AI-900 Practice Test 3 — 30 Questions

Question 1

A city transportation department wants to use a live camera feed at a bus stop to estimate how many people are waiting for the bus. Which Azure Computer Vision capability should they use?

Accepted Answer

C. Object detection. Object detection is the correct capability because it can identify and locate multiple people in a live camera feed, providing bounding boxes around each person. This allows the system to count the number of individuals waiting at the bus stop, which is the core requirement. Optical Character Recognition (OCR) extracts text, face detection identifies faces but not necessarily counts people in a crowd, and semantic segmentation classifies each pixel but is overkill for simple counting.

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A. Optical Character Recognition (OCR)

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B. Face detection

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D. Semantic segmentation

Question 2

What is 'entity linking' in Azure AI Language and how does it differ from NER?

Accepted Answer

Linking identified entities to knowledge base entries (e.g., Wikipedia) for disambiguation. Entity linking in Azure AI Language disambiguates identified entities by associating them with a unique identifier from a knowledge base, such as Wikipedia's Q-numbers. This differs from NER, which only labels entities (e.g., 'person', 'location') without resolving ambiguity—for example, 'Paris' could refer to a city or a person, and entity linking determines the correct one via the knowledge base.

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Creating hyperlinks in a document that connect to related content online

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Connecting named entities across multiple documents to track the same person over time

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Linking entity recognition results to downstream API calls for data enrichment

Question 3

What is the primary difference between supervised and unsupervised machine learning?

Accepted Answer

Supervised uses labeled training data; unsupervised finds patterns in unlabeled data. The primary difference between supervised and unsupervised machine learning is that supervised learning uses labeled training data to learn a mapping from inputs to outputs, while unsupervised learning finds hidden patterns or structures in unlabeled data without predefined labels. This distinction is fundamental to choosing the right approach for a given problem, such as classification (supervised) versus clustering (unsupervised).

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Supervised learning requires human oversight; unsupervised runs automatically

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Supervised is for images; unsupervised is for text

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Supervised is older and less accurate than unsupervised

Question 4

What is 'AI privacy and security' in Microsoft's Responsible AI principles?

Accepted Answer

Protecting personal data from AI systems and securing AI against adversarial attacks and misuse. Microsoft's Responsible AI principle of 'privacy and security' focuses on protecting individuals' personal data from being exposed or misused by AI systems, and ensuring AI models and infrastructure are resilient against adversarial attacks, data poisoning, and other security threats. Option B correctly captures this dual focus on data protection and system security, which is distinct from general Azure encryption or cybersecurity use cases.

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Encrypting all data at rest in Azure storage used by AI workloads

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Using AI to enhance cybersecurity by detecting network intrusions

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Ensuring employees don't share AI model weights externally without authorisation

Question 5

A data scientist trains a regression model to predict house prices. The model performs poorly on both the training data and the test data, showing high error in both sets. Which concept best describes this situation?

Accepted Answer

Underfitting. Underfitting occurs when a model is too simple to capture the underlying patterns in the data, resulting in high error on both the training and test sets. In this regression scenario, the model fails to learn the relationship between features and house prices, leading to poor performance across all data splits.

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Overfitting

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Data leakage

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Feature scaling

Question 6

What is 'face attribute analysis' in Azure AI Face service?

Accepted Answer

Estimating age, emotion, head pose, and appearance attributes from detected faces. Face attribute analysis in Azure AI Face service extracts a set of facial attributes from detected faces, including estimated age, emotion (e.g., happiness, sadness, anger), head pose (pitch, yaw, roll), and appearance traits like facial hair, glasses, and makeup. This is distinct from identification or verification tasks because it does not match faces against a database or compare two images; it simply returns metadata about the face itself.

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Identifying the named person in a photograph using a face database

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Verifying whether a submitted selfie matches a government-issued ID document

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Detecting whether a face has been digitally manipulated or deepfaked

Question 7

What is 'custom named entity recognition' (custom NER) in Azure AI Language?

Accepted Answer

Training a model to recognise domain-specific entities not covered by pre-built NER. Custom named entity recognition (custom NER) in Azure AI Language allows you to train a machine learning model to identify domain-specific entities that are not covered by the pre-built NER model. This is achieved by providing labeled examples of your own entity types, enabling the model to extract specialized terms such as product codes, internal project names, or medical conditions unique to your organization.

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Renaming standard NER entity types to match your organisation's terminology

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A faster, lighter version of NER that uses simpler rules instead of machine learning

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Filtering NER outputs to return only the entity types relevant to your application

Question 8

A hotel chain wants to automatically determine whether online guest reviews express a positive, negative, or neutral opinion about their stays. Which built-in Azure AI Language feature should they use?

Accepted Answer

Sentiment analysis. Sentiment analysis is the correct Azure AI Language feature because it is specifically designed to classify text as positive, negative, or neutral, which directly matches the hotel chain's requirement to determine guest opinions from online reviews. This feature uses machine learning models to evaluate the overall sentiment expressed in a document or sentence, providing a confidence score for each sentiment category.

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Named Entity Recognition (NER)

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Key phrase extraction

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Language detection

Question 9

What is 'speech recognition' as an AI workload?

Accepted Answer

Converting spoken audio into written text. Speech recognition, also known as automatic speech recognition (ASR), is an AI workload that converts spoken language into written text. It processes audio input and maps it to words using acoustic and language models, enabling transcription, voice commands, and dictation. Option B correctly identifies this core function.

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Identifying which employee is speaking during a meeting using their voice

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Recognising specific wake words to activate voice assistant devices

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Detecting background noise in audio to improve recording quality

Question 10

What is generative AI?

Accepted Answer

AI that creates new content such as text, images, or code based on learned patterns. Generative AI refers to models that learn patterns from training data and then produce new, original content—such as text, images, audio, or code—that resembles the training distribution. Unlike discriminative models that map inputs to labels, generative models (e.g., GPT, DALL-E) sample from a learned probability distribution to create novel outputs. This is the core definition tested in AI-900 for the 'features of generative AI workloads' domain.

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AI that classifies existing data into predefined categories

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AI that detects anomalies in structured data

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AI that controls physical robots

Question 11

A company deploys an AI system to screen job resumes. The system consistently rejects candidates from a certain university, but the company cannot determine which features led to the decision or how the model arrived at that outcome. Which Microsoft responsible AI principle is most directly violated?

Accepted Answer

Transparency. The scenario describes a system that makes decisions without providing any insight into how or why those decisions were made. Transparency, as a Microsoft responsible AI principle, requires that AI systems be understandable and that their decisions can be explained. Since the company cannot determine which features led to the rejection or how the model arrived at the outcome, the lack of explainability directly violates the Transparency principle.

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Fairness

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Accountability

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Privacy and security

Question 12

What is 'model cards' in responsible AI and what information do they contain?

Accepted Answer

Transparency documents describing a model's intended use, training data, performance, biases, and limitations. Model cards are transparency documents that accompany machine learning models to disclose their intended use, training data, performance metrics, known biases, and limitations. They are a key responsible AI practice, mandated by frameworks like Microsoft's Responsible AI Standard, to ensure stakeholders understand a model's capabilities and risks before deployment.

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Azure billing documents showing the monthly cost of running a model in production

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Technical specification sheets for AI hardware accelerators used in model training

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Playing cards used in gamification of AI training to motivate data labellers

Question 13

What are embeddings in the context of AI and language models?

Accepted Answer

Numerical vector representations of text that capture semantic meaning. Embeddings are dense numerical vector representations of text that capture semantic meaning, enabling language models to understand relationships between words and phrases. In the context of AI and language models, embeddings map words, sentences, or documents to high-dimensional vectors where similar meanings are closer in vector space, which is fundamental for tasks like semantic search, clustering, and transfer learning.

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The process of inserting AI capabilities into existing applications

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The training dataset used to build a language model

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Compressed versions of large language models for edge deployment

Question 14

What is the purpose of Azure AI Vision's 'thumbnail generation' feature?

Accepted Answer

Generating crop-focused preview images that highlight the most important content area. Azure AI Vision's thumbnail generation feature analyzes the image content to identify the most important region (e.g., a person's face or a prominent object) and then crops the image around that region to produce a focused preview. This is distinct from simple resizing or compression, as it uses AI-based spatial analysis to preserve the key subject while discarding irrelevant background areas.

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Reducing file sizes of images for faster web page loading

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Creating thumbnail-sized AI model icons for the Azure portal

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Generating multiple image variations in different artistic styles

Question 15

What is 'Azure OpenAI's content filter' configurability and why does it matter?

Accepted Answer

Adjustable severity thresholds per harm category for legitimate domain-specific use cases. Azure OpenAI's content filter configurability allows administrators to adjust severity thresholds for each harm category (e.g., hate, violence, self-harm) to accommodate legitimate domain-specific use cases, such as medical or legal content that may require higher tolerance. This matters because it balances safety with utility, enabling organizations to fine-tune filtering based on their unique content policies and compliance needs without blocking valid applications.

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Configuring which users can access Azure OpenAI based on their location

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Setting the maximum token count before content is filtered for length

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Configuring which Azure OpenAI models are available to different teams within an organisation

Question 16

Which Azure AI service allows you to analyze medical images for clinical decision support?

Accepted Answer

Azure AI Health Insights and specialized medical imaging AI. Azure AI Health Insights includes specialized medical imaging AI capabilities designed to analyze radiology and other medical images for clinical decision support. This service is built on domain-specific models trained on medical data, unlike general-purpose computer vision services.

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Azure AI Custom Vision with medical training data

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Azure AI Translator for medical documents

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Azure AI Speech for dictation only

Question 17

What is the purpose of key phrase extraction in Azure AI Language?

Accepted Answer

To identify the main talking points and important concepts in text. Key phrase extraction in Azure AI Language is designed to identify the main talking points and important concepts within a given text. It analyzes the text structure and returns a list of key phrases that summarize the core topics, enabling quick understanding of the document's primary themes without reading the entire content.

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To translate key phrases between languages

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To classify text into positive or negative sentiment

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To generate new text based on key topics

Question 18

What is 'personalisation' as an AI workload and how does it differ from recommendation?

Accepted Answer

Dynamically adapting the full user experience for each individual based on their real-time behaviour. Personalisation as an AI workload involves dynamically adapting the full user experience—such as content, layout, or interactions—for each individual based on their real-time behaviour and historical data. This goes beyond simple recommendation by modifying the entire interface and flow, not just suggesting items. It leverages machine learning models that continuously learn from user actions to tailor the experience.

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Allowing users to customise the visual theme and layout of an application manually

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Recommending specific items a user might purchase based on their purchase history

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Creating personalised data privacy policies for each user based on their location

Question 19

A building management company develops an AI system that uses temperature and humidity sensors to automatically adjust the HVAC system. They want to ensure that the system does not inadvertently cause uncomfortable temperature swings for occupants. Which Microsoft responsible AI principle is most directly relevant to this requirement?

Accepted Answer

Reliability and safety. The requirement to avoid uncomfortable temperature swings directly relates to the system's ability to operate reliably and safely under expected conditions. Microsoft's Reliability and safety principle ensures that AI systems perform consistently, fail gracefully, and do not cause physical harm or discomfort to users. In this HVAC scenario, the AI must be robust to sensor noise and environmental changes to maintain stable temperature control.

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Fairness

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Transparency

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Privacy and security

Question 20

A hospital collects patient feedback forms in text format. They want to automatically identify whether each feedback is positive, negative, or neutral, and also extract specific recurring phrases like 'waiting time' and 'staff attitude'. Which Azure AI Language feature should they use to determine the overall tone of the feedback?

Accepted Answer

C) Sentiment analysis. Sentiment analysis is the correct Azure AI Language feature because it is specifically designed to determine the overall tone (positive, negative, or neutral) of text. The question asks for identifying the tone of feedback, which is exactly what sentiment analysis provides by scoring each document and its sentences for sentiment polarity.

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A) Key phrase extraction

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B) Entity recognition

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D) Language detection