true positives. Recall reveals this failure because it focuses solely on how many fraudulent transactions were caught, ignoring the vast majority of non-fraud cases.

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Accuracy

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Precision

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F1 score

Question 2

A company uses a generative AI model to create blog posts. They want to ensure that the model's output never contains offensive or harmful language before the content is published. They implement a system that checks the generated text against a list of prohibited terms and blocks or edits the content if necessary. Which type of safety measure is this?

Accepted Answer

Post-processing content filtering. The described system operates after the model generates text, scanning the output against a prohibited terms list and blocking or editing it. This is a classic post-processing content filtering approach, distinct from modifying the model's training data, prompts, or weights. Azure AI Content Safety is an example of such a post-processing filter that can be applied to generative AI outputs.

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Pre-training data cleaning

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Prompt engineering with safety instructions

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Model fine-tuning on safe examples

Question 3

What is 'question answering' in Azure AI Language and what are its two main types?

Accepted Answer

Custom QA (trained on your documents) and prebuilt QA (document provided at query time). Azure AI Language's 'question answering' feature provides two distinct capabilities: Custom QA, where you train a model on your own documents (e.g., PDFs, FAQs) to answer questions from that knowledge base, and Prebuilt QA, which uses a document provided at query time to extract answers without prior training. This distinction is fundamental to how the service is deployed—either as a persistent, trained knowledge base or as an on-the-fly extraction from a user-supplied document.

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Multiple-choice question generation and open-ended answer scoring

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Structured QA for databases and unstructured QA for text documents

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Real-time QA for chatbots and batch QA for scheduled document processing

Question 4

A data scientist trains a binary classification model to detect spam emails. The dataset contains 95% legitimate emails (negative class) and 5% spam (positive class). The model predicts all emails as legitimate. The accuracy is 95%, but the model is useless. Which metric would best indicate the model's failure?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positive cases correctly identified. With 5% spam and the model predicting all as legitimate, recall is 0% because no spam emails are detected. This directly exposes the model's failure to identify the positive class despite high accuracy.

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Precision

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F1 score

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Specificity

Question 5

A security company wants to use Azure Computer Vision to monitor a restricted area. They need to count the number of people present in each camera frame and draw bounding boxes around each person. Which Azure Computer Vision capability should they use?

Accepted Answer

Image Analysis (object detection). (Image Analysis with object detection) is correct because Azure Computer Vision's object detection capability can identify and locate multiple instances of a specific object class—in this case, people—within an image. It returns bounding box coordinates for each detected person, enabling the security company to count individuals and draw boxes around them in each camera frame.

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Optical Character Recognition (OCR)

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Face detection

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Image classification

Question 6

What is 'Azure Machine Learning's Responsible AI dashboard' and what does it include?

Accepted Answer

A unified tool for error analysis, interpretability, fairness, data exploration, and causal inference. The Responsible AI dashboard in Azure Machine Learning is a unified, integrated tool that combines multiple components for building and evaluating AI systems responsibly. It includes error analysis, model interpretability, fairness assessment, data exploration, and causal inference capabilities, all accessible through a single interface. This dashboard helps data scientists and developers understand model behavior, identify potential biases, and make informed decisions throughout the ML lifecycle.

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A compliance checklist confirming a model meets Microsoft's responsible AI certification requirements

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A monitoring dashboard showing responsible AI policy violations in production

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A report auto-generated and submitted to regulators when a model is deployed

Question 7

What is 'data drift' and why is it a concern for deployed AI models?

Accepted Answer

The gradual change in data distribution over time that causes deployed model accuracy to degrade. Data drift refers to the gradual change in the statistical properties of the input data that a deployed AI model receives, compared to the data it was trained on. This shift in distribution causes the model's predictions to become less accurate over time because the model was optimized for the original data patterns. In Azure Machine Learning, data drift is monitored using dataset monitors that compare baseline and target datasets to detect significant changes, triggering retraining pipelines to maintain model performance.

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When training data files are accidentally moved to the wrong storage container

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The movement of data between Azure regions for latency optimisation

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Intentional modification of training data to improve model robustness

Question 8

A media company wants to automatically organize a large collection of news articles into several topic-based categories (e.g., politics, sports, technology) without using any predefined labels. They plan to use Azure Machine Learning. Which type of machine learning task should they use?

Accepted Answer

Clustering. Clustering is the correct choice because the media company wants to group unlabeled news articles into topic-based categories based on inherent similarities in the data, without using predefined labels. Azure Machine Learning provides clustering algorithms like K-Means that automatically partition the dataset into distinct clusters, making it ideal for unsupervised learning tasks where the goal is to discover natural groupings.

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Regression

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Classification

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Anomaly detection

Question 9

What is image classification and how is it different from object detection?

Accepted Answer

Image classification labels the whole image; object detection finds and locates multiple objects within it. Image classification assigns a single label to an entire image based on its dominant content, such as 'cat' or 'dog'. Object detection goes further by not only identifying multiple objects within an image but also drawing bounding boxes around each one, providing both class labels and spatial locations. This distinction is fundamental in computer vision workloads on Azure, where Custom Vision and Computer Vision API offer separate capabilities for classification and detection tasks.

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Image classification is faster; object detection is slower but more accurate

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Image classification works on videos; object detection works on static images only

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They are the same task with different names

Question 10

What is the 'Azure OpenAI Playground' and what is it used for?

Accepted Answer

A web-based interface for interactively testing Azure OpenAI models and prompts without coding. The Azure OpenAI Playground is a web-based interface that allows users to interactively test and experiment with Azure OpenAI models (like GPT-4, GPT-3.5, and DALL-E) by entering prompts and adjusting parameters (e.g., temperature, max tokens) without writing any code. It is used for rapid prototyping, prompt engineering, and evaluating model behavior before integrating into applications via the API.

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A children's educational game powered by Azure OpenAI for learning to code

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A sandboxed environment for running untrusted AI models safely

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A feature for generating synthetic training data for custom model fine-tuning

Question 11

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 12

What is 'face detection' vs 'face identification' in Azure AI Vision?

Accepted Answer

Detection locates faces and returns attributes; identification matches faces to a known person database. Face detection in Azure AI Vision locates human faces in an image and returns attributes such as bounding box coordinates, landmarks (e.g., eyes, nose), and optional attributes like age or emotion. Face identification, part of the Azure Face API, goes a step further by matching a detected face against a secured person database (PersonGroup) to verify or recognize a specific individual. This distinction is fundamental: detection finds faces, identification assigns an identity.

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Face detection and identification are the same feature with different names

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Detection works on live video; identification works only on still images

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Face detection requires a paid tier; identification is available in the free tier

Question 13

A security company needs to analyze live video feeds from multiple cameras to detect specific objects (e.g., vehicles, people) and also read license plate numbers from vehicles. Which combination of Azure Computer Vision capabilities should they use?

Accepted Answer

Object detection and Optical Character Recognition. The scenario requires two distinct capabilities: detecting specific objects (vehicles, people) in live video feeds, which is handled by Azure Computer Vision's Object Detection feature, and reading license plate numbers, which requires Optical Character Recognition (OCR). Object detection identifies and locates objects within an image or video frame, while OCR extracts text from images, making this combination ideal for the use case.

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Image analysis and face detection

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Semantic segmentation and image captioning

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Spatial analysis and image classification

Question 14

What does Azure Machine Learning's 'compute cluster' provide?

Accepted Answer

Scalable, auto-scaling cloud compute for running ML training jobs that scales to zero when idle. Azure Machine Learning's compute cluster provides a scalable, auto-scaling cloud compute environment specifically designed for running ML training jobs. It automatically scales up to handle large workloads and scales down to zero nodes when idle, optimizing cost and resource utilization.

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A Kubernetes cluster for deploying trained models as REST APIs

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A data storage cluster for distributing training datasets across nodes

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A network of IoT sensors for collecting training data

Question 15

What is 'differential privacy' and how is it relevant to AI model training?

Accepted Answer

A mathematical guarantee that model training reveals negligible information about any individual's data. Differential privacy is a mathematical framework that ensures the output of a model training process does not reveal whether any specific individual's data was included in the training dataset. It achieves this by adding calibrated noise to the training process or query results, providing a formal privacy guarantee quantified by the epsilon parameter. This is directly relevant to AI model training because it allows organizations to train models on sensitive data while protecting individual privacy, which is a core requirement for compliance with regulations like GDPR and HIPAA.

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The difference in model accuracy between a private deployment and a public API

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Encrypting model weights so they remain private from users accessing the model API

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Using different models for different privacy tiers of customers