AI-900 Practice Test 11 — 15 Questions

Question 1

A medical research team trains a model to detect a rare disease from lab results. The disease occurs in only 1% of patients. The model predicts 'no disease' for every patient and achieves 99% accuracy. Which metric best reveals that the model is failing to identify actual disease cases?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positive cases correctly identified by the model. With a 99% accuracy but zero true positives (since the model always predicts 'no disease'), recall is 0%, which directly reveals the model's failure to detect any actual disease cases. In Azure Machine Learning, recall is a key metric for imbalanced classification tasks, especially when missing a positive case has severe consequences.

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Accuracy

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Precision

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F1 score

Question 2

A retail company collects thousands of customer reviews. They want to automatically extract frequently mentioned aspects (e.g., 'battery life', 'customer service', 'price') to understand common topics. Which Azure AI Language capability should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct Azure AI Language capability because it is specifically designed to identify and extract the main talking points or topics from unstructured text, such as 'battery life', 'customer service', and 'price' from customer reviews. This directly matches the requirement to automatically extract frequently mentioned aspects without needing predefined categories.

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Sentiment analysis

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Named entity recognition

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Language detection

Question 3

A developer is using Azure OpenAI to generate code snippets for a banking application. The developer wants to minimize the risk that the generated code contains security vulnerabilities or malicious instructions, even if the prompt is ambiguous. Which Azure OpenAI feature should the developer configure to address this concern?

Accepted Answer

Enable content filters. Content filters in Azure OpenAI are specifically designed to detect and block harmful content, including security vulnerabilities and malicious instructions, in both prompts and completions. Unlike other parameters, content filters provide a safety layer that actively scans generated code for prohibited patterns, making them the correct choice for minimizing security risks in ambiguous prompts.

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Set the temperature parameter to 0

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Set max_tokens to a low value

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Use a specific system message that requests secure code

Question 4

A retail company wants to automatically analyze in-store video footage to count the number of customers entering and exiting through different doors. Which Azure Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection is the correct capability because it can identify and locate multiple instances of people within a video frame, drawing bounding boxes around each person. This allows the system to track individuals across frames and count them as they cross virtual lines at doorways, distinguishing between entering and exiting movements. Optical Character Recognition (OCR), image classification, and face detection lack the spatial localization and multi-instance tracking required for this specific counting task.

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Optical Character Recognition (OCR)

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Image classification

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Face detection

Question 5

A data scientist trains a regression model to predict house prices. The model achieves very low error on the training data but significantly higher error on a held-out test set. Which problem does this scenario best describe?

Accepted Answer

Overfitting. The scenario describes overfitting, where the model learns the training data too well, including noise and outliers, resulting in very low training error but poor generalization to new data. In Azure Machine Learning, this is often detected by comparing training and validation metrics; a large gap indicates overfitting. The correct answer is B.

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Underfitting

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High bias

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High variance

Question 6

A developer uses Azure OpenAI Service to generate code. They provide a few examples of function definitions and their corresponding descriptions, then ask the model to write a new function based on a new description. Which technique is the developer using?

Accepted Answer

Prompt engineering with few-shot learning. The developer is using prompt engineering with few-shot learning, a technique where a small set of input-output examples (here, function definitions and descriptions) is included in the prompt to guide the model's behavior without modifying its weights. This leverages the model's in-context learning ability to generalize from the provided examples and generate a new function for a new description.

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Fine-tuning the model with the examples

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Training a custom model from scratch

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Using reinforcement learning from human feedback

Question 7

A developer uses Azure OpenAI Service to generate long-form articles. The developer notices that the model tends to repeat the same sentence structures and vocabulary, making the output monotonous. Which parameter should the developer increase to reduce this repetition?

Accepted Answer

C. Increasing the 'frequency penalty' parameter (option C) reduces repetition by penalizing tokens that have already appeared in the generated text. This encourages the model to use a wider variety of sentence structures and vocabulary, making the output less monotonous.

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A

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B

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D

Question 8

A retail company uses ceiling-mounted cameras to monitor shelf stock. They want an automated system that analyzes each camera image to detect if any product is missing from its expected location on the shelf (a product gap). Which Azure Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection is the correct choice because it can identify and locate multiple objects (e.g., product boxes) within an image and determine if expected items are missing from their designated positions on the shelf. Unlike image classification, which assigns a single label to the entire image, object detection provides bounding boxes and class labels for each detected object, enabling precise gap analysis.

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Image classification

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Optical Character Recognition (OCR)

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Face detection

Question 9

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 10

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 11

A retail store uses ceiling-mounted cameras to analyze customer traffic flow. They need to detect when a person enters a specific aisle and determine the direction they are walking. Which Azure Computer Vision capability should they use?

Accepted Answer

People counting (Spatial Analysis). Option C is correct because Spatial Analysis, part of Azure Computer Vision, uses ceiling-mounted cameras to track people's movement and direction in a physical space. It specifically provides people counting and trajectory analysis, making it ideal for detecting when a person enters an aisle and determining their walking direction.

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Image Analysis dense captioning

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Facial recognition

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Optical Character Recognition (OCR)

Question 12

A fashion retailer wants to automatically generate new, unique images of clothing items based on textual descriptions (e.g., 'a blue silk dress with floral patterns'). Which Azure service would be most appropriate to accomplish this?

Accepted Answer

B) Azure OpenAI Service. Azure OpenAI Service provides access to powerful generative AI models like GPT-4 and DALL-E, which can create new images from textual descriptions. This service is specifically designed for generative tasks, such as producing unique clothing images based on prompts like 'a blue silk dress with floral patterns', making it the most appropriate choice.

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A) Azure Machine Learning

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C) Azure Cognitive Search

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D) Custom Vision

Question 13

A data scientist wants to train a model that predicts whether a customer will respond to a marketing offer (yes or no). The dataset includes features such as age, income, past purchase history, and the labeled outcome (responded or not responded) for previous customers. Which type of machine learning is this?

Accepted Answer

Supervised learning. This is supervised learning because the dataset includes labeled outcomes (responded or not responded) for previous customers, which the model uses to learn a mapping from input features (age, income, past purchase history) to the correct output. The goal is to predict a categorical label (yes/no), making it a classification task within supervised learning.

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Unsupervised learning

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Reinforcement learning

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Semi-supervised learning

Question 14

A hospital deploys an AI system to recommend treatment plans for patients. After deployment, the system is found to have significantly lower accuracy for patients from certain racial and ethnic groups because historical medical data for those groups is sparse. Which Microsoft responsible AI principle should the hospital prioritize to address this issue?

Accepted Answer

Fairness. The correct answer is B. Fairness. The AI system's lower accuracy for certain racial and ethnic groups is a direct fairness issue, as it produces biased outcomes due to sparse historical data. Microsoft's Fairness principle requires AI systems to treat all groups equitably and mitigate disparities in performance, which is the core problem here.

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Inclusiveness

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Transparency

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Accountability

Question 15

A hospital wants to automatically anonymize patient medical records by removing all personally identifiable information (PII) such as names, dates, and social security numbers from unstructured text notes. Which Azure AI Language feature should they use?

Accepted Answer

Named entity recognition (NER). Named entity recognition (NER) is the correct Azure AI Language feature because it is specifically designed to identify and categorize entities in unstructured text, including PII categories such as names, dates, and social security numbers. The hospital can use NER's pre-built PII detection model to automatically locate and redact these sensitive elements from patient notes, meeting their anonymization requirement.

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Key phrase extraction

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Sentiment analysis

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Language detection