AI-900 Practice Exam 53 — 20 Questions

Question 1

What is a prompt in the context of generative AI?

Accepted Answer

The input text or instruction given to a generative AI model to guide its output. In generative AI, a prompt is the input text or instruction provided to a model (such as GPT-4 or DALL-E) to guide its output. It acts as the starting context or query that the model uses to generate a relevant response, image, or completion. This is a fundamental concept in Azure OpenAI Service and other generative AI workloads, where prompt engineering is used to refine outputs.

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A configuration file for training AI models

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A reward signal used in reinforcement learning

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A type of neural network activation function

Question 2

What is 'energy and sustainability' as an AI application area?

Accepted Answer

Using AI to optimise energy grids, building efficiency, agriculture, and climate modelling. 'energy and sustainability' as an AI application area refers to using AI to solve environmental challenges, such as optimizing energy grids, improving building efficiency, enhancing agricultural yields, and advancing climate modeling. This aligns with Microsoft's definition of AI for sustainability, where AI models analyze data to reduce waste, predict energy demand, and support renewable integration. It is not about the energy cost of AI itself, but about applying AI to broader sustainability goals.

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Measuring and reducing the energy consumed by AI model training itself

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Powering AI data centres with 100% renewable energy sources

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Creating AI models that require less energy to run than traditional algorithms

Question 3

What is the primary challenge of deploying computer vision AI in real-world environments?

Accepted Answer

Handling real-world variability in lighting, occlusion, image quality, and domain differences. Real-world computer vision systems must cope with significant environmental variability—such as changing lighting conditions, partial occlusions, varying image resolutions, and domain shifts (e.g., training on studio photos but deploying on security camera feeds). These factors directly degrade model accuracy and require robust data augmentation, domain adaptation, or retraining strategies. Azure's Computer Vision service addresses this through pre-built models trained on diverse datasets and the ability to fine-tune with Custom Vision, but the fundamental challenge remains handling this variability at scale.

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Computer vision models are too large to fit in cloud storage

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The difficulty of displaying results in different languages

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Obtaining legal permission to use cameras

Question 4

An e-commerce company deploys an AI-powered robot for warehouse inventory management. The robot uses computer vision to navigate and pick items. In certain lighting conditions, the robot misidentifies empty shelves and attempts to pick items that are not there, causing damage. According to Microsoft's Responsible AI principles, which principle is most directly concerned with ensuring the robot performs correctly and safely under expected conditions?

Accepted Answer

Reliability and Safety. The robot's failure to perform correctly under varying lighting conditions directly violates the Reliability and Safety principle, which mandates that AI systems must operate consistently and safely within their defined operational parameters. This principle requires rigorous testing across expected environmental conditions (e.g., lighting variations) to prevent physical damage and ensure predictable behavior.

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Fairness

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Privacy and Security

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Transparency

Question 5

What is 'custom text classification' in Azure AI Language?

Accepted Answer

Training a model on labelled examples to classify documents into custom business-specific categories. Custom text classification in Azure AI Language allows you to train a model using your own labeled data to classify documents into categories that are specific to your business needs, such as contract types, customer feedback themes, or support ticket priorities. This is a supervised learning capability where you provide examples of text and their corresponding categories, and the service learns to predict the category for new, unseen text. It is not a pre-built or universal classifier, but rather a tailored solution for domain-specific classification tasks.

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Automatically applying CSS classes to text displayed on a web page

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Classifying text files by their file type (PDF, Word, TXT)

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A pre-built classifier that categorises all text into 10 universal topics

Question 6

What is 'model card' documentation in responsible AI?

Accepted Answer

Standardized documentation describing a model's intended use, performance, limitations, and ethical considerations. A model card is a standardized documentation framework, originally proposed by researchers at Google, that provides transparency about a machine learning model's intended use, performance metrics, limitations, and ethical considerations. This documentation helps stakeholders understand when and how to responsibly deploy the model, aligning with Microsoft's responsible AI principles of fairness, reliability, transparency, and accountability.

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A payment card system for purchasing AI cloud services

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A Flash card application for learning machine learning concepts

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A business card template for data scientists to share contact information

Question 7

A warehouse uses ceiling-mounted cameras to monitor inventory shelves. The system needs to determine whether each shelf is 'full', 'half full', or 'empty' based on the entire image of the shelf. Which Azure Computer Vision capability should they use?

Accepted Answer

C) Image classification. Image classification (C) is the correct choice because the system needs to assign a single label (full, half full, or empty) to the entire image of a shelf. Azure Computer Vision's image classification analyzes the whole image and outputs a single category or tag, which directly matches the requirement of determining the overall state of the shelf. Object detection would identify and locate multiple objects within the image, not classify the entire scene, and semantic segmentation would assign a label to every pixel, which is overkill for this task.

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A) Optical Character Recognition (OCR)

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B) Object detection

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D) Semantic segmentation

Question 8

What is 'explainable AI' (XAI) and why is it required in regulated industries?

Accepted Answer

AI systems that can explain their decisions in understandable terms — required for regulatory compliance. Explainable AI (XAI) refers to AI systems that provide human-understandable justifications for their decisions, predictions, or recommendations. In regulated industries such as finance, healthcare, and insurance, regulations like GDPR's 'right to explanation' and the EU AI Act require that automated decisions be transparent and auditable, making XAI a compliance necessity.

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AI systems with publicly available source code that anyone can inspect

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AI models that are simple enough for non-experts to rebuild from scratch

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AI systems that automatically explain errors in user-submitted code

Question 9

What is 'Azure Machine Learning compute' and what types are available?

Accepted Answer

The managed cloud infrastructure (VMs, clusters) used to run ML training and inference workloads. Azure Machine Learning compute is a managed cloud infrastructure that provides on-demand virtual machines (VMs) and clusters for running machine learning training and inference workloads. It abstracts away the underlying hardware management, allowing you to dynamically scale compute resources up or down based on job requirements, and supports both CPU and GPU instances for different model types.

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The mathematical computations performed by the model during training

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The number of floating-point operations a model performs per second

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A billing calculator that estimates the cost of running machine learning workloads

Question 10

A law firm needs to automatically redact personal identifiable information (PII) such as names, addresses, and social security numbers from thousands of legal documents before making them public. They want to use a prebuilt Azure AI Language feature without custom training. Which feature should they use?

Accepted Answer

Personally Identifiable Information (PII) Detection. PII Detection is a prebuilt Azure AI Language feature specifically designed to identify and redact personal identifiable information such as names, addresses, and social security numbers from text. It requires no custom training, making it ideal for the law firm's use case of automatically redacting PII from legal documents before public release.

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Key Phrase Extraction

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Sentiment Analysis

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Language Detection

Question 11

A city deploys an AI-powered kiosk to help residents access government services. The kiosk uses a voice interface only, without any text or screen reader support. Which Microsoft responsible AI principle is most directly being ignored?

Accepted Answer

C. The kiosk uses only a voice interface without text or screen reader support, which directly violates the Microsoft responsible AI principle of Inclusiveness. Inclusiveness requires that AI systems are designed to empower everyone, including people with disabilities such as hearing impairments or those who rely on visual or text-based interfaces. By excluding non-verbal interaction methods, the system fails to accommodate diverse user needs, making it inaccessible.

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A

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B

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D

Question 12

A retail company has historical data about customers, including age, purchase history, and whether they have churned (yes/no). They want to train a model that predicts if a new customer will churn. Which type of machine learning should they use?

Accepted Answer

Supervised classification. The goal is to predict a categorical outcome (churn: yes/no) from historical labeled data. Supervised classification algorithms, such as logistic regression or decision trees, learn from input features (age, purchase history) and the target label (churn status) to assign new customers to one of the discrete classes. This directly matches the requirement for a binary classification model.

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Supervised regression

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Unsupervised clustering

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Reinforcement learning

Question 13

Which type of machine learning uses labeled training data where the correct output is provided for each input?

Accepted Answer

Supervised learning. Supervised learning is the correct answer because it explicitly uses labeled training data where each input example is paired with the correct output label. The algorithm learns to map inputs to outputs by minimizing the error between its predictions and the provided labels, enabling tasks like classification and regression.

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Unsupervised learning

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Reinforcement learning

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Transfer learning

Question 14

A university deploys an AI model to predict which students are at risk of dropping out. The predictions are used to offer targeted support. Students who may be negatively impacted by this prediction have the right to understand how the model arrived at its decision. Which Microsoft responsible AI principle is most directly relevant?

Accepted Answer

Transparency. Transparency is the responsible AI principle that requires AI systems to be understandable and interpretable. In this scenario, students have the right to know how the model arrived at its dropout prediction, which directly aligns with transparency's goal of providing clear explanations for AI decisions. This principle ensures that affected individuals can access meaningful information about the logic and factors used by the model.

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Fairness

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Reliability and safety

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Privacy and security

Question 15

A multinational corporation receives customer feedback emails in several languages. The company wants to translate all emails into English for centralized analysis by its support team. Which Azure service should they use?

Accepted Answer

Azure Translator. Azure Translator is the correct service because it is specifically designed for text-to-text translation across multiple languages, making it ideal for translating customer feedback emails into English. It provides a REST API that supports real-time or batch translation of text, which aligns with the requirement to process emails in various languages for centralized analysis.

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Azure AI Language

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Azure OpenAI Service

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Azure Speech Service

Question 16

What is a 'hallucination' in the context of large language models?

Accepted Answer

When a model generates plausible-sounding but factually incorrect information. In the context of large language models (LLMs), a hallucination occurs when the model generates text that is fluent, coherent, and plausible-sounding but is factually incorrect or nonsensical. This happens because LLMs are trained to predict the next token based on statistical patterns in their training data, not to verify facts against a ground truth. Option B correctly identifies this behavior.

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When a model refuses to answer a question

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When a model processes images instead of text

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When a model runs out of context window space

Question 17

A data scientist is building a model to predict the exact temperature in degrees Celsius based on humidity and atmospheric pressure. The model will output a single numeric value for each input. Which type of machine learning task is this?

Accepted Answer

Regression. This is a regression task because the goal is to predict a continuous numeric value (temperature in degrees Celsius) from input features (humidity and atmospheric pressure). Regression models output a real number, unlike classification which predicts discrete categories. In Azure Machine Learning, regression algorithms like Linear Regression or Decision Forest Regression are used for such tasks.

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Classification

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Clustering

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Object detection

Question 18

A company wants to build a self-service FAQ bot that answers customer questions based on a collection of policy documents (PDFs and Word files). They want the bot to extract answers directly from the documents without manually creating question-answer pairs. Which Azure AI Language feature should they use?

Accepted Answer

Custom Question Answering. Custom Question Answering (C) is the correct choice because it is specifically designed to extract answers directly from source documents (PDFs, Word files) without requiring manual creation of question-answer pairs. It uses a deep learning-based extractive reader to locate answer spans within the text, making it ideal for building a self-service FAQ bot from policy documents.

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Key Phrase Extraction

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Conversational Language Understanding (CLU)

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Azure OpenAI on your data

Question 19

What is semantic segmentation in computer vision?

Accepted Answer

Classifying each pixel in an image into a semantic category. Semantic segmentation is a computer vision task that assigns a class label to every single pixel in an image, effectively partitioning the image into regions that correspond to different semantic categories (e.g., road, car, pedestrian). This is distinct from object detection, which only provides bounding boxes around objects, and from image captioning or OCR, which operate at a higher or different level of abstraction.

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Detecting the boundaries of objects using rectangular boxes

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Generating natural language descriptions of images

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Extracting text from images using OCR

Question 20

What is 'Azure AI Search' (formerly Cognitive Search) and how does it support generative AI?

Accepted Answer

A search service that retrieves relevant document chunks for RAG — grounding LLM responses in source material. Azure AI Search is a cloud search service that indexes and retrieves relevant document chunks, which can be used in a Retrieval-Augmented Generation (RAG) pattern. By providing grounded, source-specific context to a large language model (LLM), it helps ensure the generated responses are based on factual, retrieved data rather than solely on the model's training data.

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A web crawling service that indexes publicly available web content for Azure customers

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A service that searches Azure resource configurations for compliance violations

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A full-text search plugin that adds search to Azure SQL databases