AI-900 Practice Exam 56 — 20 Questions

Question 1

A startup develops an AI system that uses images of skin lesions to diagnose skin cancer. The model is trained exclusively on images from dermatology clinics in North America, which primarily feature lighter skin tones. When the system is deployed globally via a mobile app, it shows high accuracy for lighter skin tones but significantly lower accuracy for darker skin tones. Which Microsoft responsible AI principle is most directly violated?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

B. Inclusiveness. Inclusiveness. The model was trained exclusively on images from North American dermatology clinics, which primarily feature lighter skin tones, leading to significantly lower accuracy for darker skin tones. This directly violates the inclusiveness principle, which requires AI systems to be designed for and perform well across all user groups, regardless of skin tone or other demographic characteristics.

Answer

A. Reliability and Safety

Answer

C. Privacy and Security

Answer

D. Transparency

Question 2

What is 'few-shot prompting' and how does it improve model outputs?

Accepted Answer

Including a small number of input-output examples in the prompt to demonstrate the desired task format. Few-shot prompting improves model outputs by providing a small number of input-output examples directly in the prompt, which helps the model understand the desired task format, style, or reasoning pattern without requiring any fine-tuning or retraining. This technique leverages the model's in-context learning ability to generalize from the given examples and produce more accurate, consistent responses.

Answer

Training a model with very few labelled examples using transfer learning

Answer

Generating a short (few-shot) response rather than a detailed answer

Answer

Running the model for only a few seconds to save compute costs

Question 3

A data scientist has a small dataset with only 200 labeled samples. They want to get a reliable estimate of model performance without using a separate validation set that would reduce the training data. Which technique should the data scientist use in Azure Machine Learning to obtain this reliable estimate?

Accepted Answer

k-fold cross-validation. B is correct because k-fold cross-validation splits the small dataset into k folds, trains the model on k-1 folds, and validates on the remaining fold, repeating this process k times. This provides a reliable performance estimate by using all 200 samples for both training and validation without requiring a separate hold-out set, which is critical for small datasets in Azure Machine Learning.

Answer

Hold-out validation

Answer

Data augmentation

Answer

Principal Component Analysis (PCA)

Question 4

What is the purpose of Azure Bot Service's channel integration?

Accepted Answer

Deploying a single bot across multiple communication platforms without code changes. Azure Bot Service's channel integration allows a single bot to be deployed across multiple communication platforms (e.g., Microsoft Teams, Slack, Facebook Messenger, Web Chat) without requiring any code changes to the bot logic. The Bot Framework handles protocol translation and event mapping between the bot and each channel, enabling reuse of the same bot code across diverse endpoints.

Answer

Connecting Azure to on-premises networks for secure bot deployment

Answer

Translating bot responses into multiple languages automatically

Answer

Training the bot on conversations from specific channels

Question 5

What is 'the AI-900 exam' testing you on?

Accepted Answer

Foundational knowledge of AI/ML concepts and Azure AI services — suitable for non-technical stakeholders. The AI-900 exam is designed to validate foundational knowledge of AI and machine learning concepts, along with familiarity with Azure AI services. It targets non-technical stakeholders, such as business analysts or project managers, who need to understand AI capabilities and ethical considerations without requiring hands-on coding or infrastructure skills.

Answer

Advanced ML model development and Azure ML pipeline coding skills

Answer

Azure infrastructure management and deployment of AI workloads using IaC tools

Answer

Ethical AI policy writing and regulatory compliance documentation

Question 6

What is 'hyperparameter tuning' in Azure Machine Learning?

Accepted Answer

Searching for the optimal algorithm settings (learning rate, batch size) that maximise model performance. Hyperparameter tuning in Azure Machine Learning is the process of searching for the optimal set of algorithm settings, such as learning rate, batch size, or number of epochs, to maximize model performance. Azure ML provides automated hyperparameter tuning via HyperDrive, which uses techniques like Bayesian sampling, random sampling, or grid search to efficiently explore the hyperparameter space. This is a core step in training a model to achieve the best accuracy or other metrics, not a hardware or compression task.

Answer

Adjusting the physical voltage supplied to GPU hardware during training

Answer

Training the model to predict hyper-specific rare events in the data

Answer

Compressing model weights to reduce inference latency

Question 7

A writer uses Azure OpenAI Service to generate story ideas. The current configuration uses a temperature setting of 0, causing the model to produce identical outputs for the same prompt. The writer wants more creative and diverse outputs. Which parameter should be increased?

Accepted Answer

temperature. Temperature controls the randomness of the model's output. A temperature of 0 makes the model deterministic, always choosing the most likely next token, which leads to identical outputs for the same prompt. Increasing the temperature (e.g., to 0.7 or higher) introduces more randomness, allowing the model to sample from less likely tokens and produce more creative, diverse story ideas.

Answer

max_tokens

Answer

top_p

Answer

frequency_penalty

Question 8

A company develops an AI system to predict employee performance based on work habits. The system uses complex neural networks and its decisions are not easily interpretable. The company wants to ensure that employees can understand why a particular performance prediction was made. Which Microsoft responsible AI principle is most directly relevant?

Accepted Answer

C) Transparency. Transparency is the responsible AI principle that directly addresses the need for interpretability and explainability of AI systems. In this scenario, the company uses complex neural networks that are inherently black-box models, making their decisions difficult to understand. Transparency requires that the system provides explanations for its predictions, enabling employees to comprehend why a particular performance rating was assigned, which aligns with the goal of building trust and accountability.

Answer

A) Fairness

Answer

B) Reliability and safety

Answer

D) Privacy and security

Question 9

A corporation deploys an AI system that uses a deep neural network to recommend candidate profiles for job openings. The hiring managers cannot understand why a particular candidate was recommended or not. Which Microsoft responsible AI principle is most directly relevant?

Accepted Answer

Transparency. The scenario describes a deep neural network whose internal reasoning is opaque to users. Microsoft's Transparency principle requires AI systems to be interpretable and explainable, so that stakeholders can understand how decisions are made. This directly addresses the hiring managers' inability to see why a candidate was recommended or not.

Answer

Fairness

Answer

Reliability and safety

Answer

Accountability

Question 10

A developer uses Azure OpenAI to generate product descriptions. They provide five examples of product descriptions that follow a specific format (name, features, price, call to action). They then ask the model to write a new description for a given product, expecting the same format. Which technique is the developer using?

Accepted Answer

Few-shot learning. The developer is using few-shot learning, which involves providing a small number of examples (in this case, five product descriptions) to guide the model's output format and style without updating the model's weights. This technique leverages the model's in-context learning ability to follow the demonstrated pattern for a new input.

Answer

Fine-tuning

Answer

Zero-shot learning

Answer

Reinforcement learning

Question 11

What is 'smart cropping' in Azure AI Vision and how is it different from simple cropping?

Accepted Answer

AI-guided cropping that keeps the most important content in frame regardless of aspect ratio. Smart cropping in Azure AI Vision uses AI to analyze the image content and intelligently determine the most important region, then crops the image to any specified aspect ratio while keeping that region in frame. This differs from simple cropping, which merely removes pixels from the edges without understanding the image's semantic content. The AI model identifies salient objects, faces, or text to ensure the cropped result remains visually meaningful.

Answer

Cropping images faster using GPU-accelerated image processing

Answer

Automatically cropping out people's faces from images for privacy protection

Answer

Cropping images to remove background noise and irrelevant context

Question 12

What is custom text classification in Azure AI Language?

Accepted Answer

Training a text classification model on your own labeled data for custom categories. Custom text classification in Azure AI Language allows you to train a machine learning model on your own labeled dataset to classify text into custom categories that are specific to your business needs. This is a supervised learning approach where you provide examples of text and their corresponding labels, and the service learns to predict the correct category for new, unseen text. It is distinct from pre-built classification models because it adapts to your unique taxonomy.

Answer

Translating text into a custom language invented by the user

Answer

Automatically detecting and removing custom offensive terms from text

Answer

Formatting text with custom styles and fonts

Question 13

What is the purpose of Azure AI Language's 'personally identifiable information (PII) detection' feature?

Accepted Answer

Identifying and extracting personal information (names, addresses, ID numbers) from text for redaction. Azure AI Language's PII detection feature is designed to identify and extract personally identifiable information such as names, addresses, phone numbers, and ID numbers from unstructured text. This allows organizations to redact or mask sensitive data before further processing or storage, helping to comply with privacy regulations like GDPR and HIPAA.

Answer

Generating fake personal information for testing applications

Answer

Verifying the identity of users accessing AI services

Answer

Encrypting personal information stored in databases

Question 14

A data scientist trains a regression model to predict housing prices. The model uses polynomial features up to degree 5. It achieves an R-squared of 0.95 on the training set but only 0.60 on the test set. Which problem is the model most likely experiencing?

Accepted Answer

Overfitting. The model performs exceptionally well on the training data (R-squared 0.95) but poorly on the test data (R-squared 0.60), which is the classic symptom of overfitting. Using polynomial features up to degree 5 introduces high model complexity, causing the model to learn noise and specific patterns in the training set that do not generalize to unseen data.

Answer

Underfitting

Answer

Data leakage

Answer

Multicollinearity

Question 15

A bank deploys an AI system that uses a complex deep learning model to approve or reject loan applications. When a loan is rejected, customers demand to know the specific reasons. The bank wants to ensure the AI system operates in a way that allows them to explain its decisions. Which Microsoft responsible AI principle is most directly relevant to this requirement?

Accepted Answer

Transparency. The bank's requirement to explain why a loan was rejected directly aligns with the transparency principle, which mandates that AI systems be understandable and that their decisions can be communicated to users. In this scenario, the complex deep learning model must be interpretable, often through techniques like feature importance analysis or surrogate models, to provide specific reasons for rejection. Transparency ensures that customers can receive meaningful explanations, building trust and enabling accountability.

Answer

Reliability and safety

Answer

Privacy and security

Answer

Fairness

Question 16

What is the primary use case for Azure AI Document Intelligence's layout model?

Accepted Answer

Extracting the structural layout of documents including tables, text blocks, and positions. Azure AI Document Intelligence's layout model is designed to extract the structural layout of documents, including tables, text blocks, and their spatial positions. This enables downstream processing like OCR, form understanding, and document analysis by preserving the original reading order and layout hierarchy.

Answer

Generating visual layouts for new document templates

Answer

Converting documents between different file formats (PDF to DOCX)

Answer

Checking documents for grammatical and spelling errors

Question 17

What is 'DALL-E' in Azure OpenAI and what does it do?

Accepted Answer

An image generation model that creates images from natural language text prompts. DALL-E is an image generation model within Azure OpenAI that creates original images from natural language text prompts. It uses a transformer-based architecture trained on image-text pairs to generate visuals that match the semantic content of the input description, making it a core generative AI workload for visual content creation.

Answer

A text summarisation model that condenses long documents

Answer

A data analysis language for querying Azure databases

Answer

A code generation tool optimised for Python development

Question 18

A content creator uses Azure OpenAI to generate unique story ideas for a fantasy novel. They want the output to be highly creative and unpredictable, avoiding common clichés. Which parameter should they primarily increase to achieve this?

Accepted Answer

Temperature. Increasing the Temperature parameter makes the model's output more random and less deterministic, which is ideal for generating highly creative and unpredictable story ideas. A higher temperature (e.g., 0.9–1.0) increases the probability of sampling less likely tokens, reducing repetition and clichés.

Answer

Top p

Answer

Frequency penalty

Answer

Presence penalty

Question 19

A data scientist trains a decision tree model to predict customer churn. The model achieves 99% accuracy on the training data but only 80% on the test data. Which concept best explains this performance difference?

Accepted Answer

Overfitting. The model's high accuracy on training data (99%) but significantly lower accuracy on test data (80%) indicates that it has memorized the training data rather than learning generalizable patterns. This is the classic symptom of overfitting, where the decision tree captures noise and outliers in the training set, leading to poor performance on unseen data.

Answer

Underfitting

Answer

Bias-variance tradeoff

Answer

Cross-validation

Question 20

What is the difference between Azure AI Speech and Azure AI Language?

Accepted Answer

Azure AI Speech handles audio processing; Azure AI Language processes and understands text. Azure AI Speech is designed to process audio input, converting speech to text (speech-to-text) and text to speech (text-to-speech), as well as enabling speaker recognition and real-time translation. Azure AI Language, on the other hand, processes and understands text by providing capabilities such as sentiment analysis, key phrase extraction, language detection, and question answering. Option B correctly captures this fundamental division: Speech handles audio processing, while Language handles text understanding.

Answer

Azure AI Speech is for transcription only; Azure AI Language handles all other NLP tasks

Answer

Azure AI Speech works only in English; Azure AI Language supports multiple languages

Answer

They are the same service with different pricing tiers