AI-900 Practice Test 20 — 15 Questions

Question 1

A marketing team wants to automatically analyze thousands of product reviews to determine if each review expresses a positive, negative, or neutral opinion about the product. Which Azure AI Language feature should they use?

Choose an answer to begin — your selection is scored in the full session.

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 requirement to automatically determine the opinion expressed in each product review, making it the appropriate choice for this marketing team's task.

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Key Phrase Extraction

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Named Entity Recognition

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Language Detection

Question 2

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 3

What is 'Azure AI Language's pre-built models' vs 'custom models' and when do you choose each?

Accepted Answer

Pre-built models need no training for general tasks; custom models train on your data for specialised needs. Azure AI Language provides pre-built models that are ready to use for common NLP tasks like sentiment analysis, key phrase extraction, and language detection without any training. Custom models, on the other hand, require you to upload your own labeled data and train a model to handle specialized needs, such as custom entity recognition or custom text classification, which pre-built models cannot address.

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Pre-built models are free; custom models have additional training costs

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Pre-built models only work in English; custom models support all languages

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Custom models are always more accurate than pre-built regardless of the use case

Question 4

What is 'Azure AI Language' and which capabilities does it include?

Accepted Answer

A cloud NLP service providing sentiment analysis, NER, summarisation, CLU, and QA capabilities. Azure AI Language is a cloud-based natural language processing (NLP) service that provides pre-built and custom capabilities for analyzing and understanding text. Option B correctly identifies its core features, including sentiment analysis, named entity recognition (NER), summarization, conversational language understanding (CLU), and question answering (QA). These capabilities allow developers to extract insights, classify intents, and generate responses from unstructured text without needing deep machine learning expertise.

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A programming language developed by Microsoft for building AI applications

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A machine translation service that converts text between all world languages

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A language learning application that helps users practise foreign languages using AI

Question 5

What is the purpose of system messages in Azure OpenAI API calls?

Accepted Answer

Developer-provided instructions that define the AI's role and behavioral constraints for a session. System messages in Azure OpenAI API calls are developer-provided instructions that define the AI's role, tone, and behavioral constraints for the entire session. They act as a persistent meta-prompt that guides the model's responses, ensuring consistency and alignment with the application's requirements.

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Technical error messages returned by the API when something goes wrong

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Messages sent by the operating system to alert of resource usage

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Notifications sent to users when the AI service is experiencing issues

Question 6

What is 'visual question answering' (VQA) in multi-modal AI?

Accepted Answer

AI that answers natural language questions about the content of a specific image. Visual Question Answering (VQA) is a multi-modal AI capability that combines computer vision and natural language processing. The system takes an image as input along with a natural language question about that image, and outputs a relevant answer. This is correct because VQA specifically requires the AI to understand both visual content and textual queries to generate a response, which is exactly what option B describes.

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A quiz application that shows images and asks users multiple-choice questions

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An interview format where candidates answer questions while being recorded on video

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Generating images in response to visual prompts provided by the user

Question 7

What is 'AI fairness' in Microsoft's Responsible AI principles?

Accepted Answer

Ensuring AI systems treat all demographic groups equitably without producing biased outcomes. AI fairness in Microsoft's Responsible AI principles is about ensuring that AI systems treat all demographic groups equitably and do not produce biased outcomes. This involves designing and testing models to detect and mitigate unfairness, such as disparities in accuracy or impact across groups defined by race, gender, age, or other protected attributes.

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Ensuring all Azure AI services are priced fairly for organisations of all sizes

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Distributing AI compute resources equally across all team members in a project

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Ensuring competition in the AI market by preventing monopolistic AI practices

Question 8

What is 'causal inference' and how does it differ from correlation-based machine learning?

Accepted Answer

Causal inference determines whether X actually causes Y; ML finds correlations that predict outcomes. Causal inference specifically aims to determine whether a change in variable X directly causes a change in variable Y, often through controlled experiments or techniques like do-calculus. In contrast, correlation-based machine learning identifies statistical patterns and associations between variables to make predictions, but does not establish a cause-and-effect relationship. This distinction is fundamental in Azure Machine Learning when choosing between predictive modeling (e.g., regression) and causal analysis (e.g., using the DoWhy library).

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Causal inference uses larger training datasets; correlation-based ML uses smaller ones

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Causal inference is exclusively used in medical research; ML is used in business applications

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ML models always establish causal relationships; causal inference is needed only when data quality is poor

Question 9

A research team wants to automatically extract the most important phrases from a large collection of scientific articles to identify emerging trends. The articles are all in English. They do not want to train a custom model. Which built-in Azure AI Language feature should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct Azure AI Language feature because it automatically identifies and returns the most important phrases in a document, which directly supports the goal of extracting key terms from scientific articles to spot emerging trends. This built-in capability requires no custom model training and works out-of-the-box for English text, making it ideal for the research team's use case.

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Sentiment analysis

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Named entity recognition (NER)

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Language detection

Question 10

A manufacturing company wants to use Azure AI to detect surface defects on metal parts. The team has a small set of labeled images of defective and non-defective parts, and images will be taken under various lighting conditions and angles. They need a solution that can leverage a pre-trained model and adapt it to their specific defect types with minimal new training data. Which approach should they take?

Accepted Answer

A. Use Custom Vision to train a classification or object detection model with transfer learning. Custom Vision allows you to use transfer learning, which starts from a pre-trained model and fine-tunes it on your small labeled dataset of defective and non-defective parts. This approach is ideal when you have limited training data and need to adapt the model to specific defect types under varying lighting and angles, as Custom Vision supports both classification and object detection for surface defects.

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B. Use the Optical Character Recognition (OCR) API

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C. Use the Describe Image API (Image Captioning)

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D. Use the Face API

Question 11

What is 'gradient boosting' and how does it differ from random forests?

Accepted Answer

Gradient boosting trains trees sequentially to correct prior errors; random forests trains trees independently in parallel. Gradient boosting is an ensemble technique that builds trees sequentially, where each new tree attempts to correct the errors (residuals) of the previous trees by optimizing a loss function via gradient descent. In contrast, random forests build multiple decision trees independently in parallel using bootstrapped samples and random feature selection, then average their predictions. This sequential error-correction process is the key difference, making option B correct.

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Gradient boosting uses deep neural networks; random forests use shallow trees

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Random forests always outperform gradient boosting for structured data

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Gradient boosting requires GPUs; random forests work only on CPUs

Question 12

What is a 'multi-turn conversation' in the context of Azure Bot Service and conversational AI?

Accepted Answer

A conversation where context is maintained across multiple exchanges to enable natural dialogue. In Azure Bot Service and conversational AI, a multi-turn conversation refers to the ability of a bot to maintain context across multiple exchanges, allowing for natural, coherent dialogue. This is achieved through state management (e.g., using Bot Framework's turn context and storage layers) to track user intent and conversation history, enabling follow-up questions and clarifications without losing context.

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A conversation with multiple human agents taking turns responding

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A bot that can respond in multiple languages within one conversation

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A conversation with multiple topics handled simultaneously in parallel

Question 13

A company analyzes customer reviews to identify common themes. They need to automatically extract the most important concepts from each review, such as 'battery life', 'customer service', and 'price'. Which Azure AI Language feature should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct Azure AI Language feature because it automatically identifies and extracts the most important concepts, such as 'battery life', 'customer service', and 'price', from unstructured text like customer reviews. It returns a list of key phrases that represent the main topics discussed, which directly matches the requirement to extract important concepts.

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Sentiment analysis

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Language detection

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Named entity recognition

Question 14

What is the role of the Azure AI Foundry (AI Studio) playground?

Accepted Answer

An interactive testing environment for experimenting with AI models and prompts without coding. The Azure AI Foundry (AI Studio) playground provides an interactive, no-code environment where developers and data scientists can experiment with generative AI models, test prompts, and adjust parameters like temperature and max tokens before integrating them into applications. This aligns with the need to prototype and validate model behavior without writing code, making it a key tool for rapid iteration in generative AI workloads.

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A gaming environment where AI plays against human developers

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A virtual machine for running AI model training jobs

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A sandbox for testing AI models in isolation from production data

Question 15

What is a training dataset in machine learning?

Accepted Answer

The labeled data used to teach a machine learning model. A training dataset is the labeled data used to teach a machine learning model by allowing it to learn patterns and relationships between features and labels. In Azure Machine Learning, this dataset is fed into an algorithm during the training step, where the model adjusts its internal parameters (e.g., weights in a neural network) to minimize prediction error. Without labeled training data, supervised learning models cannot learn the mapping from inputs to outputs.

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A dataset used to evaluate a trained model's performance on unseen data

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Data that has been cleaned and normalized for analysis

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Real-world data used after model deployment