Microsoft AI-900 Dumps - A Way To Prepare Quickly For Exam

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Microsoft Azure AI Fundamentals Sample Questions (Q127-Q132):

NEW QUESTION # 127
You need to predict the income range of a given customer by using the following dataset.

Which two fields should you use as features? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

Answer: B,D

Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Describe core concepts of machine learning on Azure", when building a predictive machine learning model, features are the input variables used by the algorithm to predict the target label. The target label is the output or value the model is trained to predict.
In this dataset, the target variable is clearly the Income Range, since the goal is to predict a customer's income bracket. Therefore, Income Range (D) is the label, not a feature. Features must be other attributes that help the model make this prediction.
The fields Education Level (A) and Age (C) are the most relevant features because both can logically and statistically influence income level.
* Education Level is a categorical variable that often correlates strongly with income. Individuals with higher education levels tend to earn more on average, making this an important predictor.
* Age is a numerical variable that typically affects income level due to factors such as experience and career progression.
By contrast:
* First Name (E) and Last Name (B) are irrelevant as features because they are identifiers, not meaningful predictors of income. Including them could lead to bias or model overfitting without contributing to accurate predictions.
Hence, according to AI-900 principles, the features used to train a model predicting income range would be Education Level and Age.


NEW QUESTION # 128
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

Reference:
https://docs.microsoft.com/en-gb/azure/cognitive-services/qnamaker/concepts/data-sources-and-content
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service QnA maker conversational AI service nothing to do with SQL database You can easily create a user support bot solution on Microsoft Azure using a combination of two core technologies:
- QnA Maker. This cognitive service enables you to create and publish a knowledge base with built-in natural language processing capabilities.
- Azure Bot Service. This service provides a framework for developing, publishing, and managing bots on Azure.
https://docs.microsoft.com/en-us/learn/modules/build-faq-chatbot-qna-maker-azure-bot-service/2-get-started-qna LUIS is used to understand user intent from utterances.
Creating a language understanding application with Language Understanding consists of two main tasks. First you must define entities, intents, and utterances with which to train the language model - referred to as authoring the model. Then you must publish the model so that client applications can use it for intent and entity prediction based on user input.
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/choose-natural-language-processing-service


NEW QUESTION # 129
You need to convert receipts into transactions in a spreadsheet. The spreadsheet must include the date of the transaction, the merchant the total spent and any taxes paid.
Which Azure Al service should you use?

Answer: B


NEW QUESTION # 130
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 # 131
Select the answer that correctly completes the sentence.

Answer:

Explanation:

Explanation:

The correct answer is Document Intelligence.
According to the Microsoft Azure AI Fundamentals (AI-900) study materials and Microsoft Learn documentation, the Azure AI Document Intelligence service (formerly known as Form Recognizer) is specifically designed to extract structured data from documents, including scanned invoices, receipts, forms, and business cards.
This service combines optical character recognition (OCR) with machine learning to analyze both the layout and semantic meaning of document content. When processing scanned invoices, Document Intelligence identifies and extracts fields such as invoice numbers, dates, totals, taxes, vendor names, and line-item details.
The extracted information can then be automatically imported into business systems like accounting software or databases, eliminating manual data entry and improving operational efficiency.
Here's why the other options are incorrect:
* Generative AI: Focuses on creating new content such as text, images, or code (for example, using GPT-
4 or DALL E). It is not used for structured data extraction.
* Natural Language Processing (NLP): Deals with understanding and generating human language from text-based input, not document scanning or layout interpretation.
The Document Intelligence workload excels at handling semi-structured documents where the location and format of data vary between samples. Microsoft's prebuilt models-like Invoice, Receipt, Identity Document, and Contract-simplify extraction without requiring custom training.
In summary, if the task involves extracting data from scanned invoices, the appropriate Azure AI service is Azure AI Document Intelligence, which uses AI-powered document understanding to convert unstructured document images into structured, usable data.


NEW QUESTION # 132
......

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