AI-900 Practice Test 44 — 15 Questions

Question 1

What is 'constitutional AI' and how does it relate to responsible AI development?

Accepted Answer

A training approach using a set of ethical principles for the model to self-critique and revise outputs. Constitutional AI is a training approach developed by Anthropic where a language model is fine-tuned using a set of written ethical principles (a 'constitution'). The model learns to self-critique its own outputs against these principles and revise them to be more helpful, harmless, and honest. This directly supports responsible AI development by embedding ethical guardrails into the model's behavior without relying solely on human feedback at every step.

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Legal requirements in government constitutions that regulate AI development

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Ensuring AI models are built on open standards that any organisation can adopt

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A framework requiring AI models to have explicit constitutional rights and protections

Question 2

What is 'autonomous vehicles' AI and what AI technologies do they combine?

Accepted Answer

Combining computer vision, sensor fusion, localisation, behaviour prediction, and path planning AI. Autonomous vehicles represent a complex AI workload that integrates multiple AI technologies to perceive the environment, understand context, and make safe driving decisions. Option B is correct because it specifically lists the core AI technologies—computer vision for object detection, sensor fusion for combining data from cameras, LiDAR, and radar, localization for precise positioning, behavior prediction for anticipating actions of other road users, and path planning for determining the optimal route—that are essential for a vehicle to operate without human intervention.

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AI that automatically controls traffic lights to reduce congestion at intersections

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AI that automatically parallel parks a car using sensors and pre-programmed rules

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Using AI to optimise traffic routing in GPS navigation applications

Question 3

What is 'neural radiance field' (NeRF) technology and how does it relate to Azure AI Vision capabilities?

Accepted Answer

A method for learning 3D scene representations from multiple 2D photographs to enable novel view synthesis. Neural Radiance Fields (NeRF) use a neural network to learn a continuous 5D representation of a scene from a sparse set of 2D photographs, enabling the synthesis of novel views from arbitrary camera angles. This relates to Azure AI Vision capabilities because Azure's Computer Vision services can be integrated with NeRF-based models for advanced 3D reconstruction and volumetric rendering tasks, such as generating immersive 3D assets from 2D images in mixed reality or digital twin scenarios.

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A technique for compressing neural network weights using magnetic fields

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A networking technology that transmits images with zero packet loss

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A type of GPU shader program used for real-time 3D rendering in games

Question 4

What is 'confusion matrix' and what does it tell you about a classification model?

Accepted Answer

A table showing counts of correct and incorrect predictions broken down by predicted vs. actual class. A confusion matrix is a specific table layout that allows visualization of the performance of a classification model. It shows the counts of true positive, true negative, false positive, and false negative predictions, broken down by each actual class versus each predicted class. This directly tells you not just overall accuracy, but also the types of errors the model is making, which is critical for evaluating classifiers in Azure Machine Learning.

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A measure of how confused users are when interacting with an AI system's predictions

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A graphical display of how confident the model is across its entire test dataset

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A diagram comparing the accuracy of multiple models on the same test set

Question 5

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. 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.

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

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Ensuring AI models comply with GDPR data residency requirements

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Limiting AI access to only trained professionals to prevent misuse

Question 6

A company deploys an AI system to screen job applications and recommend candidates for interviews. The system consistently rates male candidates higher than equally qualified female candidates. Which Microsoft responsible AI principle is most directly violated?

Accepted Answer

Fairness. The AI system's consistent rating of male candidates higher than equally qualified female candidates demonstrates a clear bias in outcomes based on gender, which directly violates the Fairness principle. Fairness in responsible AI requires that AI systems treat all people equitably, avoiding discrimination based on sensitive attributes such as gender, race, or age. This bias likely stems from biased training data or flawed feature engineering that encodes historical hiring disparities.

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Reliability and safety

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Privacy and security

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Inclusiveness

Question 7

What is 'document intelligence' (Azure AI Document Intelligence) and what types of documents can it process?

Accepted Answer

A service that extracts structured data (fields, tables, key-value pairs) from forms and documents. Azure AI Document Intelligence (formerly Form Recognizer) is a service that uses optical character recognition (OCR) and machine learning to extract structured data—such as fields, tables, and key-value pairs—from scanned forms and documents. This enables automated processing of invoices, receipts, business cards, and other structured documents without manual data entry.

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A service that creates documents from structured data in a database

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A document management system for storing and organising files in Azure

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A grammar checking tool that reviews documents for writing quality

Question 8

What is 'Azure AI Speech's keyword recognition' and what are its use cases?

Accepted Answer

Continuously listening for a specific wake word to activate full speech processing without cloud round-trips. Azure AI Speech's keyword recognition is designed to continuously listen for a specific wake word (e.g., 'Hey Cortana') and activate full speech processing only when that keyword is detected. This allows the system to remain idle until triggered, reducing unnecessary cloud round-trips and conserving bandwidth and processing resources.

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Extracting the most frequently used words from a speech transcript

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Highlighting important keywords in a speech transcript for meeting notes

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Detecting when a customer uses specific product keywords during a support call

Question 9

What is 'Azure Machine Learning Responsible AI dashboard's error analysis'?

Accepted Answer

Identifying data subgroups where the model makes disproportionately more errors than average. Azure Machine Learning Responsible AI dashboard's error analysis is specifically designed to identify data subgroups where the model performs poorly, often revealing bias or systematic failures. It uses a decision tree-based approach to partition the dataset and highlight cohorts with disproportionately high error rates, enabling targeted mitigation. This goes beyond simple aggregate metrics to uncover hidden disparities in model performance.

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A log of all Python exceptions and errors that occurred during model training

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Counting the total number of incorrect predictions across the full test set

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Reviewing error messages from failed Azure ML pipeline runs to diagnose infrastructure issues

Question 10

What is 'computer vision' as a category of AI workload?

Accepted Answer

AI capabilities that interpret and understand images, video, and visual information. Computer vision is an AI workload category that enables systems to extract meaningful information from digital images, videos, and other visual inputs. It involves techniques like object detection, image classification, facial recognition, and optical character recognition (OCR), allowing machines to interpret and act on visual data. This is distinct from display hardware or UI design, as it focuses on understanding content rather than rendering or creating it.

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The display technology used in computer monitors and screens

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Software for designing user interfaces and graphical layouts

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A programming paradigm for writing code that processes visual data efficiently

Question 11

What is 'AI transparency' and why is it challenging for deep learning models?

Accepted Answer

Deep learning models are 'black boxes' — high performance but difficult to explain because of millions of interacting parameters. Deep learning models, particularly those with many layers and millions of parameters, operate as 'black boxes.' Their internal decision-making processes are highly complex and non-linear, making it extremely difficult to trace how specific inputs lead to particular outputs. This lack of interpretability is the core challenge of AI transparency in deep learning.

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Transparency is easy for all AI models because they use simple mathematical formulas

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Transparency only matters for AI systems used in consumer products

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Transparency is fully solved by showing the training data to stakeholders

Question 12

What is the purpose of a confusion matrix in evaluating a classification model?

Accepted Answer

To show the breakdown of correct and incorrect predictions by class. A confusion matrix is a table that compares the actual class labels against the model's predicted class labels, showing the counts of true positives, true negatives, false positives, and false negatives for each class. This breakdown allows you to compute key performance metrics such as accuracy, precision, recall, and F1-score, which are essential for evaluating a classification model's performance. Option B correctly identifies this purpose.

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To measure how long the model takes to make predictions

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To visualize the distribution of training data

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To show how confused users are when interacting with AI systems

Question 13

A retail company wants to predict the exact number of units of a product that will be sold next month. They have historical sales data and information about promotions and holidays. The target variable is the number of units sold, which is a continuous value. Which type of machine learning task should they perform?

Accepted Answer

Regression. Regression is the correct choice because the target variable—number of units sold—is a continuous numeric value. Regression algorithms, such as linear regression or decision forest regression, are designed to predict a numeric quantity from historical features like sales data, promotions, and holidays. In Azure Machine Learning, regression models output a real number, making them ideal for this forecasting scenario.

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Binary classification

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Multiclass classification

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Clustering

Question 14

A hospital deploys an AI diagnostic system that achieves 95% accuracy overall. However, for patients from a specific minority ethnic group, the accuracy drops to 60%. The hospital decides to continue using the system because the overall accuracy is acceptable. Which Microsoft responsible AI principle is most directly violated by this decision?

Accepted Answer

Fairness. The decision to continue using the system despite a 60% accuracy for a minority ethnic group directly violates the Fairness principle. Fairness requires that AI systems treat all groups equitably and avoid discrimination, even if overall metrics are high. A 35% accuracy gap between groups indicates systemic bias, which the hospital is ignoring by prioritizing aggregate performance over equitable outcomes.

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Inclusiveness

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Transparency

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Accountability

Question 15

What is 'data augmentation' and how does it help with limited training data?

Accepted Answer

Creating synthetic training variants (flips, rotations, synonyms) to expand small datasets. Data augmentation is a technique that artificially expands a training dataset by applying transformations (e.g., image flips, rotations, cropping, or text synonym replacement) to existing samples. This helps models generalize better when real-world data is scarce, reducing overfitting without requiring new labeled data collection.

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Collecting more labelled data from external sources to supplement training

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Increasing the number of compute nodes to process large training datasets faster

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Adding more evaluation metrics to get a richer view of model performance