AI-900 Timed Practice Test 5 — 10 Questions

Question 1

What does 'responsible AI' mean in the context of Microsoft's AI principles?

Accepted Answer

Following principles of fairness, reliability, privacy, inclusiveness, transparency, and accountability in AI systems. Option B is correct because Microsoft's responsible AI framework is built on six core principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. These principles guide the development and deployment of AI systems to ensure they are ethical, trustworthy, and beneficial to society. The other options either misrepresent the scope of responsible AI or focus on narrow compliance or access restrictions.

Answer

Using AI only for tasks that generate a financial return on investment

Answer

Ensuring AI models comply with GDPR data residency requirements

Answer

Limiting AI access to only trained professionals to prevent misuse

Question 2

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

Accepted Answer

Identifying and classifying real-world entities (people, organisations, locations) mentioned in text. Named entity recognition (NER) is a feature of Azure AI Language that identifies and categorizes real-world entities such as people, organizations, locations, dates, and quantities within unstructured text. It uses pre-trained machine learning models to extract these entities, enabling downstream tasks like information retrieval and content summarization. Option B correctly describes this core functionality.

Answer

Renaming database fields to follow a consistent naming convention

Answer

Recognising the named author of a document for copyright purposes

Answer

Detecting when a user provides their name in a chatbot conversation

Question 3

A meeting transcription service needs to convert multilingual audio recordings into accurate text in real time. Which Azure OpenAI Service model is specifically designed for this task?

Accepted Answer

Whisper. Whisper is the Azure OpenAI Service model specifically designed for speech-to-text transcription, including multilingual audio recordings, and it supports real-time conversion. Unlike GPT-4, which is a large language model for text generation, Whisper is optimized for audio processing tasks such as transcription and translation. This makes it the correct choice for converting multilingual audio into accurate text in real time.

Answer

GPT-4

Answer

DALL-E 2

Answer

Codex

Question 4

A multinational corporation deploys an AI-powered language translation system that performs well for English, Spanish, and French, but has significantly lower accuracy for Swahili and Navajo. The company wants to ensure the system serves all users equitably. Which Microsoft responsible AI principle is most directly relevant to this scenario?

Accepted Answer

Inclusiveness. The correct answer is A. Inclusiveness. This principle directly addresses designing AI systems that work well for all users, including those with diverse languages, abilities, and cultural backgrounds. The scenario highlights a performance disparity for Swahili and Navajo, which are underrepresented in the training data, making inclusiveness the most relevant principle to ensure equitable service.

Answer

Fairness

Answer

Reliability and safety

Answer

Transparency

Question 5

A photo sharing app wants to automatically generate descriptive captions for uploaded photos to improve accessibility for visually impaired users. Which Azure Computer Vision feature should they use?

Accepted Answer

Describe Image (Image Captioning). Option D is correct because the Describe Image (Image Captioning) feature of Azure Computer Vision generates human-readable captions that describe the content of an image. This directly meets the requirement of automatically generating descriptive captions for uploaded photos to improve accessibility for visually impaired users.

Answer

Optical Character Recognition (OCR)

Answer

Object Detection

Answer

Image Classification

Question 6

A security company needs to identify individuals in a crowd by matching their faces against a database of known persons of interest. The system must detect faces, verify the identities, and provide a confidence score. Which Azure Computer Vision capability should they use?

Accepted Answer

Facial recognition. Azure Computer Vision's facial recognition capability is specifically designed to detect human faces in images, match them against a known database of persons, and return a confidence score for each match. This directly aligns with the security company's requirement to identify individuals in a crowd by verifying their identities against a watchlist.

Answer

Optical character recognition (OCR)

Answer

Image classification

Answer

Object detection

Question 7

A self-driving car company develops an AI system that is highly accurate in testing but fails to consistently detect pedestrians during heavy rain. Which Microsoft responsible AI principle is most directly violated?

Accepted Answer

Reliability and safety. The system fails to consistently detect pedestrians during heavy rain, which is a failure of the AI to perform reliably under real-world conditions. Microsoft's 'Reliability and safety' principle requires AI systems to operate dependably and safely across all expected scenarios, including edge cases like adverse weather. This directly violates that principle because the system's accuracy drops in a common environmental condition, posing safety risks.

Answer

Fairness

Answer

Privacy and security

Answer

Transparency

Question 8

An autonomous vehicle team needs a system that not only identifies objects like cars and pedestrians but also creates a precise pixel-level mask for each individual object instance, even when objects overlap. Which Azure Computer Vision capability should they use?

Accepted Answer

Instance segmentation. Instance segmentation (Option D) is the correct choice because it combines object detection with semantic segmentation to identify each individual object instance and generate a precise pixel-level mask for it, even when objects overlap. This capability is essential for autonomous vehicles to distinguish between multiple cars or pedestrians that may partially occlude each other, enabling safe navigation.

Answer

Image classification

Answer

Object detection

Answer

Semantic segmentation

Question 9

A retail store wants to use Azure Computer Vision to count the number of people entering a store from live video feeds. Which prebuilt Azure Computer Vision capability should they use?

Accepted Answer

People Detection. People Detection is a specialized prebuilt capability within Azure Computer Vision that is designed specifically to detect and count people in images or video frames. Unlike generic object detection, it is optimized for identifying human figures regardless of pose or occlusion, making it ideal for counting store entries from live video feeds.

Answer

Image classification

Answer

Object detection

Answer

Optical Character Recognition (OCR)

Question 10

What is 'model monitoring' in Azure Machine Learning after deployment?

Accepted Answer

Tracking deployed model performance and data drift over time to detect degradation. Model monitoring in Azure Machine Learning refers to the ongoing process of tracking a deployed model's performance metrics (such as accuracy or precision) and detecting data drift (changes in input data distribution) or concept drift (changes in the relationship between inputs and outputs) over time. This is critical because models can degrade in production even if they performed well during training, due to shifts in real-world data. Azure ML provides built-in monitoring capabilities, including drift detection and alerting, to ensure models remain reliable.

Answer

Watching the training loss curve during model training to detect overfitting

Answer

A dashboard showing the compute costs of running model inference in production

Answer

Monitoring the uptime and latency of the model serving endpoint