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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
| Topic 2: Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
| Topic 3: Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
| Topic 4: Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
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NEW QUESTION # 224
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:
Text Description automatically generated
Box 1: Yes
Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the final model generated, and its associated assets. This information offers insights about how the model was created, which allows it to be reproduced in a transparent way.
Box 2: No
A data holder is obligated to protect the data in an AI system, and privacy and security are an integral part of this system. Personal needs to be secured, and it should be accessed in a way that doesn't compromise an individual's privacy.
Box 3: No
Inclusiveness mandates that AI should consider all human races and experiences, and inclusive design practices can help developers to understand and address potential barriers that could unintentionally exclude people. Where possible, speech-to-text, text-to-speech, and visual recognition technology should be used to empower people with hearing, visual, and other impairments.
Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
NEW QUESTION # 225
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Reference:
https://azure.microsoft.com/en-gb/services/cognitive-services/speech-to-text/#features
NEW QUESTION # 226
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:
According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Azure Machine Learning documentation, Automated Machine Learning (AutoML) is a feature designed to help users build, train, and tune machine learning models automatically without requiring deep knowledge of programming or data science.
* First Statement: "Automated machine learning provides you with the ability to include custom Python scripts in a training pipeline."This is False (No). AutoML automates the model selection and tuning process but does not allow the inclusion of custom Python scripts within its workflow. Custom Python integration is supported in Azure Machine Learning designer pipelines or SDK-based training, not in AutoML.
* Second Statement: "Automated machine learning implements machine learning solutions without the need for programming experience."This is True (Yes). One of AutoML's core benefits is that it enables non-programmers to train and evaluate models by simply selecting data, choosing a target column, and letting Azure automatically test algorithms and hyperparameters. This aligns with Microsoft's AI-900 objective to democratize AI development.
* Third Statement: "Automated machine learning provides you with the ability to visually connect datasets and modules on an interactive canvas."This is False (No). That feature belongs to Azure Machine Learning Designer, not AutoML. The designer offers a drag-and-drop visual interface for connecting datasets and modules, whereas AutoML provides a wizard-driven approach focused on automation.
NEW QUESTION # 227
You have the following dataset.
You plan to use the dataset to train a model that will predict the house price categories of houses.
What are Household Income and House Price Category? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
In machine learning, especially within the Microsoft Azure AI Fundamentals (AI-900) framework, datasets used for supervised learning are composed of features (inputs) and labels (outputs). According to the Microsoft Learn module "Explore the machine learning process", a feature is any measurable property or attribute used by the model to make predictions, whereas a label is the actual value or category the model is trying to predict.
* Household Income # FeatureA feature (also known as an independent variable) represents the input data that the machine learning algorithm uses to detect patterns or correlations. In this dataset, Household Income is a numeric value that influences the prediction of house price categories. During training, the model learns how variations in household income correlate with changes in the house price category.
Microsoft Learn defines features as "the attributes or measurable inputs that are used to train the model." Thus, Household Income serves as a predictive input or feature.
* House Price Category # LabelThe label (or dependent variable) represents the output the model aims to predict. It is the known result during training that helps the algorithm learn correct mappings between features and outcomes. In this scenario, House Price Category-which can take values such as "Low,"
"Middle," or "High"-is the classification outcome that the model will predict based on household income (and possibly other variables). According to Microsoft Learn, "the label is the variable that contains the known values that the model is trained to predict." In summary, the dataset defines a supervised learning classification problem, where Household Income is the feature (input) and House Price Category is the label (output) that the model will learn to predict.
NEW QUESTION # 228
You need to create a customer support solution to help customers access information. The solution must support email, phone, and live chat channels. Which type of Al solution should you use?
Answer: B
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study materials and Microsoft Learn module "Describe features of common AI workloads", a chatbot (also known as a conversational AI agent) is a solution designed to interact with users through natural language conversation across multiple channels such as email, phone, webchat, and messaging apps.
Chatbots use Natural Language Processing (NLP) to interpret what users are saying, identify their intent, and provide relevant responses. In Azure, this functionality is implemented using the Azure Bot Service integrated with the Azure Cognitive Service for Language (Question Answering and Language Understanding). The study guide emphasizes that chatbots are used in customer service, information retrieval, and support automation to reduce the workload on human agents and improve response times.
The requirement in this question - supporting email, phone, and live chat channels - aligns exactly with the definition of a conversational AI chatbot, which can operate across multiple communication platforms.
Microsoft Learn clearly identifies that chatbots can be deployed to assist customers in retrieving information, answering FAQs, and escalating complex issues when necessary.
The other options are incorrect because:
* A. NLP is the underlying technology used by the chatbot but not the solution itself.
* B. Computer vision involves analyzing images or videos, which is unrelated to this scenario.
* C. Machine learning is a broader AI field and not a specific customer support solution type.
NEW QUESTION # 229
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