AI-900 Practice Test 39 — 15 Questions

Question 1

Which type of AI workload uses historical transaction data to identify potentially fraudulent transactions in real time?

Accepted Answer

Anomaly detection or classification to flag unusual transaction patterns. Anomaly detection and classification are AI techniques specifically designed to identify patterns that deviate from normal behavior. In fraud detection, historical transaction data is used to train a model that learns typical spending patterns, and then in real time, the model flags transactions that fall outside those learned patterns as potentially fraudulent.

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Image captioning to describe transaction screenshots

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Text generation to create transaction summaries

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Object detection to verify identity documents

Question 2

A company wants to automatically categorize support tickets into categories such as 'Billing', 'Technical Issue', and 'Account Management'. They have a set of 1,000 labeled tickets that they can use to train a model. Which Azure AI Language feature should they use?

Accepted Answer

Custom text classification. Custom text classification is the correct choice because it allows you to train a model on your own labeled dataset (1,000 tickets) to categorize text into custom-defined classes like 'Billing', 'Technical Issue', and 'Account Management'. This feature is specifically designed for scenarios where you need to classify documents into user-defined categories, unlike pre-built classification options.

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Key phrase extraction

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Sentiment analysis

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Language detection

Question 3

An advertising agency wants to generate product images from text prompts. They need the ability to specify the visual style (e.g., photorealistic, oil painting) and also ensure that the generated images are safe for work by blocking inappropriate content. Which Azure OpenAI model and feature should they use?

Accepted Answer

DALL-E with built-in content filtering. B is correct because DALL-E is the Azure OpenAI model specifically designed for generating images from text prompts, and it includes built-in content filtering to block inappropriate or unsafe content. This combination directly meets the agency's need to specify visual styles (e.g., photorealistic, oil painting) via prompt engineering while ensuring safety compliance without additional configuration.

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GPT-4 with standard content filtering

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GPT-3.5 with custom moderation

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Codex with output validation

Question 4

What is Azure AI Document Intelligence's 'custom extraction model' used for?

Accepted Answer

Training on your labeled documents to extract business-specific fields not covered by prebuilt models. Azure AI Document Intelligence's custom extraction model is correct because it allows you to train a model on your own labeled documents to extract fields that are specific to your business domain and not covered by prebuilt models. This is essential for processing specialized forms like invoices, contracts, or medical records that have unique data fields.

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Automatically generating new document templates from existing forms

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Translating documents into multiple languages simultaneously

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Redacting sensitive information from documents automatically

Question 5

A marketing team uses Azure OpenAI Service to generate multiple variations of a product description from a single prompt. They want the generated descriptions to be more creative and diverse, rather than repetitive. Which parameter should they increase to achieve this?

Accepted Answer

Temperature. Increasing the Temperature parameter makes the model more creative and diverse by raising the randomness of token selection. At higher temperatures (e.g., 0.8–1.0), the model assigns more weight to less probable tokens, producing varied and unexpected outputs. This directly addresses the need for diverse product descriptions rather than repetitive ones.

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Max tokens

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Top probability

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Frequency penalty

Question 6

A data scientist trains a binary classification model to detect fraudulent credit card transactions. The dataset contains 99.5% legitimate transactions and 0.5% fraudulent transactions. The model predicts every transaction as legitimate and achieves 99.5% accuracy on the test set. Which metric would best reveal that the model is failing to identify any fraudulent transactions?

Accepted Answer

Recall. Recall (also known as sensitivity) measures the proportion of actual positive cases correctly identified by the model. In this scenario, the model predicts all transactions as legitimate, so it correctly identifies zero fraudulent transactions, giving a recall of 0%. Accuracy alone is misleading because the dataset is highly imbalanced (99.5% legitimate, 0.5% fraudulent), and a 99.5% accuracy can be achieved by simply predicting the majority class. Recall directly reveals the model's failure to detect any fraud.

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Precision

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F1 score

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Mean Absolute Error (MAE)

Question 7

A data scientist is building a binary classification model to predict fraudulent credit card transactions. The dataset is highly imbalanced: only 1% of transactions are fraudulent. The cost of a false negative is very high because missing a fraudulent transaction can lead to significant financial loss. Which evaluation metric should the data scientist prioritize to minimize false negatives?

Accepted Answer

Recall. Recall (also known as sensitivity or true positive rate) measures the proportion of actual positive cases (fraudulent transactions) that are correctly identified. In this highly imbalanced scenario where missing a fraud (false negative) is extremely costly, maximizing recall ensures that the model catches as many fraudulent transactions as possible, even if it means some false positives occur. This directly aligns with the goal of minimizing false negatives.

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Accuracy

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Precision

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F1 Score

Question 8

A hospital deploys an AI system to assist in diagnosing diseases from medical images. The system is a complex deep learning model that provides a diagnosis without any explanation. Doctors are skeptical and want to understand why the system made a particular recommendation. The hospital decides to deploy the system without providing any interpretability. Which Microsoft responsible AI principle is most directly being violated?

Accepted Answer

Transparency. The system provides a diagnosis without any explanation of how it reached its conclusion, and the hospital decides to deploy it without interpretability. This directly violates the transparency principle, which requires AI systems to be understandable and for their decisions to be explainable to users, especially in high-stakes domains like healthcare.

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Fairness

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Reliability & Safety

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Inclusiveness

Question 9

A company builds a machine learning model to predict whether a customer will purchase a product. They use a training dataset with 50% purchasers and 50% non-purchasers. The model achieves 90% accuracy on the test set. However, when deployed, the model performs poorly because the actual customer base has only 5% purchasers. What is the most likely cause of this poor performance?

Accepted Answer

The training and deployment data have different distributions.. The model was trained on a balanced dataset (50% purchasers, 50% non-purchasers) but deployed on a real-world dataset with only 5% purchasers. This mismatch in class distribution between training and deployment data causes the model to fail, as it learned decision boundaries optimized for balanced classes. This is a classic case of distribution shift, specifically prior probability shift, which invalidates the model's assumptions about the target variable's base rate.

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The model is overfitted to the training data.

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The model is underfitted and fails to capture key patterns.

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Data leakage caused inflated accuracy during testing.

Question 10

A marketing agency wants to use Azure OpenAI Service to generate product descriptions. They need the descriptions to be factually accurate and based on their specific product catalog, which is stored in a vector database. Which technique should they use to ground the model's outputs in their own data?

Accepted Answer

Prompt engineering with retrieval augmented generation (RAG). Retrieval augmented generation (RAG) is the correct technique because it allows the model to retrieve relevant, up-to-date product information from the vector database at inference time and use that data as context to generate factually accurate descriptions. This grounds the model's outputs in the specific product catalog without modifying the underlying model weights, ensuring responses are based on the agency's own data.

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Fine-tuning the model on the product catalog

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Zero-shot prompting without additional data

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Reinforcement learning from human feedback (RLHF)

Question 11

A company develops an autonomous vehicle AI system. The system was trained exclusively on data from sunny, dry weather conditions. When the vehicles are deployed in a region that experiences frequent snow and fog, the system fails to correctly identify obstacles, leading to safety risks. Which Microsoft responsible AI principle is most directly violated by this deployment?

Accepted Answer

Reliability and safety. The system fails in snow and fog because it was trained only on sunny, dry data, making it unreliable in those conditions. The Microsoft responsible AI principle of Reliability and safety requires AI systems to perform consistently and safely across their intended deployment environments. Deploying without testing for diverse weather violates this principle by exposing users to safety risks.

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Fairness

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Transparency

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Privacy and security

Question 12

A company wants to build a customer service chatbot that can understand user intents (e.g., 'cancel order', 'track shipment') and extract relevant entities (e.g., order number, product name). Which Azure AI Language feature should they use?

Accepted Answer

Conversational Language Understanding (CLU). Conversational Language Understanding (CLU) is the correct Azure AI Language feature because it is specifically designed to understand user intents (e.g., 'cancel order') and extract relevant entities (e.g., order number) from natural language input. This makes it ideal for building a customer service chatbot that needs to interpret and act on user requests.

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Key phrase extraction

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Sentiment analysis

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Named entity recognition (NER)

Question 13

A company uses Azure OpenAI Service to automatically generate customer support email responses. They want to ensure that the model does not produce responses containing offensive language, hate speech, or biased content. Which Microsoft responsible AI principle is most directly addressed by implementing content filters that screen the model's output before it is sent?

Accepted Answer

D. Fairness. Implementing content filters to screen model outputs for offensive language, hate speech, or biased content directly addresses the Fairness principle, which requires AI systems to treat all people equitably and avoid reinforcing societal biases. By filtering out harmful or biased content, the organization ensures that the generated responses do not discriminate against or marginalize any group, aligning with Microsoft's commitment to fairness in AI.

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A. Transparency

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B. Reliability and Safety

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C. Inclusiveness

Question 14

A data scientist uses Azure Machine Learning to train a model that predicts the electricity consumption (in kilowatt-hours) of a building based on features like building age, square footage, and number of occupants. The data scientist wants to evaluate how accurately the model's predictions match the actual consumption values. Which evaluation metric is most appropriate for this regression task?

Accepted Answer

Mean Absolute Error (MAE). Mean Absolute Error (MAE) is the most appropriate metric for this regression task because it directly measures the average absolute difference between predicted and actual electricity consumption values. Unlike classification metrics, MAE provides an interpretable error in the same unit (kilowatt-hours) as the target variable, making it ideal for evaluating continuous numerical predictions.

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Precision

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F1 score

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Area Under the ROC Curve (AUC)

Question 15

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.. 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.

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Collect more training data from underrepresented groups.