AI-900 Exam Info and Free Practice Test All-in-One Exam Guide Oct-2025 [Q150-Q169]

AI-900 Exam Info and Free Practice Test All-in-One Exam Guide Oct-2025

Pass Microsoft AI-900 Actual Free Exam Q&As Updated Dump Oct 14, 2025

Microsoft AI-900, also known as Microsoft Azure AI Fundamentals, is an entry-level certification exam for individuals who are interested in working with artificial intelligence (AI) and machine learning (ML) technologies. Microsoft Azure AI Fundamentals certification is designed to provide a foundational understanding of AI and its capabilities, and how it can be leveraged to solve business problems. AI-900 exam covers a range of topics including AI workloads, fundamental principles of machine learning, and computer vision.

NEW QUESTION # 150
To complete the sentence, select the appropriate option in the answer area.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/home custom vision - This is a type of computer vision service which helps in building/training models using user provided data Creating an object detection solution with Custom Vision consists of three main tasks. First you must use upload and tag images, then you can train the model, and finally you must publish the model so that client applications can use it to generate predictions.
https://docs.microsoft.com/en-us/learn/modules/detect-objects-images-custom-vision/2-object-detection-azure

NEW QUESTION # 151
Match the facial recognition tasks to the appropriate questions.
To answer, drag the appropriate task from the column on the left to its question on the right. Each task may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: verification
Face verification: Check the likelihood that two faces belong to the same person and receive a confidence score.
Box 2: similarity
Box 3: Grouping
Box 4: identification
Face detection: Detect one or more human faces along with attributes such as: age, emotion, pose, smile, and facial hair, including 27 landmarks for each face in the image.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/face/#features

NEW QUESTION # 152
Which type of machine learning should you use to predict the number of gift cards that will be sold next month?

A. classification
B. clustering
C. regression

Answer: B

Explanation:
Section: Describe fundamental principles of machine learning on Azure
Explanation:
Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation.
Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning- initialize-model-clustering

NEW QUESTION # 153
You have an Azure Machine Learning model that uses clinical data to predict whether a patient has a disease.
You clean and transform the clinical data.
You need to ensure that the accuracy of the model can be proven.
What should you do next?

A. Train the model by using the clinical data.
B. Train the model by using automated machine learning (automated ML).
C. Validate the model by using the clinical data.
D. Split the clinical data into Two datasets.

Answer: C

NEW QUESTION # 154
Select the answer that correctly completes the sentence.

Answer:

Explanation:
Explanation

NEW QUESTION # 155
To complete the sentence, select the appropriate option in the answer area.

Answer:

Explanation:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-science-process/create-features

NEW QUESTION # 156
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

NEW QUESTION # 157
You plan to apply Text Analytics API features to a technical support ticketing system.
Match the Text Analytics API features to the appropriate natural language processing scenarios.
To answer, drag the appropriate feature from the column on the left to its scenario on the right. Each feature may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box1: Sentiment analysis
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Box 2: Broad entity extraction
Broad entity extraction: Identify important concepts in text, including key Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.
Box 3: Entity Recognition
Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics

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.

Answer:

Explanation:

Reference:
https://azure.microsoft.com/es-es/blog/machine-assisted-text-classification-on-content-moderator-public-preview/
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing

NEW QUESTION # 159
Match the types of AI workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/learn/paths/get-started-with-artificial-intelligence-on-azure/

NEW QUESTION # 160
Select the answer that correctly completes the sentence

Answer:

Explanation:

NEW QUESTION # 161
To complete the sentence, select the appropriate option in the answer area.
Using Recency, Frequency, and Monetary (RFM) values to identify segments of a customer base is an example of___________

Answer:

Explanation:

NEW QUESTION # 162
You need to create a training dataset and validation dataset from an existing dataset.
Which module in the Azure Machine Learning designer should you use?

A. Add Rows
B. Split Data
C. Select Columns in Dataset
D. Join Data

Answer: B

Explanation:
A common way of evaluating a model is to divide the data into a training and test set by using Split Data, and then validate the model on the training data.
Use the Split Data module to divide a dataset into two distinct sets.
The studio currently supports training/validation data splits
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-cross-validation-data-splits2

NEW QUESTION # 163
Select the answer that correctly completes the sentence

Answer:

Explanation:

Explanation:

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.

Answer:

Explanation:

Explanation:
The Text Analytics API is a cloud-based service that provides advanced natural language processing over raw text, and includes four main functions: sentiment analysis, key phrase extraction, named entity recognition, and language detection.
Box 1: Yes
You can detect which language the input text is written in and report a single language code for every document submitted on the request in a wide range of languages, variants, dialects, and some regional/cultural languages. The language code is paired with a score indicating the strength of the score.
Box 2: No
Box 3: Yes
Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview

NEW QUESTION # 165
Select the answer that correctly completes the sentence.

Answer:

Explanation:

NEW QUESTION # 166
Select the answer that correctly completes the sentence

Answer:

Explanation:

NEW QUESTION # 167
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.