AI-900 Practice Test 23 — 15 Questions

Question 1

What is 'Azure ML's experiment tracking' and why do data scientists use it?

Accepted Answer

Recording hyperparameters, metrics, and configurations for each training run for comparison and reproduction. Azure ML's experiment tracking is a feature that automatically records hyperparameters, metrics, and configuration details for each training run. Data scientists use it to compare multiple runs, identify the best-performing model, and reproduce results by revisiting the exact settings and data used. This is essential for iterative experimentation and ensuring reproducibility in machine learning workflows.

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Monitoring the progress of Azure ML service new feature deployments

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Tracking which Azure ML resources are used by which team members for billing allocation

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A compliance audit log of all model predictions made in production

Question 2

What are 'plugins' or 'tools' in the context of AI agents and Microsoft Copilot?

Accepted Answer

Extensions that give AI models the ability to call external APIs and take actions beyond text generation. Plugins (or tools) in AI agents and Microsoft Copilot are extensions that enable the AI to call external APIs, retrieve real-time data, or perform actions beyond text generation. This allows the AI to interact with services like databases, calendars, or custom business logic, making it an agent capable of executing tasks rather than just generating static responses.

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Browser extensions that block AI-generated content on websites

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Audio plugins for improving AI speech synthesis quality

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Software updates for Azure OpenAI service deployments

Question 3

A retail company wants to predict which customers are likely to stop using their service. They have a dataset with many customer attributes including age, income, purchase history, website activity, and support interactions. They suspect some features are redundant. Which technique should they use to reduce the number of features while preserving as much information as possible?

Accepted Answer

Principal Component Analysis (PCA). Principal Component Analysis (PCA) is an unsupervised dimensionality reduction technique that transforms the original correlated features into a smaller set of uncorrelated principal components, ordered by the variance they capture. By retaining only the top components, PCA reduces the number of features while preserving as much of the total variance (information) as possible, making it ideal for handling redundant features in customer datasets.

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Normalization

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One-hot encoding

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Regression analysis

Question 4

A financial services company uses an AI system to recommend personalized investment portfolios. A customer requests an explanation of why a particular investment was recommended. Which Microsoft responsible AI principle is primarily focused on ensuring the company can provide this explanation?

Accepted Answer

Transparency. Transparency is the correct principle because it directly addresses the need for AI systems to be understandable and interpretable. In this scenario, the customer's request for an explanation of a specific investment recommendation requires the AI to provide clear reasoning for its output, which is the core of transparency. This principle ensures that the company can explain how and why a decision was made, building trust and enabling oversight.

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Accountability

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Fairness

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Reliability

Question 5

What is 'Azure Custom Vision's training iterations' and why would you train multiple iterations?

Accepted Answer

Versioned training runs — each iteration trains on all current tagged images and can be compared and published. In Azure Custom Vision, a training iteration is a versioned model produced by training on the current set of tagged images. Each iteration captures the model's learned patterns at a specific point in time. Training multiple iterations allows you to compare performance across different hyperparameters, data splits, or image sets, then publish the best-performing iteration to a prediction endpoint for production use.

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Iterations represent attempts to upload images before one succeeds due to network issues

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The number of times the model scans the same image for different object types

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A pricing unit where each API call consumes one iteration from your monthly quota

Question 6

What is 'semi-supervised learning' and when is it useful?

Accepted Answer

Using small amounts of labelled data alongside large amounts of unlabelled data to train a model. Semi-supervised learning combines a small set of labeled data with a large set of unlabeled data to train a model. This approach is useful when labeling data is expensive or time-consuming, but large volumes of unlabeled data are readily available. The model first learns patterns from the labeled subset, then propagates those labels to the unlabeled data, iteratively improving its accuracy.

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Training a model that is partially supervised by one human and partially by another

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A model that receives feedback from users during deployment to improve over time

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Training that automatically stops halfway through and resumes the next day

Question 7

A global customer support team receives feedback messages in multiple languages. They want to build an automated pipeline that first identifies the language of each message, then translates it to English, and finally analyzes the sentiment of the translated text. Which combination of Azure AI services should they use?

Accepted Answer

Azure AI Translator and Azure AI Language (Text Analytics). The pipeline requires language detection, translation, and sentiment analysis. Azure AI Translator provides language detection and translation, while Azure AI Language (Text Analytics) provides sentiment analysis on the translated English text. This combination directly fulfills all three requirements.

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Azure AI Translator and Azure Anomaly Detector

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Azure AI Speech and Azure AI Language (Text Analytics)

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Azure AI Vision and Azure AI Language (Text Analytics)

Question 8

A company develops an AI system to screen job resumes and rank candidates for interviews. The system is trained on historical hiring data that favored candidates from certain well-known universities. The company decides to deploy the system without any adjustments to address this bias. Which Microsoft responsible AI principle is most directly being violated?

Accepted Answer

Fairness. (Fairness) because the AI system was trained on biased historical data that favored candidates from certain universities, and deploying it without adjustments directly violates the fairness principle. Fairness in responsible AI requires that systems treat all people equitably and do not discriminate based on protected attributes such as educational background. By not mitigating the bias, the system perpetuates historical inequities in the hiring process.

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Inclusiveness

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Reliability and Safety

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Privacy and Security

Question 9

A museum wants to automatically generate descriptive tags for its digital art collection. They need to identify objects, themes, and artistic styles in the images without any custom training. Which Azure Computer Vision feature should they use?

Accepted Answer

Azure AI Computer Vision Image Analysis. Azure AI Computer Vision Image Analysis provides pre-built models that can automatically generate descriptive tags for images, identifying objects, themes, and artistic styles without any custom training. This feature uses a set of thousands of recognizable objects, living beings, scenery, and actions, making it ideal for the museum's requirement to tag digital art without custom model development.

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Azure AI Custom Vision

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Azure AI Face service

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Azure AI Form Recognizer

Question 10

A hospital uses an AI system to prioritize emergency room patients based on severity. The system was trained on historical data that may contain biases against certain demographic groups. The hospital wants to ensure the system does not disproportionately disadvantage any group. According to Microsoft's responsible AI principles, which practice should the hospital implement during the design phase?

Accepted Answer

Conduct an impact assessment and involve diverse stakeholders during design. Microsoft's responsible AI principles emphasize the importance of conducting impact assessments and involving diverse stakeholders during the design phase to identify and mitigate potential biases before deployment. This proactive approach aligns with the fairness principle, ensuring that the AI system does not disproportionately disadvantage any demographic group. Simply removing features or relying on post-deployment monitoring is insufficient to address systemic biases embedded in historical data.

Answer

Remove all demographic features from the training data to achieve fairness through unawareness

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Use a complex, uninterpretable model to avoid scrutiny of predictions

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Deploy the system and rely on post-deployment monitoring to catch unfair outcomes

Question 11

A transportation company wants to automatically identify whether an image contains a car, a truck, or a motorcycle. The system should output a single label for the entire image. Which computer vision capability in Azure should they use?

Accepted Answer

Image classification. Image classification assigns a single label to an entire image based on its dominant content. Since the requirement is to output one label (car, truck, or motorcycle) per image, this maps directly to Azure's Custom Vision image classification capability, which trains a model to categorize whole images into predefined classes.

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Object detection

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Optical Character Recognition (OCR)

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Semantic segmentation

Question 12

What is facial recognition and what are the key responsible AI considerations for its use?

Accepted Answer

Facial recognition requires ethical consideration regarding accuracy disparities, privacy, and potential for misuse. Facial recognition is a computer vision technology that identifies or verifies individuals by analyzing facial features from images or video. The key responsible AI considerations include addressing accuracy disparities across demographic groups (e.g., higher false positive rates for certain ethnicities), ensuring privacy through data minimization and consent, and preventing misuse such as mass surveillance without oversight. Option B correctly captures these ethical imperatives, which are critical for trustworthy deployment.

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Facial recognition has no ethical concerns and should be deployed universally

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Facial recognition is only used for unlocking smartphones

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Facial recognition is 100% accurate across all demographics

Question 13

A bank is developing an AI system to automatically approve or reject small personal loans. To ensure the system treats applicants fairly regardless of race, gender, or age, which Microsoft responsible AI principle is most directly relevant?

Accepted Answer

Fairness. The Fairness principle is directly relevant because it requires AI systems to treat all individuals equitably, avoiding discrimination based on protected attributes like race, gender, or age. In this loan approval scenario, the system must be designed and tested to ensure its decisions do not systematically disadvantage any group, which is the core goal of fairness in AI.

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Inclusiveness

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Reliability and safety

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Transparency

Question 14

Which metric is MOST appropriate for evaluating a regression model's performance?

Accepted Answer

Root Mean Squared Error (RMSE). Root Mean Squared Error (RMSE) is the most appropriate metric for evaluating a regression model because it measures the average magnitude of prediction errors in the same units as the target variable, penalizing larger errors more heavily due to squaring. In Azure Machine Learning, regression models like Linear Regression or Decision Forest Regression are evaluated using RMSE to quantify how well the predicted continuous values match actual values.

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Accuracy

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Precision and recall

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AUC-ROC curve

Question 15

What is Azure Machine Learning?

Accepted Answer

A cloud platform for building, training, deploying, and monitoring ML models. Azure Machine Learning is a comprehensive cloud-based platform that provides end-to-end capabilities for the machine learning lifecycle, including building, training, deploying, and monitoring models. It supports various frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and offers features like automated ML, pipelines, and MLOps integration. This distinguishes it from pre-built AI services or specialized infrastructure offerings.

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A pre-built AI service for specific tasks like vision or language

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A database service optimized for storing ML training data

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A GPU-only service for deep learning training