AI-900 Practice Test 20 — 15 Questions

Question 1

An autonomous vehicle system needs to both read the speed limit text on traffic signs and detect the presence and location of pedestrians crossing the road. Which combination of Azure Computer Vision capabilities should be used?

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

Accepted Answer

Optical Character Recognition (OCR) and Object Detection. The autonomous vehicle system requires two distinct capabilities: reading text from speed limit signs (OCR) and detecting the presence and location of pedestrians (Object Detection). OCR extracts text from images, while Object Detection identifies objects and provides bounding boxes around them, making option C the correct combination.

Answer

Image Classification and OCR

Answer

Semantic Segmentation and OCR

Answer

Face Detection and OCR

Question 2

What is 'model evaluation' and what metrics are used for different ML task types?

Accepted Answer

Different tasks use different metrics: F1 for classification, RMSE for regression, mAP for detection. Model evaluation is the process of assessing how well a trained machine learning model performs on unseen data. Different ML task types require different metrics because they measure distinct aspects of performance: for classification tasks, F1-score balances precision and recall; for regression tasks, RMSE (Root Mean Squared Error) quantifies prediction error in the same units as the target; for object detection tasks, mAP (mean Average Precision) evaluates both localization and classification accuracy. Option B correctly identifies these task-specific metrics.

Answer

Accuracy is the only metric needed for all ML task types

Answer

Model evaluation is only needed before deployment, not after

Answer

The only reliable evaluation is user feedback after the model is deployed in production

Question 3

What does Azure AI Speech's 'custom neural voice' capability allow organizations to do?

Accepted Answer

Create a unique branded synthetic voice trained on recordings of a specific speaker. Azure AI Speech's 'custom neural voice' capability allows organizations to create a unique, branded synthetic voice by training a neural text-to-speech model on recordings of a specific speaker. This enables high-quality, natural-sounding voice personalization for applications like virtual assistants, audiobooks, and customer service bots, while requiring explicit speaker consent and adherence to responsible AI guidelines.

Answer

Train speech recognition to understand unique industry vocabulary

Answer

Translate speech from one language to another in real time

Answer

Automatically identify accents and dialects in speech

Question 4

What does it mean for an AI system to be 'inclusive' according to Microsoft's responsible AI principles?

Accepted Answer

AI systems should empower all people including those with disabilities and from diverse backgrounds. Microsoft's responsible AI principle of inclusiveness requires that AI systems are designed to empower everyone, including people with disabilities and those from diverse cultural, linguistic, and socioeconomic backgrounds. This means the system should account for accessibility needs (e.g., screen readers, voice input) and avoid biases that could exclude or disadvantage any group.

Answer

AI systems should include as many features as possible regardless of user needs

Answer

AI data should include examples from every country in the world

Answer

All employees should be included in AI model training decisions

Question 5

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?

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.

Answer

Key Phrase Extraction

Answer

Named Entity Recognition

Answer

Language Detection

Question 6

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.

Answer

Key phrase extraction

Answer

Language detection

Answer

Entity recognition

Question 7

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.

Answer

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

Answer

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

Answer

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

Question 8

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.

Answer

A programming language developed by Microsoft for building AI applications

Answer

A machine translation service that converts text between all world languages

Answer

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

Question 9

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.

Answer

Technical error messages returned by the API when something goes wrong

Answer

Messages sent by the operating system to alert of resource usage

Answer

Notifications sent to users when the AI service is experiencing issues

Question 10

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.

Answer

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

Answer

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

Answer

Generating images in response to visual prompts provided by the user

Question 11

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.

Answer

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

Answer

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

Answer

Ensuring competition in the AI market by preventing monopolistic AI practices

Question 12

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).

Answer

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

Answer

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

Answer

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

Question 13

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.

Answer

Sentiment analysis

Answer

Named entity recognition (NER)

Answer

Language detection

Question 14

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.

Answer

B. Use the Optical Character Recognition (OCR) API

Answer

C. Use the Describe Image API (Image Captioning)

Answer

D. Use the Face API

Question 15

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.

Answer

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

Answer

Random forests always outperform gradient boosting for structured data

Answer

Gradient boosting requires GPUs; random forests work only on CPUs