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[General] AI-900 Test Questions - AI-900 Test Torrent & AI-900 Latest Torrents

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【General】 AI-900 Test Questions - AI-900 Test Torrent & AI-900 Latest Torrents

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Microsoft Azure AI Fundamentals Sample Questions (Q31-Q36):NEW QUESTION # 31
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:
Yes, No, Yes.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify capabilities of Azure Cognitive Services for Language", the Azure Translator service is a cloud-based machine translation service used to translate text or entire documents between languages in real time. It uses REST APIs or client libraries to translate text input, detect languages, and support multiple target languages in a single request.
* "The following service call will accept English text as an input and output Italian and French text:
/translate?from=en&to=it,fr - Yes.This URL format is correct because the Translator service API allows multiple target languages to be specified in a single to parameter separated by commas. In this case, from=en defines the source language (English), and to=it,fr requests translations into Italian (it) and French (fr). The API would return results in both target languages simultaneously. This syntax is officially documented in Microsoft Learn as the valid format for multi-language translation.
* "The following service call will accept English text as an input and output Italian and French text:
/translate?from=en&to=fr&to=it - No.This format is incorrect, as the Translator API does not support repeating the to parameter multiple times. Only one to parameter is valid, and multiple target languages must be provided as a comma-separated list within the same to parameter.
* "The Translator service can be used to translate documents from English to French." - Yes.This statement is true. The Translator service supports both text translation and document translation. The document translation capability allows the translation of whole files such as Word, PowerPoint, or PDF documents while preserving formatting and structure. This feature is included in the official Translator API under "Document Translation." In summary, the AI-900 study content clarifies that:
# /translate?from=en&to=it,fr # Valid syntax
# /translate?from=en&to=fr&to=it # Invalid syntax
# Translator can translate full documents between languages

NEW QUESTION # 32
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:

Reference:
https://docs.microsoft.com/en-us ... -analytics/overview

NEW QUESTION # 33
You need to predict the sea level in meters for the next 10 years.
Which type of machine learning should you use?
  • A. regression
  • B. clustering
  • C. classification
Answer: A
Explanation:
Explanation
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 ... e/linear-regression

NEW QUESTION # 34
Select the answer that correctly completes the sentence

Answer:
Explanation:


NEW QUESTION # 35
Your website has a chatbot to assist customers.
You need to detect when a customer is upset based on what the customer types in the chatbot.
Which type of AI workload should you use?
  • A. regression
  • B. anomaly detection
  • C. semantic segmentation
  • D. natural language processing
Answer: D
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) curriculum and Microsoft Learn module
"Explore natural language processing", NLP enables computers to understand, interpret, and analyze human language. One of its key capabilities is sentiment analysis, which detects emotional tone (positive, negative, or neutral) in text.
In this scenario, the chatbot must detect when a customer is upset based on what they type. This directly maps to sentiment analysis, a core NLP function. The Text Analytics service within Azure Cognitive Services provides prebuilt sentiment analysis models that return a sentiment score and classification (e.g., positive, neutral, negative). As per Microsoft Learn, "Natural language processing allows systems to understand sentiment and intent within text and speech to derive meaningful insights." Explanation of other options:
* A. Anomaly detection identifies unusual patterns in data (e.g., fraud detection), not emotions in text.
* B. Semantic segmentation is a computer vision technique used to label pixels in an image.
* C. Regression predicts continuous numeric values and is not related to understanding text or emotions.
Therefore, to enable the chatbot to detect when a user is upset based on text input, the correct AI workload is Natural Language Processing (NLP), specifically through Azure Text Analytics sentiment analysis.

NEW QUESTION # 36
......
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