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| Section | Weight | Objectives |
|---|---|---|
| Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
| Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
| Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
| Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
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NEW QUESTION # 204
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?
Answer: C
Explanation:
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview
NEW QUESTION # 205
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The correct completion of the sentence is:
"The interactive answering of questions entered by a user as part of an application is an example of natural language processing." According to the Microsoft Azure AI Fundamentals (AI-900) official study materials, Natural Language Processing (NLP) is a branch of Artificial Intelligence that focuses on enabling computers to understand, interpret, and respond to human language in a way that is both meaningful and useful. It is one of the key AI workloads described in the "Describe features of common AI workloads" module on Microsoft Learn.
When a user types a question into an application and the system responds interactively - such as in a chatbot, Q&A system, or virtual assistant - this process requires language understanding. NLP allows the system to process the input text, determine user intent, extract relevant entities, and generate an appropriate response. This is the foundational capability behind services such as Azure Cognitive Service for Language, Language Understanding (LUIS), and QnA Maker (now integrated as Question Answering in the Language service).
Microsoft's study guide explains that NLP workloads include the following key scenarios:
* Language understanding: Determining intent and context from text or speech.
* Text analytics: Extracting meaning, key phrases, sentiment, or named entities.
* Conversational AI: Powering bots and virtual agents to interact using natural language.These systems rely on NLP models to analyze user inputs and respond accordingly.
In contrast:
* Anomaly detection identifies data irregularities.
* Computer vision analyzes images or video.
* Forecasting predicts future values based on historical data.
Therefore, based on the AI-900 official materials, the interactive answering of user questions through an application clearly falls under Natural Language Processing (NLP).
NEW QUESTION # 206
What can be used to analyze scanned invoices and extract data, such as billing addresses and the total amount due?
Answer: B
NEW QUESTION # 207
Which two components can you drag onto a canvas in Azure Machine Learning designer? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: A,D
Explanation:
In Azure Machine Learning designer, a low-code drag-and-drop interface, users can visually build machine learning workflows. According to the AI-900 study guide and Microsoft Learn module "Create and publish models with Azure Machine Learning designer", two key components that can be dragged onto the designer canvas are datasets and modules.
* Datasets (A): These are collections of data that serve as the input for training or evaluating models.
They can be registered in the workspace and then dragged onto the canvas for use in transformations or model training.
* Modules (D): These are prebuilt processing and modeling components that perform operations such as data cleaning, feature engineering, model training, and evaluation. Examples include "Split Data,"
"Train Model," and "Evaluate Model."
Compute (B) and Pipeline (C) are not drag-and-drop items within the designer. Compute targets are infrastructure resources used to run the pipeline, while a pipeline represents the overall workflow, not a component that can be added like a dataset or module.
Hence, the correct answers are A. Dataset and D. Module.
Reference:Microsoft Learn - Create a machine learning model with Azure Machine Learning designer
NEW QUESTION # 208
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/dotnet/machine-learning/resources/tasks
NEW QUESTION # 209
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