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VCEPrep AI-900 Dumps Real Exam Questions Test Engine Dumps Training [Q57-Q72]

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You can read the Best Solution to prepare AI-900: Microsoft Azure AI Fundamentals Exam

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The AI-900 exam covers a wide range of topics related to AI and ML such as Understanding AI workloads and considerations, Understanding fundamental principles of machine learning on Azure, Understanding features of computer vision workloads on Azure, Understanding features of Natural Language Processing (NLP) workloads on Azure, Understanding features of conversational AI workloads on Azure, and many more.

QUESTION 57
Select the answer that correctly completes the sentence.

QUESTION 58
To complete the sentence, select the appropriate option in the answer area.

Explanation

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 59
You are developing a conversational AI solution that will communicate with users through multiple channels including email, Microsoft Teams, and webchat.
Which service should you use?

 Text Analytics

 Azure Bot Service

 Translator

 Form Recognizer

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

QUESTION 60
To complete the sentence, select the appropriate option in the answer area.
Computer vision capabilities can be Deployed to………………..

seetheanswerinbelowExplanation
Explanation:
Integrate a facial recognition feature into an app.

QUESTION 61
You need to build an app that will identify celebrities in images.
Which service should you use?

 Azure OpenAI Service

 Azure Machine Learning

 conversational language understanding (CLU)

 Azure Al Vision

QUESTION 62
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Explanation

Box 1: Yes
Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.
Box 2: No
Box 3: Yes
During training, Azure Machine Learning creates a number of pipelines in parallel that try different algorithms and parameters for you. The service iterates through ML algorithms paired with feature selections, where each iteration produces a model with a training score. The higher the score, the better the model is considered to
“fit” your data. It will stop once it hits the exit criteria defined in the experiment.
Box 4: No
Apply automated ML when you want Azure Machine Learning to train and tune a model for you using the target metric you specify.
The label is the column you want to predict.
Reference:
https://azure.microsoft.com/en-us/services/machine-learning/automatedml/#features

QUESTION 63
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

QUESTION 64
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Explanation

Box 1: No
Box 2: Yes
Box 3: Yes
Anomaly detection encompasses many important tasks in machine learning:
Identifying transactions that are potentially fraudulent.
Learning patterns that indicate that a network intrusion has occurred.
Finding abnormal clusters of patients.
Checking values entered into a system.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/anomaly-detection

QUESTION 65
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.

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

QUESTION 66
Which two scenarios are examples of a conversational AI workload? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

 a telephone answering service that has a pre-recorder message

 a chatbot that provides users with the ability to find answers on a website by themselves

 telephone voice menus to reduce the load on human resources

 a service that creates frequently asked Questions (FAQ) documents by crawling public websites

B: A bot is an automated software program designed to perform a particular task. Think of it as a robot without a body.
C: Automated customer interaction is essential to a business of any size. In fact, 61% of consumers prefer to communicate via speech, and most of them prefer self-service. Because customer satisfaction is a priority for all businesses, self-service is a critical facet of any customer-facing communications strategy.
Incorrect Answers:
D: Early bots were comparatively simple, handling repetitive and voluminous tasks with relatively straightforward algorithmic logic. An example would be web crawlers used by search engines to automatically explore and catalog web content.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/ai-overview
https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/interactive-voice-response-bot

QUESTION 67
You need to generate cartoons for use in a brochure. Each cartoon will be based on a text description.
Which Azure OpenAI model should you use?

 Codex

 DALL-E

 GPT-3.5

 GPT-4

To generate cartoons or images from text descriptions, the correct Azure OpenAI model is DALL-E. As described in Microsoft’s OpenAI integration documentation, DALL-E is a generative image model that converts natural language prompts into images, illustrations, and artwork.
Codex is for code generation, GPT-3.5 and GPT-4 are for text and reasoning tasks, not image creation.
Therefore, B. DALL-E is correct.

QUESTION 68
Select the answer that correctly completes the sentence.

Explanation

QUESTION 69
You need to develop a chatbot for a website. The chatbot must answer users’ questions based on the information in the following documents:
A product troubleshooting guide in a Microsoft Word document
A frequently asked questions (FAQ) list on a webpage
Which service should you use to process the documents?

 Text Analytics

 QnA Maker

 Azure Bot Service

 Language Understanding

Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/QnAMaker/Overview/overview

QUESTION 70
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Explanation
Text Description automatically generated

QUESTION 71
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 72
You need to provide content for a business chatbot that will help answer simple user queries.
What are three ways to create question and answer text by using QnA Maker? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

 Generate the questions and answers from an existing webpage.

 Use automated machine learning to train a model based on a file that contains the questions.

 Manually enter the questions and answers.

 Connect the bot to the Cortana channel and ask questions by using Cortana.

 Import chit-chat content from a predefined data source.

Section: Describe features of conversational AI workloads on Azure
Explanation:
Automatic extraction
Extract question-answer pairs from semi-structured content, including FAQ pages, support websites, excel files, SharePoint documents, product manuals and policies.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/content-types

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