AI-900 Practice Test 23 — 15 Questions

Question 1

What is 'personally identifiable information' (PII) detection in Azure AI Language?

Accepted Answer

Identifying and optionally redacting sensitive personal information (names, SSNs, emails) in text. PII detection in Azure AI Language is a pre-built feature that identifies sensitive personal data such as names, social security numbers, email addresses, and phone numbers within unstructured text. It can also redact (mask) these entities to help comply with data privacy regulations like GDPR. This is a core capability of the Azure AI Language service's Text Analytics API.

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Verifying that a user's identity matches their stated credentials during login

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Tracking which users have accessed personally sensitive Azure resources

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Detecting when users are sharing their own personal information in an inappropriate context

Question 2

What is 'quality control' computer vision and how is it used in manufacturing?

Accepted Answer

Detecting manufacturing defects at production line speeds with consistent accuracy. Quality control in computer vision refers to using AI models to inspect products on a manufacturing line, detecting defects such as scratches, dents, or misalignments at high speed. Azure Custom Vision or Azure Computer Vision can be trained on labeled images of good and defective items to perform real-time inference, ensuring consistent accuracy far beyond human visual inspection. This directly addresses the need for automated, scalable defect detection in production environments.

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Monitoring the quality of AI model outputs to ensure they meet accuracy standards

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Verifying that factory video surveillance cameras meet quality standards

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Controlling the quality of training images used to build computer vision models

Question 3

A hospital has a dataset with historical patient records, each labeled as either 'readmitted within 30 days' or 'not readmitted'. The hospital wants to train a model to predict which current patients are likely to be readmitted. Which type of machine learning task is this?

Accepted Answer

Supervised classification. This is a supervised classification task because the dataset contains labeled historical patient records (readmitted or not readmitted), and the goal is to predict a discrete category (binary outcome) for new patients. In Azure Machine Learning, this would use a classification algorithm like logistic regression or decision tree to assign each patient to one of the two classes.

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Supervised regression

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Unsupervised clustering

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Reinforcement learning

Question 4

What is 'Bayesian optimisation' in hyperparameter tuning?

Accepted Answer

A smart hyperparameter search that uses past trial results to select promising configurations. Bayesian optimisation is a smart hyperparameter search method that builds a probabilistic model (typically a Gaussian process) of the objective function based on past trial results. It uses an acquisition function (e.g., Expected Improvement) to balance exploration and exploitation, selecting the most promising hyperparameter configurations to evaluate next. This makes it far more efficient than grid or random search for expensive-to-evaluate models.

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A statistical method for updating model confidence as new training data arrives

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An automatic method for adjusting learning rate during training based on gradient information

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A probabilistic approach to labelling uncertain training examples

Question 5

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 6

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 7

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 8

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 9

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 10

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 11

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 12

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 13

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 14

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.

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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 15

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