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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of generative AI workloads on Azure | 20–25% | - Describe use cases for generative AI - Describe generative AI concepts - Describe responsible AI practices for generative AI - Describe capabilities of Azure OpenAI Service |
| Topic 2: Artificial Intelligence workloads and considerations | 15–20% | - Describe considerations for developing AI solutions - Identify types of AI workloads - Describe responsible AI principles |
| Topic 3: Fundamental principles of machine learning on Azure | 15–20% | - Describe core concepts of machine learning - Describe machine learning pipelines - Describe automated machine learning - Describe capabilities of Azure Machine Learning |
| Topic 4: Features of computer vision workloads on Azure | 15–20% | - Describe capabilities of Azure Face - Describe capabilities of Azure Computer Vision - Describe capabilities of Azure Form Recognizer - Identify types of computer vision solutions - Describe capabilities of Azure Custom Vision |
| Topic 5: Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Translator - Describe capabilities of Azure Language - Describe capabilities of Azure Speech - Identify types of NLP solutions |
The TestValid is offering real and updated Microsoft AI-900 practice test questions. Very easy to use and perfectly assist you in Microsoft AI-900 exam preparation. Microsoft AI-900 Exams and will give you real-time Microsoft AI-900 exam preparation environment all the time.
NEW QUESTION # 103
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:
NEW QUESTION # 104
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation:
Classification
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Describe features of common AI workloads", classification is a supervised machine learning technique used when the goal is to predict which category or class an item belongs to. In supervised learning, the model is trained with labeled data-data that already contains known outcomes. The system learns patterns and relationships between input features and their corresponding labels so it can predict future classifications accurately.
In the scenario provided - "A banking system that predicts whether a loan will be repaid" - the model's output is a binary decision, meaning there are two possible outcomes:
* The loan will be repaid (positive class)
* The loan will not be repaid (negative class)
This kind of problem involves predicting a discrete value (a label or category), not a continuous numeric output. Therefore, it perfectly fits the classification type of machine learning.
The AI-900 learning materials describe classification as being used in many real-world examples, including:
* Determining whether an email is spam or not spam.
* Predicting whether a customer will churn (leave) or stay.
* Detecting fraudulent transactions.
* Assessing medical test results as positive or negative.
By contrast:
* Regression predicts continuous numeric values, such as predicting house prices, temperatures, or sales revenue. It would not apply here because repayment prediction is not a numeric value but a categorical decision.
* Clustering is an unsupervised learning method that groups similar data points without predefined categories, such as segmenting customers by purchasing behavior.
Thus, based on Microsoft's Responsible AI and AI-900 study guide concepts, a banking system that predicts whether a loan will be repaid uses the Classification type of machine learning.
NEW QUESTION # 105
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation:
Features
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Explore fundamental principles of machine learning," data values that influence the prediction of a model are called features. In the context of machine learning, a feature is an individual measurable property, attribute, or input variable used by the model to make predictions.
Features are the independent variables that describe the characteristics of the data. For example, in a housing price prediction model, features might include square footage, location, number of bedrooms, and year built.
These inputs help the model understand relationships in the data so it can predict the target outcome (the house price).
Microsoft Learn explains that features are the input variables that the algorithm uses to identify patterns and relationships in the training data. During training, the model learns how changes in these features influence the label (also known as the dependent variable or target variable). The label is the value the model tries to predict-such as "price," "category," or "yes/no." Here's how the other options differ:
* Dependent variables (labels): These are the outcomes or target values the model predicts, not the inputs.
* Identifiers: These are unique keys (like customer ID or transaction ID) used to distinguish records but not to influence predictions.
* Labels: As mentioned, labels are the results the model tries to predict.
Therefore, based on the AI-900 learning objectives and Microsoft's official explanation, the data values that influence the prediction of a model-that is, the input variables that guide the model's learning-are called features. These features form the foundation of the model's predictive capabilities and directly impact its accuracy and performance.
NEW QUESTION # 106
You have an Azure Machine Learning model that predicts product quality. The model has a training dataset that contains 50,000 records. A sample of the data is shown in the following table.
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:
NEW QUESTION # 107
You have a bot that identifies the brand names of products in images of supermarket shelves.
Which service does the bot use?
Answer: D
NEW QUESTION # 108
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