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Microsoft AI-900 Exam Questions
Free Microsoft AI-900 Exam Actual Questions
Note: Premium Questions for AI-900 were last updated On Jun. 06, 2026 (see below)
What should you do to reduce the number of false positives produced by a machine learning classification model?
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module ''Describe features of machine learning on Azure'', a classification model outputs a probability score representing how likely each input belongs to a particular class. To decide whether a prediction is ''positive'' or ''negative,'' the model applies a threshold (often defaulted to 0.5). Adjusting this threshold directly affects the balance between false positives and false negatives.
A false positive occurs when the model incorrectly predicts a positive outcome (for example, predicting that a patient has a disease when they do not).
A false negative occurs when the model fails to predict a true positive (for example, predicting that a patient does not have a disease when they actually do).
To reduce false positives, you must make the model less likely to classify borderline cases as positive. This is done by increasing the decision threshold, thereby favoring false negatives (because the model will only classify a case as positive when the prediction confidence is very high). In other words, by moving the threshold upward, you tighten the model's standard for what qualifies as a ''positive'' prediction, reducing incorrect positives.
Let's review why other options are incorrect:
A . Include test data in training data: This contaminates your dataset and causes overfitting, which leads to unreliable performance metrics.
B . Increase the number of training iterations: This may improve learning but doesn't specifically target false positives.
C . Modify the threshold in favor of false positives: That would increase, not reduce, false positives.
Therefore, the correct step to reduce false positives is to adjust the threshold in favor of false negatives, making the model more conservative when labeling a case as positive --- hence, Answer: D.
Stating the source of the data used to train a model is an example of which responsible Al principle?
According to Microsoft's Responsible AI Principles, Transparency means that AI systems should clearly communicate how they operate, including data sources, limitations, and decision-making processes. Stating the source of data used to train a model helps users understand where the model's knowledge comes from, enabling informed trust and accountability.
Transparency ensures that organizations disclose relevant details about data collection and model design, especially for compliance, fairness, and reproducibility.
Other options are incorrect:
A . Fairness: Focuses on avoiding bias and ensuring equitable outcomes.
C . Reliability and safety: Ensures AI performs consistently and safely.
D . Privacy and security: Protects user data and maintains confidentiality.
Thus, the principle illustrated by disclosing training data sources is Transparency.
You need to develop a chatbot for a website. The chatbot must answer users' questions based on the information in the following documents:
A product troubleshooting guide in a Microsoft Word document
A frequently asked questions (FAQ) list on a webpage
Which service should you use to process the documents?
QnA Maker is an Azure Cognitive Service used to build question-and-answer knowledge bases from structured and unstructured documents, such as FAQs, product manuals, or webpages. According to the AI-900 study guide and Microsoft Learn module ''Build a knowledge base with QnA Maker'', this service allows you to extract question-answer pairs from existing data sources like FAQ pages, PDF files, or Word documents.
In this scenario, you have:
A product troubleshooting guide (Word document)
A FAQ webpage
QnA Maker can automatically read both sources, extract relevant Q&A pairs, and create a knowledge base that your chatbot can use to respond to user queries intelligently.
To clarify the other options:
A . Azure Bot Service provides the chatbot interface and conversation logic but doesn't extract knowledge from documents.
B . Language Understanding (LUIS) identifies intents and entities in natural language input, but it's not used to read document content.
C . Text Analytics is used for key phrase extraction and sentiment analysis, not Q&A creation.
Therefore, the correct service for processing FAQ-style and document-based content into a question-answering bot is QnA Maker.
Which type of machine learning should you use to identify groups of people who have similar purchasing habits?
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module ''Describe features of common AI workloads'', clustering is a type of unsupervised machine learning used to group data points that share similar characteristics. In unsupervised learning, the data provided to the model does not have predefined labels or outcomes. Instead, the algorithm identifies inherent patterns or groupings within the dataset based on similarities in input features.
In this scenario, the task is to identify groups of people who have similar purchasing habits. There is no predefined label such as ''buyer type'' or ''purchase category.'' The goal is to discover hidden patterns---such as grouping customers by spending behavior, preferred products, or frequency of purchases. This is precisely what clustering algorithms are designed to do.
Clustering is commonly used in:
Customer segmentation for marketing analytics.
Market basket analysis to find associations in purchasing patterns.
Recommender systems to identify similar user profiles.
Anomaly detection when outliers deviate from natural clusters.
Typical algorithms for clustering include K-means, Hierarchical clustering, and DBSCAN. These models analyze multidimensional data to form clusters that maximize intra-group similarity and minimize inter-group similarity.
By contrast:
Classification (A) is a supervised learning method that predicts a categorical label (e.g., whether a customer will churn or not). It requires labeled training data.
Regression (B) is used to predict continuous numeric values (e.g., sales revenue, temperature).
Since the question focuses on discovering groups of similar customers without prior labels, the correct type of machine learning is Clustering.
You use natural language processing to process text from a Microsoft news story.
You receive the output shown in the following exhibit.
Which type of natural languages processing was performed?
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview
You can provide the Text Analytics service with unstructured text and it will return a list of entities in the text that it recognizes. You can provide the Text Analytics service with unstructured text and it will return a list of entities in the text that it recognizes. The service can also provide links to more information about that entity on the web. An entity is essentially an item of a particular type or a category; and in some cases, subtype, such as those as shown in the following table.
https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azure
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