AI-900 Practice Test 33 — 15 Questions

Question 1

Which Azure AI service is purpose-built for detecting anomalies in time series data?

Accepted Answer

Azure AI Anomaly Detector. Azure AI Anomaly Detector is a dedicated, pre-built service specifically designed to identify anomalies in time series data without requiring custom model training. It uses machine learning algorithms to automatically detect spikes, dips, or pattern changes in sequential data, making it the correct choice for this purpose.

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Azure Machine Learning with custom anomaly detection models

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Azure AI Language sentiment analysis

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Azure AI Vision spatial analysis

Question 2

What is 'responsible AI impact assessment' for generative AI applications?

Accepted Answer

Identifying potential harms, affected groups, and mitigation measures before deploying AI applications. Responsible AI impact assessment is a structured process to identify potential harms (e.g., bias, fairness, privacy violations), affected groups (e.g., demographic segments), and mitigation measures before deploying generative AI applications. It aligns with Microsoft's Responsible AI principles and is a key governance step in Azure AI services to ensure ethical deployment.

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Measuring the compute cost impact of adding generative AI to an application

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Measuring user satisfaction scores after a generative AI feature launches

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Calculating the environmental impact of AI model training in terms of CO2 emissions

Question 3

What is 'AI for scientific discovery' and what examples exist in this domain?

Accepted Answer

AI accelerating breakthroughs in protein folding, drug discovery, climate modelling, and materials science. 'AI for scientific discovery' refers to the use of machine learning and deep learning models to accelerate complex scientific research, such as predicting protein structures (e.g., AlphaFold), optimizing drug candidates, improving climate models, and discovering new materials. These AI systems process vast datasets and simulate molecular interactions far faster than traditional methods, enabling breakthroughs that would otherwise take years.

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AI models that write scientific papers automatically without human researchers

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Using AI to ensure scientific publications meet journal formatting requirements

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AI systems for managing scientific equipment bookings and lab resources

Question 4

What is Azure OpenAI Service?

Accepted Answer

Azure's deployment of OpenAI models with enterprise security and compliance. Azure OpenAI Service is correct because it provides access to OpenAI's powerful generative AI models (like GPT-4, GPT-3.5, and DALL-E) through Azure's cloud platform, with built-in enterprise-grade security, compliance, and responsible AI guardrails. Unlike a generic API, it integrates with Azure Active Directory, virtual networks, and private endpoints, ensuring data residency and privacy for enterprise workloads.

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A service for building traditional rule-based chatbots

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A machine learning training platform for custom models

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A database service for storing AI training data

Question 5

What is 'receipt analysis' in Azure AI Document Intelligence and what data does it extract?

Accepted Answer

Extracting merchant name, items, prices, tax, and totals from retail receipt images. Receipt analysis in Azure AI Document Intelligence is a prebuilt model designed to extract key-value pairs and line items from sales receipts. Option B correctly identifies that it extracts merchant name, items, prices, tax, and totals from retail receipt images, which is the primary function of this model.

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Analysing customer satisfaction scores from post-purchase surveys

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Verifying that a receipt matches the purchase record in a financial database

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Detecting fraudulent receipts by comparing them to a known-good receipt database

Question 6

What is a feature in the context of machine learning?

Accepted Answer

An individual measurable property used as input to a machine learning model. In machine learning, a feature is an individual measurable property or characteristic of the data that is used as input to a model. Features are the variables that the model learns from to make predictions or classifications. This is a fundamental concept in ML, as the quality and relevance of features directly impact model performance.

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The output or prediction made by a machine learning model

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A type of neural network layer

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A software capability in Azure Machine Learning

Question 7

What is 'summarisation quality' evaluation and what metrics are used?

Accepted Answer

ROUGE scores measuring n-gram overlap between generated and reference summaries. Summarisation quality is evaluated using ROUGE (Recall-Oriented Understudy for Gisting Evaluation) scores, which measure the overlap of n-grams, word sequences, or word pairs between a generated summary and one or more reference summaries. This automated metric correlates well with human judgment and is standard in NLP tasks like text summarisation.

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Measuring summary quality by counting how many sentences were preserved from the original

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Asking users to rate summary quality on a 1-10 scale in production

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Measuring how much shorter the summary is compared to the original document

Question 8

What is 'Azure AI Foundry's model benchmarks' and how do they help you choose a model?

Accepted Answer

Standardised AI task performance comparisons (reasoning, code, math) across models in the catalogue. Azure AI Foundry's model benchmarks provide standardized performance comparisons across models in the catalog, evaluating key AI tasks such as reasoning, code generation, and math. These benchmarks allow you to objectively compare models based on their performance on specific tasks, helping you select the most suitable model for your workload.

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Performance tests for Azure AI Foundry's web interface loading speed

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Azure's SLA guarantees for model availability and API response time

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Pricing benchmarks comparing Azure OpenAI costs against competitor services

Question 9

A logistics company receives thousands of handwritten shipping labels each day. They want to use Azure AI to automatically read the handwritten addresses and convert them into digital text. Which Azure Cognitive Services capability should they use?

Accepted Answer

Optical character recognition (OCR). Optical character recognition (OCR) is the correct Azure Cognitive Services capability because it is specifically designed to extract printed or handwritten text from images and convert it into machine-readable digital text. In this scenario, the logistics company needs to read handwritten addresses from shipping labels, which is a classic OCR workload. Azure's Computer Vision OCR API (including the Read API) can handle both printed and handwritten text, making it the ideal choice for this task.

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Image classification

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Object detection

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Face detection

Question 10

An e-commerce company has a dataset of customer purchase histories with no predefined categories. The data analyst wants to identify natural groupings of customers based on their purchasing behavior to target marketing campaigns. Which type of machine learning should the analyst use?

Accepted Answer

Clustering. Clustering is the correct choice because it is an unsupervised learning technique used to discover inherent groupings in data without predefined labels. In this scenario, the analyst wants to identify natural customer segments based on purchase behavior, which aligns perfectly with clustering algorithms like K-Means or DBSCAN that partition data into clusters of similar patterns.

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Regression

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Classification

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Reinforcement learning

Question 11

What is 'retail intelligence' using computer vision and what business value does it provide?

Accepted Answer

Using store video to analyse traffic flow, dwell time, queue length, and planogram compliance. Retail intelligence using computer vision involves analyzing video feeds from in-store cameras to extract actionable insights such as customer traffic flow, dwell time at shelves, queue lengths, and planogram compliance. This is a classic computer vision workload on Azure, often implemented using Azure Video Indexer or Custom Vision, which processes visual data rather than transactional or textual data.

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AI that recommends products to online shoppers based on browsing history

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An AI system that processes retail POS transaction data to forecast sales

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Sentiment analysis of customer reviews from retail websites to improve products

Question 12

What is artificial intelligence (AI) in the context of computer science?

Accepted Answer

Software that enables machines to simulate human intelligence and learn from data. Artificial intelligence (AI) in computer science refers to software systems that can perform tasks typically requiring human intelligence, such as learning from data, reasoning, and decision-making. This definition encompasses machine learning, deep learning, and other subfields where models are trained on data to improve performance over time, rather than following explicitly programmed rules.

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A type of computer hardware that processes data faster than traditional CPUs

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A programming language used to write algorithms

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A type of database that stores structured information

Question 13

A data scientist trains a regression model to predict house prices. The model has a mean absolute error (MAE) of $5,000 on the test set. Which statement best interprets this metric?

Accepted Answer

On average, the model's predictions are $5,000 away from the actual prices.. Mean Absolute Error (MAE) measures the average absolute difference between predicted and actual values. An MAE of $5,000 means that, on average, each prediction deviates from the true house price by $5,000. This is a standard interpretation of MAE in regression metrics.

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The model is accurate 95% of the time.

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The model's predictions are within $5,000 of the actual prices for 50% of the houses.

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The square root of the average squared error is $5,000.

Question 14

Which AI workload type is used when a system needs to automatically organize unstructured data into meaningful groups without predefined categories?

Accepted Answer

Clustering. Clustering is the correct AI workload type because it is an unsupervised learning technique that automatically groups unstructured data into meaningful clusters based on inherent similarities, without requiring predefined categories or labeled training data. This makes it ideal for tasks like customer segmentation, document organization, or anomaly detection where the natural structure of the data is unknown.

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Classification

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Regression

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Object detection

Question 15

A data scientist has a dataset containing images of handwritten digits (0-9) where each image is labeled with the correct digit. The goal is to train a model that can predict the digit from a new image. Which type of machine learning approach should be used?

Accepted Answer

Classification. This is a supervised learning problem where the model must predict a discrete class label (digit 0-9) from input images. Classification algorithms, such as logistic regression or neural networks, are designed to map inputs to categorical outputs, making B the correct choice.

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Regression

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Clustering

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Reinforcement learning