Question 1easymultiple choice
Full question →AI-900 · topic practice
Describe Artificial Intelligence Workloads And Considerations practice questions
Use this page to practise AI-900 Describe Artificial Intelligence Workloads And Considerations practice questions. The goal is not to memorise dumps, but to understand the concept, review the explanation and improve your exam readiness.
20 questionsDomain: Describe Artificial Intelligence Workloads And Considerations
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What to know about Describe Artificial Intelligence Workloads And Considerations
Describe Artificial Intelligence Workloads And Considerations questions test whether you can apply the concept in context, not just recognise a definition.
How the topic appears in realistic exam-style scenarios.
Which detail in the question changes the correct answer.
How to eliminate plausible but wrong options.
How to connect the question back to the wider exam objective.
Practice set
Describe Artificial Intelligence Workloads And Considerations questions
20 questions · select your answer, then reveal the explanation
Question 2mediummultiple choice
Full question →A company uses a large language model to generate answers to employee questions about internal HR policies. However, the model sometimes produces answers that are factually incorrect or not based on the official policies. To reduce these inaccuracies, the company wants to provide the model with relevant, up-to-date policy documents as extra context before generating a response. Which technique is being applied?
Question 3mediummultiple choice
Full question →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?
Question 4easymultiple choice
Full question →A company needs to automatically extract text from scanned invoices that contain both printed text and handwritten notes. Which Azure AI service is specifically designed to handle this type of document?
Question 5easymultiple choice
Full question →A company wants to use Azure Computer Vision to automatically analyze images of handwritten forms and extract the text for data entry. Which prebuilt Azure Computer Vision capability should they use?
Question 6mediummultiple choice
Full question →A city traffic department wants to use Azure Computer Vision to automatically analyze live video feeds from traffic cameras. They need to detect and locate common objects such as cars, pedestrians, and bicycles in each frame. The department does not have a labeled dataset for custom training. Which prebuilt Azure Computer Vision capability should they use?
Question 7easymultiple choice
Full question →A company deploys an AI system to screen job applications. The system is a complex neural network that learns patterns from historical hiring data. A rejected candidate asks for an explanation, but the development team cannot describe how the decision was reached. Which Microsoft responsible AI principle is most directly violated?
Question 8mediummulti select
Full question →A company needs to extract text from scanned invoices and receipts. Which Azure services are suitable for this task? (Choose two.)
Question 9mediummultiple choice
Full question →A company is developing a chatbot that can both answer customer questions in natural language and create images on demand (e.g., 'Generate a picture of a product prototype'). Which combination of Azure generative AI models should they integrate?
Question 10easymultiple choice
Full question →A bank deploys an AI system to automatically approve or reject loan applications. After six months, an audit reveals that the system approves loans at a significantly lower rate for applicants from a specific ethnic group compared to other groups with similar financial profiles. Which Microsoft responsible AI principle is most directly violated by this outcome?
Question 11mediummultiple choice
Full question →A data scientist is training a regression model to predict energy consumption. The dataset includes features like temperature, humidity, time of day, and day of week. After training, the model performs well on the training set but poorly on new data. Which approach would most likely help reduce this problem?
Question 12mediummultiple choice
Full question →A data scientist trains a machine learning model to predict house prices based on features like square footage, number of bedrooms, and location. The model achieves a very low error on the training data but performs poorly on a held-out test set. Which term best describes this situation?
Question 13mediummultiple choice
Full question →A data scientist trains a linear regression model to predict house prices. The model's training error is very high, and its test error is nearly as high. Which term best describes this situation?
Question 14hardmultiple choice
Full question →A data scientist is training a regression model to predict house prices. The model performs near perfectly on the training data but poorly on a held-out test set. The scientist suspects the model is memorizing the training data instead of learning general patterns. Which technique is most appropriate to directly address this issue?
Question 15mediummultiple choice
Full question →A data scientist trains a machine learning model on historical sales data to predict future sales volume. The model achieves 99% accuracy on the training dataset but only 75% accuracy on a separate test dataset. What is the most likely issue with this model?
Question 16mediummultiple choice
Full question →A data scientist is developing a classification model to detect fraudulent transactions. The dataset is split into training and test sets. The data scientist repeatedly tunes the model's hyperparameters and evaluates performance on the test set until the test accuracy reaches 95%. However, when the model is deployed on new, unseen data, its accuracy drops to 70%. Which concept best explains this performance degradation?
Question 17mediummultiple choice
Full question →A data scientist trains a classification model on a dataset of 10,000 labeled emails to distinguish spam from non-spam. The model achieves 99% accuracy on the training data but only 70% accuracy on a held-out test set. Which term best describes this situation?
Question 18mediummultiple choice
Full question →A data scientist trains a regression model to predict house prices. The model has a mean absolute error (MAE) of $5,000 on the test set. Which statement best interprets this metric?
Question 19mediummultiple choice
Full question →A data scientist trains a regression model to predict house prices. The model performs poorly on both the training data and the test data, showing high error in both sets. Which concept best describes this situation?
Question 20mediummultiple choice
Full question →A data scientist trains a classification model to predict whether an email is spam or not. The model achieves 98% accuracy on the test set, but upon inspection, it classifies all emails as 'not spam' because the dataset has 95% non-spam emails. What is the most likely issue?
Watch out for
Common Describe Artificial Intelligence Workloads And Considerations exam traps
- ▸Answering from memory before reading the full scenario.
- ▸Missing a constraint such as cost, availability, security, scope or command context.
- ▸Choosing a broad answer when the question asks for the most specific fix.
- ▸Ignoring why the wrong options are tempting.
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- What does the AI-900 exam test about Describe Artificial Intelligence Workloads And Considerations?
- Describe Artificial Intelligence Workloads And Considerations questions test whether you can apply the concept in context, not just recognise a definition.
- How should I use these practice questions?
- Select your answer before revealing the explanation. Then read why each option is right or wrong — this active recall approach builds retention far faster than re-reading notes.
- Can I practise just Describe Artificial Intelligence Workloads And Considerations questions in a focused session?
- Yes — the session launcher on this page draws every question from the Describe Artificial Intelligence Workloads And Considerations domain. Use a 10-question session first to gauge your baseline, then move to 20 or 30 once the weak spots are clear.
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- These are original practice questions written to test the same concepts the AI-900 exam covers. They are not copied from any real exam or dump site.
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Exam traps to avoid
- ▸Answering from memory before reading the full scenario.
- ▸Missing a constraint such as cost, availability, security, scope or command context.
- ▸Choosing a broad answer when the question asks for the most specific fix.
- ▸Ignoring why the wrong options are tempting.