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You can read the Best Solution to prepare AI-900: Microsoft Azure AI Fundamentals Exam. It has become an essential trend for the candidates who are preparing for the exam. There are several methods in this field. Familiarity with the necessary resources is essential for this purpose. Translator is comes with the certification. Solutiondescribe is required for this purpose. Forecasting is used for the AI. The exam AI-900 Microsoft Azure AI Fundamentals Exam is useful to work on several systems. Feature engineering is a very important method of the AI. Evaluation and testing of the accuracy and correctness is the best practice for this purpose. Regression analysis is used to achieve this. Data validation is used for this purpose.
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NEW QUESTION # 58
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The correct answer is Azure AI Language, which includes the Question Answering capability (previously known as QnA Maker). According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation, the Azure AI Language service can be used to create a knowledge base from frequently asked questions (FAQ) and other structured or semi-structured text sources.
This service allows developers to build intelligent applications that can understand and respond to user questions in natural language by referencing prebuilt or custom knowledge bases. The Question Answering feature extracts pairs of questions and answers from documents, websites, or manually entered data and uses them to construct a searchable knowledge base. This knowledge base can then be integrated with Azure Bot Service or other conversational platforms to create interactive, self-service chatbots.
Here's how it works:
* Developers upload FAQ documents, URLs, or structured content.
* Azure AI Language processes the content and identifies logical question-answer pairs.
* The model stores these pairs in a knowledge base that can be queried by user input.
* When users ask questions, the model finds the best matching answer using natural language understanding techniques.
In contrast:
* Azure AI Document Intelligence (Form Recognizer) is used to extract structured data from forms and documents, not to create FAQ knowledge bases.
* Azure AI Bot Service is for managing and deploying conversational bots but does not generate knowledge bases.
* Microsoft Bot Framework SDK provides tools for building conversational logic but still requires a knowledge source like Question Answering from Azure AI Language.
Therefore, the service that can create a knowledge base from FAQ content is Azure AI Language.
NEW QUESTION # 59
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/linear-regression
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize-model-clustering Regression is a form of machine learning that is used to predict a numeric label based on an item's features.
https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer/introduction
NEW QUESTION # 60
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation
Azure Custom Vision is a cognitive service that lets you build, deploy, and improve your own image classifiers. An image classifier is an AI service that applies labels (which represent classes) to images, according to their visual characteristics. Unlike the Computer Vision service, Custom Vision allows you to specify the labels to apply.
Note: The Custom Vision service uses a machine learning algorithm to apply labels to images. You, the developer, must submit groups of images that feature and lack the characteristics in question. You label the images yourself at the time of submission. Then the algorithm trains to this data and calculates its own accuracy by testing itself on those same images. Once the algorithm is trained, you can test, retrain, and eventually use it to classify new images according to the needs of your app. You can also export the model itself for offline use.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/home custom vision - This is a type of computer vision service which helps in building/training models using user provided data Creating an object detection solution with Custom Vision consists of three main tasks. First you must use upload and tag images, then you can train the model, and finally you must publish the model so that client applications can use it to generate predictions.
https://docs.microsoft.com/en-us/learn/modules/detect-objects-images-custom-vision/2-object-detection-azure
NEW QUESTION # 61
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://azure.microsoft.com/es-es/blog/machine-assisted-text-classification-on-content-moderator-public-preview/
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
NEW QUESTION # 62
You have a large dataset that contains motor vehicle sales data.
You need to train an automated machine learning (automated ML) model to predict vehicle sale values based on the type of vehicle.
Which task should you select? To answer, select the appropriate task in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) and Azure Machine Learning documentation, regression is the appropriate machine learning task when the goal is to predict continuous numeric values- such as prices, sales amounts, or other measurable quantities.
In this scenario, the dataset contains motor vehicle sales data, and the objective is to predict vehicle sale values. A vehicle's sale value (price) is a continuous numeric variable, meaning it can take on a wide range of possible numbers (for example, $15,000, $28,500, $42,300). Regression models are designed to analyze relationships between input features (like make, model, mileage, age, fuel type) and a continuous output variable (price).
Automated Machine Learning (AutoML) in Azure simplifies this process by automatically testing multiple regression algorithms (e.g., Linear Regression, Random Forest, Gradient Boosted Trees) and hyperparameters to find the best-performing model.
Let's evaluate why the other options are incorrect:
* Classification - Used for predicting discrete categories (e.g., car type: sedan, SUV, or truck). Sale value prediction is not categorical.
* Time series forecasting - Used when predicting future values based on time-dependent data (e.g., sales over months or years). The question focuses on predicting price based on vehicle characteristics, not over time.
* Natural Language Processing (NLP) - Deals with text-based data, not numeric vehicle data.
* Computer Vision - Applies to image-based tasks (e.g., detecting car types from photos).
Therefore, per Microsoft Learn's Automated ML task selection guidance, when predicting a numeric output like vehicle sale value, the correct machine learning task type is Regression.
# Final answer: Regression
NEW QUESTION # 63
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