AI-900 Practice Test 38 — 15 Questions

Question 1

A construction safety team wants to automatically detect whether workers on a job site are wearing hard hats by analyzing images from surveillance cameras. They have a large set of labeled images containing workers wearing hard hats and workers without hard hats. The team needs to train a model that can identify the location of each hard hat in an image. Which Azure Computer Vision service should they use?

Accepted Answer

Custom Vision – Object Detection. Option A is correct because Custom Vision – Object Detection is specifically designed to identify and locate multiple objects within an image by drawing bounding boxes around them. The construction safety team needs to detect the location of each hard hat, which requires object detection, not just classification. Custom Vision allows training a model with labeled images that include bounding box annotations for objects like hard hats.

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Computer Vision – Optical Character Recognition (OCR)

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Face API

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Custom Vision – Image Classification

Question 2

A building management company develops an AI system that uses temperature and humidity sensors to automatically adjust the HVAC system. They want to ensure that the system does not inadvertently cause uncomfortable temperature swings for occupants. Which Microsoft responsible AI principle is most directly relevant to this requirement?

Accepted Answer

Reliability and safety. The requirement to avoid uncomfortable temperature swings directly relates to the system's ability to operate reliably and safely under expected conditions. Microsoft's Reliability and safety principle ensures that AI systems perform consistently, fail gracefully, and do not cause physical harm or discomfort to users. In this HVAC scenario, the AI must be robust to sensor noise and environmental changes to maintain stable temperature control.

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Fairness

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Transparency

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Privacy and security

Question 3

A customer service team wants to automatically determine whether each customer feedback message is positive, negative, or neutral. Which Azure AI Language feature should they use?

Accepted Answer

Sentiment analysis. Sentiment analysis is the correct Azure AI Language feature because it is specifically designed to classify text into positive, negative, or neutral sentiments. This directly matches the customer service team's requirement to automatically determine the sentiment of each feedback message. Other features like key phrase extraction or entity recognition do not perform sentiment classification.

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Key phrase extraction

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Language detection

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Entity recognition

Question 4

A customer service team wants to analyze thousands of call transcripts to identify common complaints and understand whether customer sentiment is positive, negative, or neutral. They plan to use prebuilt Azure AI Language features without any custom training. Which combination of features should they use?

Accepted Answer

Key phrase extraction and sentiment analysis. Option A is correct because the customer service team needs to identify common complaints (which requires extracting key phrases from the call transcripts) and understand sentiment polarity (positive, negative, or neutral). Azure AI Language's prebuilt key phrase extraction returns the main talking points and recurring terms, while sentiment analysis assigns a sentiment score and labels per sentence or document. Both features are available out-of-the-box without any custom training, directly meeting the stated requirements.

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Entity recognition and text translation

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Language detection and summarization

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PII detection and conversation analysis

Question 5

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 6

A company develops an AI system that screens job applications to recommend candidates for interviews. The system consistently recommends male candidates over equally qualified female candidates. Which Microsoft responsible AI principle is most directly violated?

Accepted Answer

Fairness. The AI system's consistent bias toward male candidates over equally qualified female candidates directly violates the fairness principle, which requires AI systems to treat all people equitably and avoid discrimination based on protected attributes like gender. This is a classic case of algorithmic bias, where the model has learned and perpetuated historical or dataset-driven gender disparities in hiring decisions.

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Reliability and safety

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Privacy and security

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Inclusiveness

Question 7

A customer support team receives hundreds of long product reviews every day. They want to automatically summarize each review into a few key sentences to quickly understand the main points. Which prebuilt Azure AI Language feature should they use?

Accepted Answer

Extractive summarization. Extractive summarization is the correct choice because it is specifically designed to condense long documents into a few key sentences by extracting the most important sentences directly from the original text. This aligns perfectly with the customer support team's goal of automatically summarizing hundreds of product reviews into concise, key points for quick understanding.

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Key phrase extraction

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Sentiment analysis

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Entity recognition

Question 8

A data science team trains several machine learning models for a regression task. They observe that Model A has low training error and low test error. Model B has low training error but high test error. Model C has high training error and high test error. Which model would most likely benefit from an ensemble technique that averages the predictions of multiple models?

Accepted Answer

Model B (low training error, high test error). Model B exhibits low training error but high test error, which is a classic sign of overfitting. Ensemble techniques like averaging predictions from multiple models reduce variance and improve generalization, making them most beneficial for overfit models. In Azure Machine Learning, you can use an ensemble pipeline or AutoML's VotingEnsemble to combine diverse models and lower test error.

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Model A (low training error, low test error)

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Model C (high training error, high test error)

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None of the models would benefit from an ensemble technique

Question 9

What is the purpose of Azure AI Content Safety in the context of generative AI deployments?

Accepted Answer

To detect and filter harmful content in AI prompts and responses. Azure AI Content Safety is a service designed to detect and filter harmful content, such as hate speech, violence, self-harm, and sexually explicit material, in both user prompts and AI-generated responses. In generative AI deployments, this ensures that the model's outputs comply with safety policies and regulatory requirements, preventing the dissemination of offensive or dangerous content.

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To compress generated content for faster delivery

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To measure the quality and accuracy of AI-generated responses

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To ensure AI content is written in the correct language

Question 10

A city government implements an AI system to analyze traffic camera feeds and predict congestion. The system is found to be less accurate for neighborhoods with lower-income populations because historical traffic data from those areas is sparse. Which Microsoft responsible AI principle is most directly relevant to address this issue?

Accepted Answer

Fairness. The system's reduced accuracy for lower-income neighborhoods due to sparse historical data is a direct fairness issue. Fairness in AI requires that systems perform equitably across different demographic groups, and this scenario describes a clear disparity in model performance based on socioeconomic factors. Addressing this would involve techniques like data augmentation, reweighting, or collecting more representative data to mitigate bias.

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Transparency

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Accountability

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Privacy and security

Question 11

A customer service company uses Azure OpenAI Service to generate automated replies to customer inquiries. They want each reply to adopt a polite and empathetic tone. Which configuration should they use to guide the model's behavior without retraining?

Accepted Answer

Define a system message that instructs the model to be polite and empathetic.. Option C is correct because a system message in Azure OpenAI Service allows you to set the context and tone for the model's responses without retraining. By defining a system message that instructs the model to be polite and empathetic, you guide the model's behavior at inference time, ensuring replies adopt the desired tone.

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Set the temperature parameter to a high value (e.g., 1.0).

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Set the top_p parameter to a low value (e.g., 0.1).

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Set the max_tokens parameter to a specific value (e.g., 150).

Question 12

What is 'quality control' computer vision and how is it used in manufacturing?

Accepted Answer

Detecting manufacturing defects at production line speeds with consistent accuracy. Quality control in computer vision refers to using AI models to inspect products on a manufacturing line, detecting defects such as scratches, dents, or misalignments at high speed. Azure Custom Vision or Azure Computer Vision can be trained on labeled images of good and defective items to perform real-time inference, ensuring consistent accuracy far beyond human visual inspection. This directly addresses the need for automated, scalable defect detection in production environments.

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Monitoring the quality of AI model outputs to ensure they meet accuracy standards

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Verifying that factory video surveillance cameras meet quality standards

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Controlling the quality of training images used to build computer vision models

Question 13

What is the difference between Azure OpenAI Service and the public OpenAI API?

Accepted Answer

Azure OpenAI adds enterprise security, compliance, private networking, and Azure integration. Option B is correct because Azure OpenAI Service is a Microsoft Azure-based offering that wraps the same underlying OpenAI models (GPT-4, GPT-3.5, etc.) with enterprise-grade features such as Azure Active Directory authentication, private endpoints via Azure Virtual Network, compliance certifications (e.g., ISO 27001, SOC 2), and seamless integration with other Azure services like Cognitive Search and Logic Apps. The public OpenAI API lacks these enterprise controls, making Azure OpenAI the preferred choice for organizations that require data residency, network isolation, and managed identity access.

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Azure OpenAI has different models that perform better than OpenAI

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Azure OpenAI is only available for government customers

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Azure OpenAI does not support GPT-4 models

Question 14

A company uses Azure OpenAI Service to generate summaries of long technical documents. They notice that the model sometimes produces summaries that sound plausible but contain factual errors contradicting the source document. Which concept describes this type of error in large language models?

Accepted Answer

Hallucination. Option B is correct because hallucination in large language models refers to the generation of content that is factually incorrect or nonsensical but presented with confidence. In this scenario, the model produces summaries that sound plausible yet contain factual errors contradicting the source document, which is the hallmark of hallucination. This occurs because the model generates text based on probabilistic patterns rather than verifying facts against the input.

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Overfitting

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Tokenization

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Bias

Question 15

What is the purpose of Azure AI Video Indexer's transcript feature?

Accepted Answer

To automatically convert speech in videos to searchable text with timestamps. Azure AI Video Indexer's transcript feature uses automatic speech recognition (ASR) to convert spoken audio in videos into a text transcript, which is then indexed with precise timestamps for each word or phrase. This enables users to search, navigate, and analyze video content by keyword or phrase, making the video's audio content fully searchable and accessible.

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To translate video subtitles into multiple languages

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To generate written scripts for producing new videos

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To extract text visible in video frames (on-screen text)