AI-900 Practice Exam 55 — 20 Questions

Question 1

Which responsible AI principle ensures that AI systems work reliably across different conditions and for all users, including those from different demographics?

Accepted Answer

Reliability and safety. The Reliability and safety principle ensures that AI systems perform consistently and correctly under a wide range of conditions, including edge cases and diverse demographic groups. This principle requires rigorous testing, validation, and monitoring to prevent failures or biased outcomes that could harm users. In the context of AI-900, this principle directly addresses the need for systems to work reliably for all users, regardless of age, gender, ethnicity, or other demographic factors.

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Privacy

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Transparency

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Accountability

Question 2

A city deploys an AI system that automatically issues parking fines based on camera images. A citizen disputes a fine, claiming the system misidentified their car. The city cannot provide an explanation of how the system reached its decision because the model is too complex to interpret. Which Microsoft responsible AI principle is most directly violated?

Accepted Answer

Transparency. The city cannot explain how the AI system reached its decision, which directly violates the transparency principle. Transparency requires that AI systems be understandable and that organizations provide meaningful explanations of their behavior, especially when decisions have legal or financial consequences. The inability to interpret the model's reasoning prevents the citizen from understanding or challenging the fine, undermining trust and accountability.

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Privacy and security

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Inclusiveness

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Reliability and safety

Question 3

A university wants to build a chatbot that can answer questions about its admission procedures. The chatbot should retrieve answers directly from a set of official PDF documents containing policies and FAQs. Which Azure AI Language feature should they use to implement this?

Accepted Answer

Custom question answering. Custom question answering (C) is the correct choice because it allows the university to ingest official PDF documents and create a knowledge base of question-answer pairs. The chatbot can then retrieve answers directly from this curated content, making it ideal for domain-specific, document-based Q&A scenarios like admission procedures.

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Sentiment analysis

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Key phrase extraction

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Language detection

Question 4

A healthcare organization deploys an AI diagnostic system that was trained primarily on data from patients in one geographic region. When used in other regions with different demographics, the system shows significantly lower accuracy for those populations. Which Microsoft responsible AI principle is most directly violated?

Accepted Answer

Fairness. The system's accuracy drop across different demographics directly violates the Fairness principle, which requires AI systems to treat all groups equitably and avoid bias. Because the training data was geographically homogeneous, the model learned patterns that do not generalize, leading to disparate performance for underrepresented populations.

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Transparency

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Privacy

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Inclusiveness

Question 5

A company wants to build a self-service FAQ bot that answers customer questions based on a collection of policy documents (PDFs and Word files). They want the bot to extract answers directly from the documents without manually creating question-answer pairs. Which Azure AI Language feature should they use?

Accepted Answer

Custom Question Answering. Custom Question Answering (C) is the correct choice because it is specifically designed to extract answers directly from source documents (PDFs, Word files) without requiring manual creation of question-answer pairs. It uses a deep learning-based extractive reader to locate answer spans within the text, making it ideal for building a self-service FAQ bot from policy documents.

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Key Phrase Extraction

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Conversational Language Understanding (CLU)

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Azure OpenAI on your data

Question 6

A customer support team wants to create a chatbot that can answer common questions about employee benefits. They have a PDF document containing a list of frequently asked questions with their answers. Which Azure AI Language feature should they use to build a solution that extracts answers directly from this content?

Accepted Answer

Custom Question Answering. Custom Question Answering (C) is the correct choice because it is specifically designed to ingest documents like PDFs and extract question-answer pairs from them, enabling a chatbot to respond directly with answers from the content. This feature uses a pre-built or custom knowledge base to match user queries to the most relevant answer, making it ideal for the described scenario.

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Sentiment Analysis

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Key Phrase Extraction

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Text Analytics for Health

Question 7

A research organization is developing an AI system to assist with medical diagnosis. They want to ensure that if the system makes an error, there is a clear process for auditing and determining responsibility. Which Microsoft responsible AI principle is most relevant?

Accepted Answer

Accountability. Accountability is the Microsoft responsible AI principle that requires organizations to define and maintain clear processes for auditing, reviewing, and taking responsibility for AI system outcomes. In this scenario, the need for a clear process to audit errors and determine responsibility directly aligns with accountability, which mandates that AI systems have governance structures, human oversight, and audit trails to assign ownership for decisions and mistakes.

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Privacy and Security

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Inclusiveness

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Transparency

Question 8

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?

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.

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A. Reliability and Safety

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C. Privacy and Security

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D. Transparency

Question 9

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.

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Training a model with very few labelled examples using transfer learning

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

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

Question 10

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.

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Hold-out validation

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Data augmentation

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Principal Component Analysis (PCA)

Question 11

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.

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Connecting Azure to on-premises networks for secure bot deployment

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Translating bot responses into multiple languages automatically

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Training the bot on conversations from specific channels

Question 12

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.

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Advanced ML model development and Azure ML pipeline coding skills

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Azure infrastructure management and deployment of AI workloads using IaC tools

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Ethical AI policy writing and regulatory compliance documentation

Question 13

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.

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Adjusting the physical voltage supplied to GPU hardware during training

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

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Compressing model weights to reduce inference latency

Question 14

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.

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max_tokens

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top_p

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frequency_penalty

Question 15

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.

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A) Fairness

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B) Reliability and safety

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D) Privacy and security

Question 16

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.

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Fairness

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Reliability and safety

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Accountability

Question 17

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.

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Fine-tuning

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Zero-shot learning

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Reinforcement learning

Question 18

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.

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Underfitting

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Data leakage

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Multicollinearity

Question 19

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.

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Generating visual layouts for new document templates

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Converting documents between different file formats (PDF to DOCX)

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Checking documents for grammatical and spelling errors

Question 20

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.

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A text summarisation model that condenses long documents

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A data analysis language for querying Azure databases

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A code generation tool optimised for Python development