Practice Test Free
  • QUESTIONS
  • COURSES
    • CCNA
    • Cisco Enterprise Core
    • VMware vSphere: Install, Configure, Manage
  • CERTIFICATES
No Result
View All Result
  • Login
  • Register
Quesions Library
  • Cisco
    • 200-301
    • 200-901
      • Multiple Choice
      • Drag Drop
    • 350-401
      • Multiple Choice
      • Drag Drop
    • 350-701
    • 300-410
      • Multiple Choice
      • Drag Drop
    • 300-415
      • Multiple Choice
      • Drag Drop
    • 300-425
    • Others
  • AWS
    • CLF-C02
    • SAA-C03
    • SAP-C02
    • ANS-C01
    • Others
  • Microsoft
    • AZ-104
    • AZ-204
    • AZ-305
    • AZ-900
    • AI-900
    • SC-900
    • Others
  • CompTIA
    • SY0-601
    • N10-008
    • 220-1101
    • 220-1102
    • Others
  • Google
    • Associate Cloud Engineer
    • Professional Cloud Architect
    • Professional Cloud DevOps Engineer
    • Others
  • ISACA
    • CISM
    • CRIS
    • Others
  • LPI
    • 101-500
    • 102-500
    • 201-450
    • 202-450
  • Fortinet
    • NSE4_FGT-7.2
  • VMware
  • >>
    • Juniper
    • EC-Council
      • 312-50v12
    • ISC
      • CISSP
    • PMI
      • PMP
    • Palo Alto Networks
    • RedHat
    • Oracle
    • GIAC
    • F5
    • ITILF
    • Salesforce
Contribute
Practice Test Free
  • QUESTIONS
  • COURSES
    • CCNA
    • Cisco Enterprise Core
    • VMware vSphere: Install, Configure, Manage
  • CERTIFICATES
No Result
View All Result
Practice Test Free
No Result
View All Result
Home Practice Test Free

AI-900 Practice Test Free

Table of Contents

Toggle
  • AI-900 Practice Test Free – 50 Real Exam Questions to Boost Your Confidence
  • Free Access Full AI-900 Practice Test Free Questions

AI-900 Practice Test Free – 50 Real Exam Questions to Boost Your Confidence

Preparing for the AI-900 exam? Start with our AI-900 Practice Test Free – a set of 50 high-quality, exam-style questions crafted to help you assess your knowledge and improve your chances of passing on the first try.

Taking a AI-900 practice test free is one of the smartest ways to:

  • Get familiar with the real exam format and question types
  • Evaluate your strengths and spot knowledge gaps
  • Gain the confidence you need to succeed on exam day

Below, you will find 50 free AI-900 practice questions to help you prepare for the exam. These questions are designed to reflect the real exam structure and difficulty level. You can click on each Question to explore the details.

Question 1

HOTSPOT -
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Hot Area:
 Image

 


Suggested Answer:
Correct Answer Image

Box 1: Yes –
For regression problems, the label column must contain numeric data that represents the response variable. Ideally the numeric data represents a continuous scale.
Box 2: No –
K-Means Clustering –
Because the K-means algorithm is an unsupervised learning method, a label column is optional.
If your data includes a label, you can use the label values to guide selection of the clusters and optimize the model.
If your data has no label, the algorithm creates clusters representing possible categories, based solely on the data.
Box 3: No –
For classification problems, the label column must contain either categorical values or discrete values. Some examples might be a yes/no rating, a disease classification code or name, or an income group. If you pick a noncategorical column, the component will return an error during training.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/train-model
https://docs.microsoft.com/en-us/azure/machine-learning/component-reference/k-means-clustering

Question 2

HOTSPOT -
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Hot Area:
 Image

 


Suggested Answer:
Correct Answer Image

Box 1: Yes –
In machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict.
In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing.
Box 2: No –
Box 3: No –
Accuracy is simply the proportion of correctly classified instances. It is usually the first metric you look at when evaluating a classifier. However, when the test data is unbalanced (where most of the instances belong to one of the classes), or you are more interested in the performance on either one of the classes, accuracy doesn’t really capture the effectiveness of a classifier.
Reference:
https://www.cloudfactory.com/data-labeling-guide

https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance

Question 3

You need to convert handwritten notes into digital text.
Which type of computer vision should you use?

A. facial detection

B. optical character recognition (OCR)

C. image classification

D. object detection

 


Suggested Answer: B

 

Question 4

You use natural language processing to process text from a Microsoft news story.
You receive the output shown in the following exhibit.
 Image
Which type of natural languages processing was performed?

A. entity recognition

B. key phrase extraction

C. sentiment analysis

D. translation

 


Suggested Answer: A

Named Entity Recognition (NER) is the ability to identify different entities in text and categorize them into pre-defined classes or types such as: person, location, event, product, and organization.
In this question, the square brackets indicate the entities such as DateTime, PersonType, Skill.
Reference:
https://docs.microsoft.com/en-in/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-entity-linking?tabs=version-3-preview

Question 5

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

 


Suggested Answer:
Correct Answer Image

 

Question 6

DRAG DROP -
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.
Select and Place:
 Image

 


Suggested Answer:
Correct Answer Image

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

Question 7

HOTSPOT
-
Select the answer that correctly completes the sentence.
 Image

 


Suggested Answer:
Correct Answer Image

 

Question 8

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. Identify the retailer from a receipt

B. Translate from French to English

C. Extract the invoice number from an invoice

D. Find images of products in a catalog

 


Suggested Answer: AC

Reference:
https://docs.microsoft.com/en-us/azure/applied-ai-services/form-recognizer/overview?tabs=v2-1

Question 9

DRAG DROP
-
You plan to deploy an Azure Machine Learning model by using the Machine Learning designer.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
 Image

 


Suggested Answer:
Correct Answer Image

 

Question 10

In a machine learning model, the data that is used as inputs are called ________.
Select the answer that correctly completes the sentence.

A. dataset

B. labels

C. variables

 


Suggested Answer: B

 

Question 11

You plan to build a conversational AI solution that can be surfaced in Microsoft Teams, Microsoft Cortana, and Amazon Alexa.
Which service should you use?

A. Azure Bot Service

B. Azure Cognitive Search

C. Speech

D. Language service

 


Suggested Answer: A

 

Question 12

You need to predict the income range of a given customer by using the following dataset.
 Image
Which two fields should you use as features? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. Education Level

B. Last Name

C. Age

D. Income Range

E. First Name

 


Suggested Answer: AC

First Name, Last Name, Age and Education Level are features. Income range is a label (what you want to predict). First Name and Last Name are irrelevant in that they have no bearing on income. Age and Education level are the features you should use.

Question 13

You need to create a model that labels a collection of your personal digital photographs.
Which Azure Cognitive Services service should you use?

A. Form Recognizer

B. Custom Vision

C. Language

D. Computer Vision

 


Suggested Answer: D

 

Question 14

HOTSPOT -
Select the answer that correctly completes the sentence.
Hot Area:
 Image

 


Suggested Answer:
Correct Answer Image

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

Question 15

DRAG DROP
-
You are designing a system that will generate insurance quotes automatically.
Match the Microsoft responsible AI principles to the appropriate requirements.
To answer, drag the appropriate principle from the column on the left to its requirement on the right. Each principle may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
 Image

 


Suggested Answer:
Correct Answer Image

 

Question 16

HOTSPOT -
To complete the sentence, select the appropriate option in the answer area.
Hot Area:
 Image

 


Suggested Answer:
Correct Answer Image

Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing

Question 17

You have an Azure Machine Learning pipeline that contains a Split Data module.
The Split Data module outputs to a Train Model module and a Score Model module.
What is the function of the Split Data module?

A. scaling numeric variables so that they are within a consistent numeric range

B. creating training and validation datasets

C. diverting records that have missing data

D. selecting columns that must be included in the model

 


Suggested Answer: B

 

Question 18

HOTSPOT -
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Hot Area:
 Image

 


Suggested Answer:
Correct Answer Image

Reference:
https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/ai/conversational-bot
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-webchat-overview?view=azure-bot-service-4.0

Question 19

Which Computer Vision feature can you use to generate automatic captions for digital photographs?

A. Recognize text.

B. Identify the areas of interest.

C. Detect objects.

D. Describe the images.

 


Suggested Answer: D

Describe images with human-readable language
Computer Vision can analyze an image and generate a human-readable phrase that describes its contents. The algorithm returns several descriptions based on different visual features, and each description is given a confidence score. The final output is a list of descriptions ordered from highest to lowest confidence.
The image description feature is part of the Analyze Image API.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-describing-images

Question 20

You are developing a natural language processing solution in Azure. The solution will analyze customer reviews and determine how positive or negative each review is.
This is an example of which type of natural language processing workload?

A. language detection

B. sentiment analysis

C. key phrase extraction

D. entity recognition

 


Suggested Answer: B

Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing

Question 21

DRAG DROP -
Match the machine learning tasks to the appropriate scenarios.
To answer, drag the appropriate task from the column on the left to its scenario on the right. Each task may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Select and Place:
 Image

 


Suggested Answer:
Correct Answer Image

Box 1: Model evaluation –
The Model evaluation module outputs a confusion matrix showing the number of true positives, false negatives, false positives, and true negatives, as well as
ROC, Precision/Recall, and Lift curves.
Box 2: Feature engineering –
Feature engineering is the process of using domain knowledge of the data to create features that help ML algorithms learn better. In Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature engineering. Collectively, these techniques and feature engineering are referred to as featurization.
Note: Often, features are created from raw data through a process of feature engineering. For example, a time stamp in itself might not be useful for modeling until the information is transformed into units of days, months, or categories that are relevant to the problem, such as holiday versus working day.
Box 3: Feature selection –
In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance
https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml

Question 22

HOTSPOT -
To complete the sentence, select the appropriate option in the answer area.
Hot Area:
 Image

 


Suggested Answer:
Correct Answer Image

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

Question 23

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

 


Suggested 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 24

DRAG DROP -
You need to scan the news for articles about your customers and alert employees when there is a negative article. Positive articles must be added to a press book.
Which natural language processing tasks should you use to complete the process? To answer, drag the appropriate tasks to the correct locations. Each task may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Select and Place:
 Image

 


Suggested Answer:
Correct Answer Image

Box 1: Entity recognition –
the Named Entity Recognition module in Machine Learning Studio (classic), to identify the names of things, such as people, companies, or locations in a column of text.
Named entity recognition is an important area of research in machine learning and natural language processing (NLP), because it can be used to answer many real-world questions, such as:
✑ Which companies were mentioned in a news article?
✑ Does a tweet contain the name of a person? Does the tweet also provide his current location?
✑ Were specified products mentioned in complaints or reviews?
Box 2: Sentiment Analysis –
The Text Analytics API’s Sentiment Analysis feature provides two ways for detecting positive and negative sentiment. If you send a Sentiment Analysis request, the API will return sentiment labels (such as “negative”, “neutral” and “positive”) and confidence scores at the sentence and document-level.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/named-entity-recognition
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-sentiment-analysis

Question 25

DRAG DROP -
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.
Select and Place:
 Image

 


Suggested Answer:
Correct Answer Image

Box 1: Knowledge mining –
You can use Azure Cognitive Search’s knowledge mining results and populate your knowledge base of your chatbot.
Box 2: Computer vision –
Box 3: Natural language processing
Natural language processing (NLP) is used for tasks such as sentiment analysis.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing

Question 26

You have 100 instructional videos that do NOT contain any audio. Each instructional video has a script.
You need to generate a narration audio file for each video based on the script.
Which type of workload should you use?

A. language modeling

B. speech recognition

C. speech synthesis

D. translation

 


Suggested Answer: C

 

Question 27

Your company manufactures widgets.
You have 1,000 digital photos of the widgets.
You need to identify the location of the widgets within the photos.
What should you use?

A. Computer Vision Spatial Analysis

B. Custom Vision object detection

C. Computer Vision Image Analysis

D. Custom Vision classification

 


Suggested Answer: B

 

Question 28

A smart device that responds to the question “What is the stock price of Contoso. Ltd.?” is an example of which AI workload?

A. knowledge mining

B. natural language processing

C. computer vision

D. anomaly detection

 


Suggested Answer: A

 

Question 29

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)

 


Suggested 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

Question 30

Which two languages can you use to write custom code for Azure Machine Learning designer? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. Python

B. R

C. C#

D. Scala

 


Suggested Answer: AB

Use Azure Machine Learning designer for customizing using Python and R code.
Reference:
https://azure.microsoft.com/en-us/services/machine-learning/designer/#features

Question 31

You need to predict the sea level in meters for the next 10 years.
Which type of machine learning should you use?

A. classification

B. regression

C. clustering

 


Suggested Answer: B

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.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regression

Question 32

HOTSPOT -
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Hot Area:
 Image

 


Suggested Answer:
Correct Answer Image

Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-service-overview-introduction?view=azure-bot-service-4.0

Question 33

DRAG DROP -
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.
Select and Place:
 Image

 


Suggested Answer:
Correct Answer Image

Box 1: Image classification –
Image classification is a supervised learning problem: define a set of target classes (objects to identify in images), and train a model to recognize them using labeled example photos.
Box 2: Object detection –
Object detection is a computer vision problem. While closely related to image classification, object detection performs image classification at a more granular scale. Object detection both locates and categorizes entities within images.
Box 3: Semantic Segmentation –
Semantic segmentation achieves fine-grained inference by making dense predictions inferring labels for every pixel, so that each pixel is labeled with the class of its enclosing object ore region.
Reference:
https://developers.google.com/machine-learning/practica/image-classification
https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/object-detection-model-builder
https://nanonets.com/blog/how-to-do-semantic-segmentation-using-deep-learning/

Question 34

Which two actions are performed during the data ingestion and data preparation stage of an Azure Machine Learning process? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. Calculate the accuracy of the model.

B. Score test data by using the model.

C. Combine multiple datasets.

D. Use the model for real-time predictions.

E. Remove records that have missing values.

 


Suggested Answer: CE

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-data-ingestion
https://docs.microsoft.com/en-us/azure/architecture/data-science-process/prepare-data

Question 35

You need to build an app that will read recipe instructions aloud to support users who have reduced vision.
Which version service should you use?

A. Text Analytics

B. Translator

C. Speech

D. Language Understanding (LUIS)

 


Suggested Answer: C

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

Question 36

In which two scenarios can you use a speech synthesis solution? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. an automated voice that reads back a credit card number entered into a telephone by using a numeric keypad

B. generating live captions for a news broadcast

C. extracting key phrases from the audio recording of a meeting

D. an AI character in a computer game that speaks audibly to a player

 


Suggested Answer: AD

Azure Text to Speech is a Speech service feature that converts text to lifelike speech.
Incorrect Answers:
C: Extracting key phrases is not speech synthesis.
Reference:
https://azure.microsoft.com/en-in/services/cognitive-services/text-to-speech/

Question 37

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

A. Use features for training and labels for evaluation.

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

C. Use labels for training and features for evaluation.

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

 


Suggested Answer: B

The Split Data module is particularly useful when you need to separate data into training and testing sets. Use the Split Rows option if you want to divide the data into two parts. You can specify the percentage of data to put in each split, but by default, the data is divided 50-50. You can also randomize the selection of rows in each group, and use stratified sampling.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/split-data

Question 38

You are designing an AI system that empowers everyone, including people who have hearing, visual, and other impairments.
This is an example of which Microsoft guiding principle for responsible AI?

A. fairness

B. inclusiveness

C. reliability and safety

D. accountability

 


Suggested Answer: B

Inclusiveness: At Microsoft, we firmly believe everyone should benefit from intelligent technology, meaning it must incorporate and address a broad range of human needs and experiences. For the 1 billion people with disabilities around the world, AI technologies can be a game-changer.
Reference:
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles

Question 39

Which AI service can you use to interpret the meaning of a user input such as `Call me back later?`

A. Translator

B. Text Analytics

C. Speech

D. Language Understanding (LUIS)

 


Suggested Answer: D

Language Understanding (LUIS) is a cloud-based AI service, that applies custom machine-learning intelligence to a user’s conversational, natural language text to predict overall meaning, and pull out relevant, detailed information.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/what-is-luis

Question 40

You have a solution that reads manuscripts in different languages and categorizes the manuscripts based on topic.
Which types of natural language processing (NLP) workloads does the solution use?

A. speech recognition and entity recognition

B. speech recognition and language modeling

C. translation and key phrase extraction

D. translation and sentiment analysis

 


Suggested Answer: C

 

Question 41

HOTSPOT
-
Select the answer that correctly completes the sentence.
 Image

 


Suggested Answer:
Correct Answer Image

 

Question 42

HOTSPOT -
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Hot Area:
 Image

 


Suggested Answer:
Correct Answer Image

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-designer-python
https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml

Question 43

HOTSPOT
-
Select the answer that correctly completes the sentence.
 Image

 


Suggested Answer:
Correct Answer Image

 

Question 44

HOTSPOT -
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Hot Area:
 Image

 


Suggested Answer:
Correct Answer Image

Box 1: Yes –
Azure bot service can be integrated with the powerful AI capabilities with Azure Cognitive Services.
Box 2: Yes –
Azure bot service engages with customers in a conversational manner.
Box 3: No –
The QnA Maker service creates knowledge base, not question and answers sets.
Note: You can use the QnA Maker service and a knowledge base to add question-and-answer support to your bot. When you create your knowledge base, you seed it with questions and answers.
Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-tutorial-add-qna

Question 45

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

 


Suggested Answer:
Correct Answer Image

 

Question 46

You have the process shown in the following exhibit.
 Image
Which type of AI solution is shown in the diagram?

A. a sentiment analysis solution

B. a chatbot

C. a machine learning model

D. a computer vision application

 


Suggested Answer: B

 

Question 47

Which machine learning technique can be used for anomaly detection?

A. A machine learning technique that classifies objects based on user supplied images.

B. A machine learning technique that understands written and spoken language.

C. A machine learning technique that classifies images based on their contents.

D. A machine learning technique that analyzes data over time and identifies unusual changes.

 


Suggested Answer: D

 

Question 48

DRAG DROP -
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.
Select and Place:
 Image

 


Suggested Answer:
Correct Answer Image

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines

Question 49

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

 


Suggested Answer:
Correct Answer Image

 

Question 50

Which scenario is an example of a webchat bot?

A. Determine whether reviews entered on a website for a concert are positive or negative, and then add a thumbs up or thumbs down emoji to the reviews.

B. Translate into English questions entered by customers at a kiosk so that the appropriate person can call the customers back.

C. Accept questions through email, and then route the email messages to the correct person based on the content of the message.

D. From a website interface, answer common questions about scheduled events and ticket purchases for a music festival.

 


Suggested Answer: D

 

Free Access Full AI-900 Practice Test Free Questions

If you’re looking for more AI-900 practice test free questions, click here to access the full AI-900 practice test.

We regularly update this page with new practice questions, so be sure to check back frequently.

Good luck with your AI-900 certification journey!

Share18Tweet11
Previous Post

AI-102 Practice Test Free

Next Post

ANS-C00 Practice Test Free

Next Post

ANS-C00 Practice Test Free

ANS-C01 Practice Test Free

AXS-C01 Practice Test Free

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Recommended

Network+ Practice Test

Comptia Security+ Practice Test

A+ Certification Practice Test

Aws Cloud Practitioner Exam Questions

Aws Cloud Practitioner Practice Exam

Comptia A+ Practice Test

  • About
  • DMCA
  • Privacy & Policy
  • Contact

PracticeTestFree.com materials do not contain actual questions and answers from Cisco's Certification Exams. PracticeTestFree.com doesn't offer Real Microsoft Exam Questions. PracticeTestFree.com doesn't offer Real Amazon Exam Questions.

  • Login
  • Sign Up
No Result
View All Result
  • Quesions
    • Cisco
    • AWS
    • Microsoft
    • CompTIA
    • Google
    • ISACA
    • ECCouncil
    • F5
    • GIAC
    • ISC
    • Juniper
    • LPI
    • Oracle
    • Palo Alto Networks
    • PMI
    • RedHat
    • Salesforce
    • VMware
  • Courses
    • CCNA
    • ENCOR
    • VMware vSphere
  • Certificates

Welcome Back!

Login to your account below

Forgotten Password? Sign Up

Create New Account!

Fill the forms below to register

All fields are required. Log In

Retrieve your password

Please enter your username or email address to reset your password.

Log In

Insert/edit link

Enter the destination URL

Or link to existing content

    No search term specified. Showing recent items. Search or use up and down arrow keys to select an item.