Latest AI-900 Pass Guaranteed Exam Dumps Certification Sample Questions [Q35-Q57]

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Latest AI-900 Pass Guaranteed Exam Dumps Certification Sample Questions

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Exam AI-900: Microsoft Azure AI Fundamentals

The content of this exam was updated on April 23, 2021.

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

Describe NLP Workloads Features on Azure (15-20%)

This domain contains the following details that you need to learn about:

Identify the features of basic NLP (Natural Language Processing) Workload Scenarios – The individuals should be able to identify various uses and features of various components, for example, keyphrase extraction, sentiment analysis, entity recognition, translation, language modeling, and speech recognition & synthesis.
Identify Azure services & tools for Natural Language Processing Workloads – This topic is created to equip you with the ability to identify various capabilities, such as Speech service, Text Analytics service, Translator Text service, and Language Understanding service.

QUESTION 35
To complete the sentence, select the appropriate option in the answer area.

QUESTION 36
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.

QUESTION 37
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

QUESTION 38
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.

Predict stock prices.

Detect brands in an image.

Detect the color scheme in an image

Translate text between languages.

Extract key phrases.

QUESTION 39
To complete the sentence, select the appropriate option in the answer area.

QUESTION 40
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?

entity recognition

key phrase extraction

sentiment analysis

translation

QUESTION 41
Select the answer that correctly completes the sentence.

QUESTION 42
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

QUESTION 43
You have the Predicted vs. True chart shown in the following exhibit.

Which type of model is the chart used to evaluate?

classification

regression

clustering

QUESTION 44
When training a model, why should you randomly split the rows into separate subsets?

to train the model twice to attain better accuracy

to train multiple models simultaneously to attain better performance

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

QUESTION 45
In which two scenarios can you use speech recognition? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

an in-car system that reads text messages aloud

providing closed captions for recorded or live videos

creating an automated public address system for a train station

creating a transcript of a telephone call or meeting

QUESTION 46
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

QUESTION 47
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

QUESTION 48
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

QUESTION 49
You are developing a solution that uses the Text Analytics service.
You need to identify the main talking points in a collection of documents.
Which type of natural language processing should you use?

entity recognition

key phrase extraction

sentiment analysis

language detection

QUESTION 50
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

QUESTION 51
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

QUESTION 52
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?

entity recognition

key phrase extraction

sentiment analysis

translation

QUESTION 53
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

QUESTION 54
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

QUESTION 55
Which metric can you use to evaluate a classification model?

true positive rate

mean absolute error (MAE)

coefficient of determination (R2)

root mean squared error (RMSE)

QUESTION 56
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.

QUESTION 57
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.