AI-900 Practice Test 35 — 15 Questions

Question 1

A retail company wants to use Azure Computer Vision to monitor shelf inventory. They need to detect whether specific products (e.g., 'Brand A cereal', 'Brand B cereal') are present on a shelf and count the number of units of each product. They have a labeled dataset with images of each product category. Which Azure Computer Vision capability should they use?

Accepted Answer

Custom object detection. Custom object detection (A) is correct because the retail company needs to detect and count specific product categories (e.g., 'Brand A cereal', 'Brand B cereal') from images, which requires training a model on a labeled dataset of those products. Azure Custom Vision's object detection capability allows you to upload labeled images, train a model to identify and locate multiple instances of each product in an image, and return bounding boxes with counts per class. This is the only option that supports custom, multi-class object detection and counting from user-provided training data.

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Optical character recognition (OCR)

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Prebuilt image analysis (Describe Image)

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Facial recognition

Question 2

A developer is using Azure OpenAI with GPT-4 to build a chatbot that answers legal questions based on a company's internal policy documents. The developer wants the model's responses to be maximally deterministic and factual, avoiding any creative or speculative language. Which parameter should the developer set to the lowest possible value in the API call?

Accepted Answer

Temperature. Temperature controls the randomness of the model's output. Setting it to the lowest possible value (0) makes the model deterministic, always choosing the most likely next token, which is ideal for factual, non-creative responses like legal answers. Higher temperature values introduce variability and creativity, which would be undesirable for this use case.

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Frequency penalty

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Presence penalty

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Top_p

Question 3

A developer is using Azure OpenAI to generate creative product descriptions. The outputs are often repetitive and lack variety. The developer wants to increase the diversity of the generated text while still keeping it coherent. Which parameter should the developer increase?

Accepted Answer

Temperature. Increasing the temperature parameter makes the model's output more random by amplifying the probability of less likely tokens, which increases diversity and reduces repetition. A higher temperature (e.g., 0.9) flattens the probability distribution, allowing the model to choose more varied words while still maintaining coherence, as long as the temperature is not set too high (e.g., above 1.0).

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Top_p

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Max_tokens

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Frequency_penalty

Question 4

A digital marketing agency wants to use an AI model that can create original images of products in different styles based on text prompts, such as 'a luxury watch in a futuristic setting.' Which Azure service should they choose?

Accepted Answer

Azure OpenAI Service. Azure OpenAI Service provides access to generative AI models like DALL-E, which can create original images from text prompts. This service is specifically designed for tasks such as generating product images in different styles based on descriptive text, making it the correct choice for the agency's requirement.

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Azure AI Language

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Azure Cognitive Search

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Azure Computer Vision

Question 5

What is the purpose of key phrase extraction in Azure AI Language?

Accepted Answer

To identify the main talking points and important concepts in text. Key phrase extraction in Azure AI Language is designed to identify the main talking points and important concepts within a given text. It analyzes the text structure and returns a list of key phrases that summarize the core topics, enabling quick understanding of the document's primary themes without reading the entire content.

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To translate key phrases between languages

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To classify text into positive or negative sentiment

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To generate new text based on key topics

Question 6

What is the 'inclusiveness' principle in Microsoft's responsible AI framework?

Accepted Answer

AI systems should be designed to benefit and empower all people, including marginalized groups. The 'inclusiveness' principle in Microsoft's responsible AI framework mandates that AI systems should be designed to benefit and empower all people, including marginalized groups. This ensures that AI solutions do not perpetuate bias or exclude underrepresented populations, aligning with Microsoft's commitment to fairness and accessibility in AI workloads.

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AI systems should be available in all countries without restriction

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AI systems should be open-source and freely available

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AI systems should include all possible features regardless of relevance

Question 7

What is 'personally identifiable information' (PII) detection in Azure AI Language?

Accepted Answer

Identifying and optionally redacting sensitive personal information (names, SSNs, emails) in text. Option B is correct because PII detection in Azure AI Language is a pre-built feature that identifies sensitive personal data such as names, social security numbers, email addresses, and phone numbers within unstructured text. It can also redact (mask) these entities to help comply with data privacy regulations like GDPR. This is a core capability of the Azure AI Language service's Text Analytics API.

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Verifying that a user's identity matches their stated credentials during login

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Tracking which users have accessed personally sensitive Azure resources

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Detecting when users are sharing their own personal information in an inappropriate context

Question 8

What is a system prompt in an Azure OpenAI deployment?

Accepted Answer

An instruction that defines the model's behavior, persona, and constraints for a session. Option B is correct because a system prompt in Azure OpenAI is a foundational instruction set that defines the model's behavior, persona, and constraints for a session. It acts as a persistent directive that guides the model's responses throughout the conversation, ensuring alignment with specific use cases like tone, safety, or domain focus.

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A technical error message returned when the model fails

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A user's first message to start a conversation

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A command to restart the AI model instance

Question 9

Which AI workload type attempts to predict a continuous numeric value based on input features?

Accepted Answer

Regression. Regression is the correct AI workload type because it is specifically designed to predict a continuous numeric value (e.g., price, temperature, sales amount) based on input features. Unlike classification, which predicts discrete categories, regression models output a real number by learning a mapping function from the input variables to a continuous target variable, often using algorithms like linear regression, decision trees, or neural networks.

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Classification

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Clustering

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Anomaly detection

Question 10

Which Azure AI service allows you to analyze medical images for clinical decision support?

Accepted Answer

Azure AI Health Insights and specialized medical imaging AI. Azure AI Health Insights includes specialized medical imaging AI capabilities designed to analyze radiology and other medical images for clinical decision support. This service is built on domain-specific models trained on medical data, unlike general-purpose computer vision services.

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Azure AI Custom Vision with medical training data

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Azure AI Translator for medical documents

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Azure AI Speech for dictation only

Question 11

What is 'transfer learning' and how is it different from training from scratch?

Accepted Answer

Transfer learning fine-tunes a pre-trained model on a new task — requiring far less data and compute than training from scratch. Transfer learning starts with a model already trained on a large dataset (e.g., ImageNet) and fine-tunes it on a smaller, task-specific dataset. This approach requires significantly less data and computational resources compared to training from scratch, where all model weights are randomly initialized and learned from the ground up. It is especially effective when the new task is similar to the original training task, allowing the pre-trained features to be reused.

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Transfer learning and training from scratch produce identical results

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Transfer learning copies model weights between Azure subscriptions

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Transfer learning is used only when the original training data is unavailable

Question 12

What is the purpose of Azure Machine Learning's automated ML (AutoML) feature?

Accepted Answer

To automatically try multiple algorithms and hyperparameters to find the best model. Azure Machine Learning's automated ML (AutoML) feature automates the process of algorithm selection and hyperparameter tuning. It iterates through various machine learning algorithms and their hyperparameter combinations, evaluating each based on a primary metric (e.g., accuracy, AUC_weighted) to identify the best-performing model for the given dataset and task (classification, regression, or forecasting). This significantly reduces the manual effort and time required for model development.

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To automatically collect and label training data

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To automatically deploy trained models to production

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To automatically monitor models for performance degradation

Question 13

What is the Azure AI Vision service's 'Image Analysis 4.0' major new capability compared to previous versions?

Accepted Answer

The Florence foundation model enabling detailed captions, dense captioning, background removal, and multimodal embeddings. Image Analysis 4.0 introduces the Florence foundation model, which significantly enhances image understanding capabilities. This model enables detailed captions, dense captioning (generating captions for multiple regions within an image), background removal, and multimodal embeddings that align images and text in a shared vector space. These features go far beyond the classification, object detection, and OCR capabilities of version 3.x.

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Support for processing video files, which was not available in version 3.x

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Support for the first time for color analysis features in images

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The ability to process images larger than 4MB for the first time

Question 14

What does it mean for an ML model to 'generalize'?

Accepted Answer

The model's ability to perform well on new, unseen data by learning underlying patterns rather than memorizing training examples. Generalization in machine learning refers to the model's ability to accurately predict outcomes on new, unseen data by learning the true underlying patterns from the training data, rather than simply memorizing the training examples (overfitting). A model that generalizes well will maintain high performance on a validation or test dataset that was not used during training, which is a core requirement for deploying reliable ML solutions in Azure Machine Learning.

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Making the model work for all programming languages and platforms

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Making the model output descriptions in plain language for non-technical users

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Training a model that works for all possible tasks without specialization

Question 15

What is 'temperature' in the context of generative AI model parameters?

Accepted Answer

A parameter controlling the randomness and creativity of model outputs. Temperature is a hyperparameter in generative AI models (such as GPT) that controls the randomness of token sampling during text generation. A higher temperature (e.g., 1.0) increases creativity by making less probable tokens more likely to be chosen, while a lower temperature (e.g., 0.1) makes the output more deterministic and focused on the most probable tokens. This directly affects the diversity and novelty of the generated content.

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The operating temperature of the GPU hardware running the model

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The time required to generate a response

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The minimum confidence threshold for a response