Latest Apr-2026 Microsoft AI-900 Dumps Updated 325 Questions [Q151-Q172]

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Latest Apr-2026 Microsoft AI-900 Dumps Updated 325 Questions

PDF Download Free of AI-900 Valid Practice Test Questions

NEW QUESTION 151
An app that analyzes social media posts to identify their tone is an example of which type of natural language processing (NLP) workload?

sentiment analysis

key phrase extraction

entity recognition

speech recognition

NEW QUESTION 152
You have the process shown in the following exhibit.

Which type AI solution is shown in the diagram?

a sentiment analysis solution

a chatbot

a machine learning model

a computer vision application

NEW QUESTION 153
To complete the sentence, select the appropriate option in the answer area.

NEW QUESTION 154
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

NEW QUESTION 155
You use Azure Machine Learning designer to publish an inference pipeline.
Which two parameters should you use to consume the pipeline? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

the model name

the training endpoint

the authentication key

the REST endpoint

NEW QUESTION 156
For a machine learning progress, how should you split data for training and evaluation?

Use features for training and labels for evaluation.

Randomly split the data into rows for training and rows for evaluation.

Use labels for training and features for evaluation.

Randomly split the data into columns for training and columns for evaluation.

https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/split-data The correct answer is B. Randomly split the data into rows for training and rows for evaluation.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module “Describe fundamental principles of machine learning on Azure”, the process of developing a machine learning model involves dividing the available dataset into two or more parts-commonly training data and evaluation (or testing) data. The goal is to ensure that the model can learn patterns from one subset of the data (training set) and then be objectively tested on unseen data (evaluation set) to measure how well it generalizes to new situations.
The training dataset contains both features (the measurable inputs) and labels (the target outputs). The model learns from the patterns and relationships between these features and labels. The evaluation dataset also contains features and labels, but it is kept separate during the training phase. Once the model has been trained, it is tested on this unseen evaluation data to calculate metrics like accuracy, precision, recall, or F1 score.
Microsoft emphasizes that the data split should be random and based on rows, not columns. Each row represents a complete observation (for example, one customer record, one transaction, or one image).
Randomly splitting ensures that both subsets represent the same distribution of data, avoiding bias. Splitting by columns would separate features themselves, which would make the model training invalid.
The AI-900 materials often illustrate this using Azure Machine Learning’s data preparation workflow, where data is randomly divided (commonly 70% for training and 30% for testing). This ensures the model learns from diverse examples and is fairly evaluated.
Therefore, the verified and correct approach, as per Microsoft’s official guidance, is B. Randomly split the data into rows for training and rows for evaluation.

NEW QUESTION 157
What is a use case for classification?

predicting how many cups of coffee a person will drink based on how many hours the person slept the previous night.

analyzing the contents of images and grouping images that have similar colors

predicting whether someone uses a bicycle to travel to work based on the distance from home to work

predicting how many minutes it will take someone to run a race based on past race times

NEW QUESTION 158
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

NEW QUESTION 159
Select the answer that correctly completes the sentence.

NEW QUESTION 160
For which two workloads can you use computer vision? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

creating photorealistic images by using three-dimensional models

assigning the color pixels in an image to object names

describing the contents of an image

detecting inconsistencies and anomalies in a stream of data

creating visual representations of numerical data

NEW QUESTION 161
Match the types of machine learning to the appropriate scenarios.
To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each machine learning type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

NEW QUESTION 162
To complete the sentence, select the appropriate option in the answer area.

NEW QUESTION 163
To complete the sentence, select the appropriate option in the answer area.

NEW QUESTION 164
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

NEW QUESTION 165
You plan to deploy an Azure Machine Learning model as a service that will be used by client applications.
Which three processes should you perform in sequence before you deploy the model? To answer, move the appropriate processes from the list of processes to the answer area and arrange them in the correct order.

NEW QUESTION 166
To complete the sentence, select the appropriate option in the answer area.

NEW QUESTION 167
Select the answer that correctly completes the sentence.

NEW QUESTION 168
To complete the sentence, select the appropriate option in the answer area.

NEW QUESTION 169
To complete the sentence, select the appropriate option in the answer area.

NEW QUESTION 170
A smart device that responds to the question. “What is the stock price of Contoso, Ltd.?” is an example of which Al workload?

computer vision

anomaly detection

knowledge mining

natural language processing

NEW QUESTION 171
Select the answer that correctly completes the sentence.

NEW QUESTION 172
For each of the following statements, select Yes if the statement is True. Otherwise, select No. NOTE: Each correct selection is worth one point.

Exam AI-900: Microsoft Azure AI Fundamentals

Candidates for this exam should have foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.

This exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.

This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, some general programming knowledge or experience would be beneficial.

Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it’s not a prerequisite for any of them.

Part of the requirements for: Microsoft Certified: Azure AI Fundamentals

Download exam skills outline

AI-900 Test Engine files, AI-900 Dumps PDF: https://www.prepawaytest.com/Microsoft/AI-900-practice-exam-dumps.html