AI-900 Practice Test 48 — 15 Questions

Question 1

A data scientist wants to train a machine learning model to predict the exact market price of a house based on features such as square footage, number of bedrooms, and location. Which type of machine learning task should be used?

Accepted Answer

Regression. Predicting the exact market price of a house is a regression task because the target variable (price) is a continuous numeric value. Regression algorithms, such as linear regression or decision tree regression, learn the relationship between input features (e.g., square footage, bedrooms, location) and a continuous output. In Azure Machine Learning, you would select a regression model from the designer or AutoML to solve this problem.

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Classification

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Clustering

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Anomaly Detection

Question 2

What is the Azure AI Speech service's 'speaker diarization' feature?

Accepted Answer

Identifying and labeling different speakers in a multi-speaker audio recording. Speaker diarization is the process of partitioning an audio stream into homogeneous segments according to the speaker identity. It answers the question 'who spoke when?' by assigning a unique label (e.g., Speaker 1, Speaker 2) to each segment in a multi-speaker recording. This is a built-in capability of Azure AI Speech, often used in meeting transcription and call center analytics.

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Converting a speaker's voice to a different language in real time

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Measuring the speaking speed (words per minute) of each speaker

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Filtering out background speakers from a primary speaker's recording

Question 3

A global social media platform wants to automatically detect the language of user posts to route them to appropriate content moderators. The posts are short and often contain mixed scripts. Which Azure AI Language feature should they use?

Accepted Answer

Language detection. Language detection is the correct Azure AI Language feature because it is specifically designed to identify the primary language of text, including short and mixed-script content. This allows the platform to automatically route posts to moderators who speak the detected language, directly addressing the requirement.

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Sentiment analysis

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Key phrase extraction

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Entity recognition

Question 4

A healthcare start-up proposes a fully automated AI system to diagnose patients from medical scans without any human doctor review. They claim the system is 99% accurate. According to Microsoft's responsible AI principles, which principle is most directly violated by removing human oversight from this critical decision-making process?

Accepted Answer

Accountability. Option D is correct because removing human oversight from a fully automated diagnostic system violates the accountability principle. Microsoft's responsible AI principle of accountability requires that humans remain responsible for AI-driven decisions, especially in high-stakes healthcare scenarios where errors can have life-or-death consequences. By eliminating any human doctor review, the start-up fails to ensure that a human can intervene, validate, or take responsibility for the system's outputs.

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Fairness

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Reliability and safety

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Transparency

Question 5

A developer uses Azure OpenAI to generate marketing copy. They want the model to follow a very specific tone and style. They provide a few high-quality examples of desired output before the actual prompt. 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 high-quality examples (the 'shots') in the prompt to guide the model's output toward a desired tone and style. This technique leverages the model's in-context learning ability without updating its weights, making it ideal for quick adaptation to specific formatting or voice requirements.

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Zero-shot learning

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Fine-tuning

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Reinforcement learning with human feedback (RLHF)

Question 6

What is the Azure OpenAI 'content filter' and what categories of content does it cover?

Accepted Answer

Safety filters that detect and block hate speech, sexual, violent, and self-harm content in inputs and outputs. Option B is correct because Azure OpenAI's content filter is a safety system that uses multi-level classification models to detect and block harmful content across four categories: hate, sexual, violence, and self-harm. It applies to both user prompts (inputs) and model completions (outputs), ensuring responsible AI usage.

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A feature that limits the length of API responses to control costs

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A spam filter that removes irrelevant or off-topic user messages

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A filter that removes personally identifiable information from model outputs

Question 7

What is the purpose of 'top_p' (nucleus sampling) in Azure OpenAI API calls?

Accepted Answer

A sampling method that restricts token selection to the most probable token set. Option B is correct because 'top_p' (nucleus sampling) in Azure OpenAI API calls controls the cumulative probability threshold for token selection. Instead of considering all possible next tokens, the model selects from the smallest set of tokens whose cumulative probability exceeds the 'top_p' value (e.g., 0.9 means the model considers only the top tokens that together have a 90% chance). This reduces randomness while allowing more natural variation than fixed 'top_k' sampling.

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The maximum number of paragraphs in the generated response

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A parameter that sets the minimum response quality threshold

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The priority level of the API request in a queue

Question 8

What does the 'read' operation in Azure AI Vision do?

Accepted Answer

Extracts printed and handwritten text from images and documents. The 'read' operation in Azure AI Vision is specifically designed to extract printed and handwritten text from images and documents using Optical Character Recognition (OCR) technology. It returns the detected text along with bounding box coordinates and confidence scores, making it suitable for digitizing documents, processing forms, and extracting text from photos.

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Reads and describes what's happening in a video

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Reads and verifies digital signatures in documents

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Reads metadata (EXIF data) embedded in image files

Question 9

What is the Azure AI Language Studio used for in addition to testing built-in features?

Accepted Answer

Building and training custom NLP models including custom classification and NER. Option B is correct because Azure AI Language Studio is a comprehensive tool that allows users to not only test pre-built language features but also to build, train, and deploy custom NLP models, such as custom text classification and custom named entity recognition (NER). This extends beyond simple testing to enable tailored solutions for domain-specific language understanding.

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Only for testing pre-built language features without any customization

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Writing and executing Python code for NLP tasks

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Managing billing and API keys for Azure AI Language

Question 10

What is the purpose of Azure AI Vision's 'product recognition' feature?

Accepted Answer

Identifying retail products in images to match them to a product catalog without barcodes. Azure AI Vision's 'product recognition' feature is designed to identify retail products in images and match them to a product catalog without relying on barcodes. It uses computer vision models trained on product images to detect and recognize items based on visual features like packaging, logos, and shape, enabling inventory management and checkout automation in retail scenarios.

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Recognizing counterfeit products in supply chain images

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Recognizing products mentioned in customer text reviews

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Detecting product defects in manufacturing quality control

Question 11

What is computer vision and give three real-world application examples.

Accepted Answer

Computer vision enables machines to understand visual data — used in autonomous driving, medical imaging, and retail automation. Option B is correct because computer vision is a field of AI that enables machines to interpret and make decisions based on visual data from the world, such as images and videos. The three examples given—autonomous driving (e.g., detecting pedestrians and lane markings), medical imaging (e.g., analyzing X-rays for tumors), and retail automation (e.g., self-checkout systems recognizing products)—are classic real-world applications that demonstrate the breadth of computer vision beyond simple text recognition.

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Computer vision is limited to text recognition only; it cannot detect objects

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Computer vision only works on satellite imagery for geographic analysis

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Computer vision requires extremely expensive hardware unavailable in the cloud

Question 12

A healthcare organization is developing an AI system to recommend treatment plans for patients based on their medical history. According to Microsoft's responsible AI principles, which principle is most directly concerned with ensuring that the system protects patients' health data from unauthorized access or misuse?

Accepted Answer

Privacy and security. The Privacy and security principle is most directly concerned with protecting patients' health data from unauthorized access or misuse. In this scenario, the AI system must comply with regulations like HIPAA and GDPR, ensuring data encryption, access controls, and audit logs are in place to safeguard sensitive medical information.

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Transparency

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Fairness

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Reliability and safety

Question 13

A home security system uses a camera to detect common household objects such as a person, a pet, a bag, or a package. The system needs to identify the presence and location (bounding box) of these objects in images. The development team wants to use a prebuilt Azure AI service without any custom training. Which Azure Computer Vision capability should they use?

Accepted Answer

Image Analysis – Object Detection. Option B (Image Analysis – Object Detection) is correct because the requirement is to identify both the presence and location (bounding box) of common household objects in images using a prebuilt Azure AI service without custom training. Azure Computer Vision's Image Analysis – Object Detection provides pre-trained models that can detect multiple objects, including people, pets, bags, and packages, and return their bounding box coordinates, exactly matching the scenario.

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Optical Character Recognition (OCR)

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Image Analysis – Image Captioning

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Custom Vision

Question 14

A data scientist trains a regression model to predict energy consumption for a smart building. The model achieves very low error on the training data but performs significantly worse on a held-out validation set. Which technique would most directly address this problem?

Accepted Answer

Regularization. The model's low training error but high validation error indicates overfitting, where the model has memorized the training data rather than learning generalizable patterns. Regularization (e.g., L1 or L2) directly penalizes large coefficients, reducing model complexity and improving generalization to unseen data.

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Feature engineering

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Cross-validation

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Hyperparameter tuning

Question 15

A financial services company uses an AI system to recommend personalized investment portfolios. A customer requests an explanation of why a particular investment was recommended. Which Microsoft responsible AI principle is primarily focused on ensuring the company can provide this explanation?

Accepted Answer

Transparency. Transparency is the correct principle because it directly addresses the need for AI systems to be understandable and interpretable. In this scenario, the customer's request for an explanation of a specific investment recommendation requires the AI to provide clear reasoning for its output, which is the core of transparency. This principle ensures that the company can explain how and why a decision was made, building trust and enabling oversight.

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Accountability

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Fairness

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Reliability