AI-900 Practice Test 12 — 15 Questions

Question 1

What type of AI workload involves training a model to play games by rewarding successful moves?

Accepted Answer

Reinforcement learning where the agent receives rewards for successful moves. Reinforcement learning is the correct AI workload because it involves an agent learning to make decisions by interacting with an environment and receiving rewards or penalties for its actions. In game-playing scenarios, the model is trained through trial and error, where successful moves are rewarded, guiding the agent to maximize cumulative reward over time.

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Supervised learning with labeled game states

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Clustering similar game strategies together

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Regression to predict the final game score

Question 2

What is 'knowledge mining' as an AI workload?

Accepted Answer

Using AI to extract meaningful information and insights from large volumes of unstructured content. Knowledge mining is an AI workload that uses services like Azure Cognitive Search to extract structured insights from unstructured data (documents, images, audio). It applies built-in AI skills (e.g., OCR, entity recognition, key phrase extraction) to index and enrich content, enabling search and discovery of hidden patterns.

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Mining cryptocurrency using AI-optimized algorithms

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A technique for extracting rare earth minerals used in GPU manufacturing

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Automatically generating training data from existing knowledge bases

Question 3

What is 'model interpretability' and why is it important in responsible AI?

Accepted Answer

Understanding and explaining why a model produces specific predictions to enable trust and auditing. Model interpretability refers to the ability to understand and explain why a model produces specific predictions. It is a critical component of responsible AI because it enables trust, accountability, and auditing by allowing stakeholders to verify that decisions are fair, unbiased, and based on relevant features rather than spurious correlations.

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The ability to translate a model's code into multiple programming languages

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The speed at which a model processes inference requests

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The accuracy of a model as measured on a standard benchmark dataset

Question 4

Which Azure AI capability can analyze video to identify and track specific people or objects across frames?

Accepted Answer

Azure AI Video Indexer. Azure AI Video Indexer is the correct choice because it is specifically designed to analyze video content, including the ability to detect, track, and identify people or objects across frames using AI-powered computer vision and audio analysis. It provides features like face detection, object tracking, and motion detection over time, making it suitable for this scenario.

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Azure AI Custom Vision

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Azure AI Face

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Azure AI Vision OCR

Question 5

A robotic arm in a factory needs to pick parts from a bin. The system must identify each part and its exact outline to ensure precise grasping. Which Computer Vision capability should be used?

Accepted Answer

Semantic segmentation. Semantic segmentation is the correct capability because it classifies each pixel in an image, providing a precise outline of each part. This pixel-level classification is essential for a robotic arm to determine the exact shape and boundaries of parts for accurate grasping, unlike object detection which only provides bounding boxes.

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Object detection

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Image classification

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Optical Character Recognition

Question 6

What is 'regression' in machine learning and when is it used?

Accepted Answer

Predicting a continuous numerical value such as price, temperature, or demand. Regression is a supervised machine learning technique used to predict a continuous numerical value, such as price, temperature, or demand, based on input features. It models the relationship between independent variables and a dependent variable that has a real-valued output, making option B correct.

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A model that predicts which category an item belongs to from a set of options

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Going back to a previous model version when the current version performs poorly

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A technique for reducing the dimensionality of training data before model fitting

Question 7

What is Azure AI Language's text summarization capability used for?

Accepted Answer

Condensing long text into shorter summaries capturing the key information. Azure AI Language's text summarization capability is designed to condense long documents into shorter summaries that capture the key information. It uses extractive or abstractive summarization techniques to identify and present the most important sentences or generate new concise text, making it ideal for quickly digesting large volumes of content.

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Translating long documents into multiple languages

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Generating new creative text based on document themes

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Classifying documents into predefined business categories

Question 8

Which responsible AI principle focuses on protecting personal information and ensuring AI systems handle data with appropriate privacy safeguards?

Accepted Answer

Privacy and security. Privacy and security is the correct responsible AI principle because it directly addresses the protection of personal data and the implementation of safeguards such as encryption, access controls, and data minimization. In AI systems, this principle ensures that sensitive information (e.g., PII) is handled in compliance with regulations like GDPR and that models do not inadvertently leak training data through inference attacks.

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Fairness

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Inclusiveness

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Accountability

Question 9

A marketing team uses Azure OpenAI Service to generate product descriptions. They want the descriptions to follow a specific brand voice (formal, concise) and avoid generating any harmful or offensive language. Which combination of features should the team use?

Accepted Answer

A: Fine-tune the model with brand-specific data and enable content filtering.. Fine-tuning the model with brand-specific data allows the model to learn the desired brand voice (formal, concise) by adjusting its weights based on a curated dataset. Enabling content filtering ensures that any harmful or offensive language is blocked, either by Azure's built-in content moderation or by custom filters, meeting the safety requirement. This combination directly addresses both the style and safety needs.

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B: Use few-shot learning with examples and disable content filtering for creativity.

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C: Increase the temperature parameter and use the logprobs parameter.

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D: Use the top_p parameter and set max_tokens to a low value.

Question 10

What are 'embeddings' in Azure OpenAI and what are they used for?

Accepted Answer

Numerical vector representations of text that capture semantic meaning for search and similarity tasks. Embeddings in Azure OpenAI are numerical vector representations of text that capture semantic meaning, enabling tasks like semantic search, clustering, and similarity comparisons. They convert words, sentences, or documents into high-dimensional vectors so that similar meanings are represented by vectors close to each other in the vector space. This is correct because embeddings are fundamental to modern AI search and recommendation systems, not related to hardware or web embedding tags.

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Embedded systems software that runs AI models on IoT devices

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HTML embed tags for displaying AI model outputs in web applications

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Compressed versions of large language models that use fewer parameters

Question 11

What is tokenization in the context of natural language processing?

Accepted Answer

Breaking text into smaller units (tokens) for processing by language models. Tokenization is the process of breaking text into smaller units called tokens, which can be words, subwords, or characters. This is a fundamental preprocessing step in natural language processing because language models like GPT or BERT operate on discrete tokens rather than raw text. Each token is mapped to an integer ID from a vocabulary, enabling the model to process and generate language mathematically.

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Encrypting text for secure transmission to AI services

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Assigning security access tokens to API calls

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Converting text into a numerical score for sentiment analysis

Question 12

What is 'spatial analysis' in Azure AI Vision?

Accepted Answer

Analysing video to understand people's movements and interactions within physical spaces. Spatial analysis in Azure AI Vision uses video analytics to detect and track people in a physical space, analyzing their movements, positions, and interactions over time. It leverages computer vision models to understand spatial relationships and patterns, such as how people move through a store or queue at a counter.

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Analysing the geographic distribution of Azure data centres globally

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Mapping pixels in an image to three-dimensional coordinates

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Categorising images by their physical dimensions and file size

Question 13

A company deploys an AI system to screen job applications. The system is a complex neural network that learns patterns from historical hiring data. A rejected candidate asks for an explanation, but the development team cannot describe how the decision was reached. Which Microsoft responsible AI principle is most directly violated?

Accepted Answer

Transparency. The system's inability to explain how it reached a decision violates the transparency principle, which requires AI systems to be understandable and interpretable. Complex neural networks often act as black boxes, making it impossible to provide meaningful explanations to users, directly contradicting Microsoft's responsible AI guideline that decisions should be explainable.

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Fairness

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Privacy and security

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Reliability and safety

Question 14

A robotics company is training a drone to fly autonomously through an obstacle course. The drone receives positive rewards for staying on course and avoiding obstacles, and negative rewards for collisions. The system learns by trial and error to maximize its cumulative reward. Which type of machine learning is being used?

Accepted Answer

Reinforcement learning. Reinforcement learning is the correct choice because the drone learns by interacting with its environment, receiving rewards (positive for staying on course, negative for collisions), and adjusting its behavior through trial and error to maximize cumulative reward. This is the defining characteristic of reinforcement learning, where an agent learns a policy from feedback signals rather than from labeled data or hidden patterns.

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Supervised learning

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Unsupervised learning

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Semi-supervised learning

Question 15

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.

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Fairness

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Privacy and security

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Transparency