AI-900 Practice Questions 2 — 50 Questions

Question 1

A customer support team wants to automatically analyze thousands of product reviews. Their goal is to extract the most frequently mentioned topics (e.g., 'battery life', 'customer service', 'screen quality') without manually reading each review. Which Azure AI Language feature should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct Azure AI Language feature because it automatically identifies the main points or topics (e.g., 'battery life', 'customer service', 'screen quality') from unstructured text. This directly meets the requirement to extract frequently mentioned topics from thousands of product reviews without manual reading.

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Sentiment analysis

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Language detection

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Entity recognition

Question 2

A customer support team receives thousands of unstructured chat transcripts every day. They want to automatically identify the most common recurring issues (e.g., 'long wait time', 'payment error', 'login problem') without training a custom model. Which prebuilt Azure AI Language feature should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct choice because it automatically identifies the most salient topics or 'key phrases' from unstructured text without requiring any custom training. In this scenario, the recurring issues like 'long wait time' or 'payment error' are exactly the type of multi-word, topic-level phrases that key phrase extraction surfaces, making it the ideal prebuilt feature for summarizing common support topics.

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Named entity recognition (NER)

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Sentiment analysis

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Language detection

Question 3

A data scientist has a dataset containing images of handwritten digits (0-9) where each image is labeled with the correct digit. The goal is to train a model that can predict the digit from a new image. Which type of machine learning approach should be used?

Accepted Answer

Classification. This is a supervised learning problem where the model must predict a discrete class label (digit 0-9) from input images. Classification algorithms, such as logistic regression or neural networks, are designed to map inputs to categorical outputs, making B the correct choice.

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Regression

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Clustering

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Reinforcement learning

Question 4

A company uses a large language model to generate answers to employee questions about internal HR policies. However, the model sometimes produces answers that are factually incorrect or not based on the official policies. To reduce these inaccuracies, the company wants to provide the model with relevant, up-to-date policy documents as extra context before generating a response. Which technique is being applied?

Accepted Answer

Grounding with relevant data (RAG). The technique described is Retrieval-Augmented Generation (RAG), which retrieves relevant, up-to-date policy documents from an external knowledge base and provides them as context to the large language model before generating a response. This grounds the model's output in verified data, reducing factual inaccuracies without modifying the model itself. Option C is correct because RAG directly addresses the need to supply extra context from authoritative sources.

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Prompt engineering only

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Fine-tuning the model on policy documents

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Using a content filter

Question 5

A customer service team wants to build an Azure AI-powered bot that can understand the intent behind customer messages. For example, the bot should recognize that 'I want to return my shoes' maps to a 'ReturnItem' intent, and 'Where is my order?' maps to 'TrackOrder'. Which Azure service provides pre-built models specifically for intent recognition?

Accepted Answer

Language Understanding (LUIS). Language Understanding (LUIS) is the correct Azure service because it provides pre-built models and custom capabilities specifically designed for intent recognition and entity extraction from natural language utterances. The scenario requires mapping customer messages like 'I want to return my shoes' to a 'ReturnItem' intent, which is exactly the core function of LUIS—it analyzes user input to identify the user's goal (intent) and any relevant details (entities).

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Text Analytics

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Translator Text

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Speech-to-text

Question 6

What is the 'bias-variance tradeoff' in machine learning?

Accepted Answer

The tradeoff between underfitting (high bias) and overfitting (high variance) when choosing model complexity. Option B is correct because the bias-variance tradeoff describes the inverse relationship between underfitting (high bias, where the model is too simple to capture patterns) and overfitting (high variance, where the model is too complex and captures noise). In Azure Machine Learning, this tradeoff is managed by tuning hyperparameters like regularization strength or tree depth to balance model complexity and generalization.

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The tradeoff between model accuracy and inference speed

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The tradeoff between training data quantity and model quality

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The difference in fairness metrics between biased and unbiased model versions

Question 7

A customer service team wants to analyze thousands of support tickets to automatically categorize them into predefined topics like 'billing', 'technical issue', and 'account management'. They have a small set of labeled tickets for each category. Which Azure AI Language feature should they use?

Accepted Answer

C. Option C is correct because the Custom Text Classification feature of Azure AI Language allows you to train a model using a small set of labeled tickets to automatically categorize text into predefined topics like 'billing', 'technical issue', and 'account management'. This feature is specifically designed for scenarios where you have labeled data and need to classify documents into custom categories, making it ideal for analyzing support tickets.

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A

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B

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D

Question 8

A customer support team uses an AI chatbot to analyze incoming messages. They want to automatically identify the most frequently mentioned topics, such as 'shipping delay', 'refund policy', and 'product quality', without manually reading each message. Which Azure AI Language feature should they use?

Accepted Answer

Key Phrase Extraction. Key Phrase Extraction is the correct choice because it automatically identifies the main topics and concepts in text, such as 'shipping delay', 'refund policy', and 'product quality', without requiring manual reading. This feature returns a list of key phrases that represent the most salient points in the input, making it ideal for topic frequency analysis in customer support messages.

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Language Detection

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Sentiment Analysis

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Entity Recognition

Question 9

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 10

A bank deploys an AI system that uses a deep neural network to approve personal loan applications. A customer whose loan was rejected requests a detailed explanation of why the decision was made. The bank's AI team realizes that the model's internal workings are too complex to provide a simple, understandable reason. According to Microsoft's responsible AI principles, which principle is most directly violated by this situation?

Accepted Answer

Transparency. The bank's inability to provide a clear, understandable explanation for the AI's loan decision directly violates the transparency principle. Microsoft's responsible AI principles require that AI systems be understandable and that their decisions can be explained to users, especially when those decisions have significant impact. A deep neural network's complex, non-linear decision boundaries and lack of inherent interpretability make it a 'black box,' which undermines the required transparency.

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Fairness

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Reliability & Safety

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Privacy & Security

Question 11

A company wants to use Azure OpenAI to generate product descriptions. They have a few example descriptions that perfectly match their desired style and structure. They want the model to produce new descriptions in the same style without retraining the underlying model. Which approach should they use?

Accepted Answer

Few-shot prompting with the examples in the prompt. Few-shot prompting provides the model with a small number of example inputs and outputs directly in the prompt, allowing it to infer the desired style and structure without any training. This approach is ideal when you have a few high-quality examples and want to generate new content that matches them, without the cost and complexity of fine-tuning.

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Fine-tune the model on the example descriptions

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Embeddings and similarity search

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Content filtering configurations

Question 12

A customer support team receives thousands of emails daily. They want to automatically route each email to the appropriate department (Billing, Technical Support, or General Inquiry). They also want to extract the customer's account number and order ID from each email. Which combination of Azure AI Language features should they use?

Accepted Answer

Text classification and custom entity recognition. Option C is correct because the scenario requires two distinct NLP tasks: categorizing emails into predefined departments (Billing, Technical Support, General Inquiry) which is a text classification task, and extracting specific structured data (account number and order ID) which requires custom entity recognition to identify domain-specific entities not covered by prebuilt NER. Azure AI Language provides both custom text classification and custom entity recognition features to handle these requirements.

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Sentiment analysis and key phrase extraction

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Language detection and translation

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Named entity recognition (NER) and summarization

Question 13

A data science team trains a regression model to predict house prices. They evaluate the model using Mean Absolute Error (MAE). After deployment, they notice that the model occasionally produces large errors (e.g., underpredicting a luxury home by $500,000) while most predictions are within $20,000. The business is more concerned about the impact of these large errors than the average small error. Which additional metric should the team use to better capture the penalty for large errors?

Accepted Answer

Root Mean Squared Error (RMSE). Root Mean Squared Error (RMSE) is the correct additional metric because it squares the residuals before averaging, which heavily penalizes large errors like the $500,000 underprediction. Unlike MAE, which treats all errors equally, RMSE amplifies the impact of outliers, making it a better fit for a business that cares more about catastrophic failures than typical small errors. This aligns with the need to capture the penalty for large deviations in regression model evaluation.

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R-squared

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Mean Absolute Percentage Error (MAPE)

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F1 score

Question 14

A bike-sharing company wants to predict the number of rentals per hour. Their model's predictions are usually close but occasionally have large errors due to unexpected events like sudden rain. They want a metric that heavily penalizes these large errors to ensure the model is not overly confident. Which evaluation metric should they primarily use?

Accepted Answer

Mean Squared Error (MSE). Mean Squared Error (MSE) is the correct choice because it squares the residuals, which heavily penalizes large errors. Since the bike-sharing company wants to discourage occasional large prediction errors (e.g., due to sudden rain), MSE’s quadratic penalty ensures that models with even a few large outliers receive a much worse score, forcing the model to avoid overconfidence.

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Mean Absolute Error (MAE)

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Classification Accuracy

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R-squared

Question 15

A company wants to build a chatbot that can answer customer questions about their product return policy, shipping times, and warranty information. They have a structured document with these questions and answers. Which Azure AI Language feature should they use to create this chatbot without writing custom code?

Accepted Answer

Custom Question Answering. Custom Question Answering (C) is the correct choice because it is specifically designed to create a chatbot that answers questions based on a structured document (e.g., FAQ, product manual) without writing custom code. It uses a predefined knowledge base of question-answer pairs and provides a built-in orchestration for bot integration, making it ideal for this scenario.

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Conversational Language Understanding (CLU)

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Custom Text Classification

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Language Detection

Question 16

A city deploys an AI-powered kiosk to help residents access government services. The kiosk uses a voice interface only, without any text or screen reader support. Which Microsoft responsible AI principle is most directly being ignored?

Accepted Answer

C. The kiosk uses only a voice interface without text or screen reader support, which directly violates the Microsoft responsible AI principle of Inclusiveness. Inclusiveness requires that AI systems are designed to empower everyone, including people with disabilities such as hearing impairments or those who rely on visual or text-based interfaces. By excluding non-verbal interaction methods, the system fails to accommodate diverse user needs, making it inaccessible.

Answer

A

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B

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D

Question 17

A company deploys an AI-powered voice assistant that only supports English. The assistant is used in a country where the official languages are English, French, and Dutch. Many users who speak French or Dutch cannot use the assistant effectively. Which Microsoft responsible AI principle is most directly relevant to this situation?

Accepted Answer

Inclusiveness. The assistant's inability to support French and Dutch users directly violates the inclusiveness principle, which requires AI systems to be designed for all users regardless of language, ability, or background. By supporting only English in a multilingual country, the system excludes a significant portion of the target audience, failing to provide equitable access.

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Fairness

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Reliability and safety

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Transparency

Question 18

A company develops an AI system to screen job candidates based on their resumes. The system is trained on historical data. Analysis reveals that the model has an adverse impact against female candidates due to a proxy feature (e.g., 'years of continuous employment') that correlates with gender. The team removes the protected attribute 'gender' from the training data but the biased outcome persists. According to Microsoft's responsible AI principles, which additional step should the team take to address this unfairness?

Accepted Answer

Use a tool like Fairlearn to detect and mitigate the bias while maintaining model performance.. Option B is correct because Microsoft's responsible AI principle of fairness requires not just removing protected attributes but also detecting and mitigating proxy features that cause bias. Fairlearn is a Microsoft open-source toolkit specifically designed to assess and mitigate unfairness in AI systems, offering algorithms like 'Exponentiated Gradient Reduction' or 'Grid Search' to reduce disparity while preserving model performance. Simply removing the proxy feature (A) may not always be feasible if it carries predictive value, and Fairlearn provides a systematic way to balance fairness and accuracy.

Answer

Remove the offending proxy feature 'years of continuous employment' from the training data.

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Train a separate model for each gender group to ensure equal outcomes.

Answer

Collect more training data from underrepresented groups.

Question 19

A business analyst wants to quickly summarize the main topics discussed in a large collection of customer feedback emails. The analyst needs to identify recurring concepts such as 'product quality', 'shipping delay', and 'customer service'. They want to use a prebuilt Azure AI Language feature without any custom training. Which feature should they use?

Accepted Answer

Key Phrase Extraction. Key Phrase Extraction is the correct choice because it is a prebuilt Azure AI Language feature designed to automatically identify and return the main topics, concepts, and recurring themes from unstructured text, such as 'product quality' or 'shipping delay'. Unlike custom-trained models, this feature requires no training data and works out-of-the-box, making it ideal for quickly summarizing large collections of customer feedback emails.

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Named Entity Recognition (NER)

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Language Detection

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Sentiment Analysis

Question 20

A company needs to automatically extract text from scanned invoices that contain both printed text and handwritten notes. Which Azure AI service is specifically designed to handle this type of document?

Accepted Answer

Azure AI Document Intelligence (formerly Form Recognizer). Azure AI Document Intelligence (formerly Form Recognizer) is specifically designed to extract text, key-value pairs, and tables from scanned documents, including invoices with both printed text and handwritten notes. It uses optical character recognition (OCR) combined with deep learning models to handle mixed content, making it the correct choice for this scenario.

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Azure Face API

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Azure Custom Vision

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Azure Video Indexer