Why use the latest Microsoft AI-900 dumps?

Compared to before 2025, the latest AI-900 exam has added content on generative AI, with recent actual exams showing it accounts for 20-25% of the content, truly reflecting the latest trends in the AI field. Additionally, deprecated Azure services (such as Anomaly Detector and Personalizer) have been removed, simplifying parts of the exam content. The question types and structure remain stable.

In summary, using the latest AI-900 dumps is very important.

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Latest AI-900 dumps practice questions:

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Microsoft Fundamentals	We share the latest version of exam practice questions

Question 1:

HOTSPOT

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

Hot Area:

Correct Answer:

Reference: https://www.cloudfactory.com/data-labeling-guide

Question 2:

When you design an AI system to assess whether loans should be approved, the factors used to make the decision should be explainable. This is an example of which Microsoft guiding principle for responsible AI?

A. transparency

B. inclusiveness

C. fairness

D. privacy and security

Correct Answer: A

Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the final model generated, and its associated assets. This information offers insights about how the model was created, which allows it to be reproduced in a transparent way.

Incorrect Answers:

B: Inclusiveness mandates that AI should consider all human races and experiences, and inclusive design practices can help developers to understand and address potential barriers that could unintentionally exclude people. Where possible, speech-to-text, text-to-speech, and visual recognition technology should be used to empower people with hearing, visual, and other impairments.

C: Fairness is a core ethical principle that all humans aim to understand and apply. This principle is even more important when AI systems are being developed. Key checks and balances need to make sure that the system\’s decisions don\’t discriminate or run a gender, race, sexual orientation, or religion bias toward a group or individual.

D: A data holder is obligated to protect the data in an AI system, and privacy and security are an integral part of this system. Personal needs to be secured, and it should be accessed in a way that doesn\’t compromise an individual\’s privacy.

Reference: https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/strategy/responsible-ai

Question 3:

HOTSPOT

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

Hot Area:

Correct Answer:

To perform real-time inferencing, you must deploy a pipeline as a real-time endpoint. Real-time endpoints must be deployed to an Azure Kubernetes Service cluster.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer#deploy

Question 4:

You have a webchat bot that provides responses from a QnA Maker knowledge base.

You need to ensure that the bot uses user feedback to improve the relevance of the responses over time.

What should you use?

A. key phrase extraction

B. sentiment analysis

C. business logic

D. active learning

Correct Answer: D

Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/how-to/improve-knowledge-base

Question 5:

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

A. sentiment analysis

B. speech recognition

C. key phrase extraction

D. entity recognition

Correct Answer: A

Sentiment analysis is analytical technique that uses statistics, natural language processing, and machine learning to determine the emotional meaning of communications. Companies use sentiment analysis to evaluate customer messages, call center interactions, online reviews, social media posts, and other content.

Reference: https://www.cio.com/article/189218/what-is-sentiment-analysis-using-nlp-and-ml-to-extract-meaning.html

Question 6:

You build a QnA Maker bot by using a frequently asked questions (FAQ) page.

You need to add professional greetings and other responses to make the bot more user friendly.

What should you do?

A. Increase the confidence threshold of responses

B. Enable active learning

C. Create multi-turn questions

D. Add chit-chat

Correct Answer: D

Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/how-to/chit-chat-knowledge-base?tabs=v1

Question 7:

Predicting how many vehicles will travel across a bridge on a give day is an example of _______. Select the answer that correctly completes the sentence.

A. regression

B. translation

C. classification

D. clustering

Correct Answer: A

Regression is a supervised machine learning technique used to predict numeric values.

Reference: https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/

Question 8:

You are authoring a Language Understanding (LUIS) application to support a music festival.

You want users to be able to ask questions about scheduled shows, such as: “Which act is playing on the main stage?”

The question “Which act is playing on the main stage?” is an example of which type of element?

A. an intent

B. an utterance

C. a domain

D. an entity

Correct Answer: B

Utterances are input from the user that your app needs to interpret.

Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/LUIS/luis-concept-utterance

Question 9:

HOTSPOT

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

Hot Area:

Correct Answer:

In the most basic sense, regression refers to prediction of a numeric target.

Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.

You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.

Incorrect Answers:

1.

Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data.

2.

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/algorithm-module-reference/linear-regression https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize-model-clustering

Question 10:

In which two scenarios can you use the Form Recognizer service? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

A. Extract the invoice number from an invoice.

B. Translate a form from French to English.

C. Find image of product in a catalog.

D. Identity the retailer from a receipt.

Correct Answer: AD

Reference: https://azure.microsoft.com/en-gb/services/cognitive-services/form-recognizer/#features

Question 11:

When training a model, why should you randomly split the rows into separate subsets?

A. to train the model twice to attain better accuracy

B. to train multiple models simultaneously to attain better performance

C. to test the model by using data that was not used to train the model

Correct Answer: C

The goal is to produce a trained (fitted) model that generalizes well to new, unknown data. The fitted model is evaluated using “new” examples from the held-out datasets (validation and test datasets) to estimate the model\’s accuracy in classifying new data.

https://en.wikipedia.org/wiki/Training,_validation,_and_test_sets#:~:text=Training %20datas et,A%20training%20datasetandtext=The%20goal%20is%20to%20produce,accuracy%20in%20classifying%20new%20data.

Question 12:

You need to predict the animal population of an area. Which Azure Machine Learning type should you use?

A. regression

B. clustering

C. classification

Correct Answer: A

Regression is a supervised machine learning technique used to predict numeric values.

Reference: https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/1-introduction

Question 13:

What are two tasks that can be performed by using computer vision? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

A. Predict stock prices.

B. Detect brands in an image.

C. Detect the color scheme in an image

D. Translate text between languages.

E. Extract key phrases.

Correct Answer: BC

B: Azure\’s Computer Vision service gives you access to advanced algorithms that process images and return information based on the visual features you\’re interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.

E: Computer Vision includes Optical Character Recognition (OCR) capabilities. You can use the new Read API to extract printed and handwritten text from images and documents. It uses the latest models and works with text on a variety of surfaces and backgrounds. These include receipts, posters, business cards, letters, and whiteboards. The two OCR APIs support extracting printed text in several languages.

Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview

Question 14:

DRAG DROP

Match the Azure Cognitive Services service to the appropriate actions. To answer, drag the appropriate service from the column on the left to its action on the right. Each service may he used once, more than once, or not at all.

NOTE: Each correct match is worth one point.

Select and Place:

Correct Answer:

Box 1: Speech

Custom Speech: Code-free automated machine learning for speech recognition

Speech to text is a Speech service feature that accurately transcribes spoken audio to text.

Make spoken audio actionable

Quickly and accurately transcribe audio to text in more than 100 languages and variants. Customize models to enhance accuracy for domain-specific terminology. Get more value from spoken audio by enabling search or analytics on transcribed text or facilitating action—all in your preferred programming language.

Box 2: Language service

Add intents to your LUIS app to identify groups of questions or commands that have the same intention.

Note: Language understanding (LU) is a very centric component to enable conversational services such as bots, IoT experiences, analytics, and others.

In a spoken dialog system, LU converts from the words in a sentence into a machine-readable meaning representation, typically indicating the intent of the sentence and any present entities.

For example, consider a physical fitness domain, with a dialog system embedded in a wearable device like a watch. This dialog system could recognize intents like StartActivity and StopActivity, and could recognize entities like ActivityType.

In the user input “begin a jog”, the goal of LU is to identify the intent as StartActivity, and identify the entity ActivityType= ’’jog’’.

Box 3: Language service

Intent compared to entity

The intent represents the action the application should take for the user, based on the entire utterance. An utterance can have only one top-scoring intent, but it can have many entities.

Create an intent when the user\’s intention would trigger an action in your client application, like a call to the checkweather() function from the table above. Then create entities to represent parameters required to execute the action.

Reference: https://azure.microsoft.com/en-us/services/cognitive-services/speech-to-text

LUIS.AI: Automated Machine Learning for Custom Language Understanding

https://docs.microsoft.com/en-us/azure/cognitive-services/luis/concepts/intents

Question 15:

You need to develop a mobile app for employees to scan and store their expenses while travelling. Which type of computer vision should you use?

A. semantic segmentation

B. image classification

C. object detection

D. optical character recognition (OCR)

Correct Answer: D

Azure\’s Computer Vision API includes Optical Character Recognition (OCR) capabilities that extract printed or handwritten text from images. You can extract text from images, such as photos of license plates or containers with serial numbers, as well as from documents – invoices, bills, financial reports, articles, and more.

Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-recognizing-text

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