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Microsoft AI-900 exam, also known as the Microsoft Azure AI Fundamentals exam, is an entry-level certification exam designed to validate a candidate's foundational knowledge of Artificial Intelligence (AI) and Machine Learning (ML) concepts, as well as their ability to work with Microsoft Azure's AI services. AI-900 exam is intended for individuals who are interested in learning about AI and ML principles and how they can be applied to solve business problems.
Microsoft AI-900 certification exam covers a wide range of topics related to AI and machine learning, including fundamental concepts such as machine learning models, data analysis, and natural language processing. AI-900 Exam also covers more advanced topics such as computer vision, speech recognition, and intelligent decision-making. Microsoft Azure AI Fundamentals certification exam is designed to test an individual's knowledge of AI concepts and their ability to apply them to real-world scenarios using Microsoft Azure services.
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NEW QUESTION # 230
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
The correct answer is "An embedding."
In the context of large language models (LLMs) such as GPT-3, GPT-3.5, or GPT-4, an embedding refers to a multi-dimensional numeric vector representation assigned to each word, token, or phrase. According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation for Azure OpenAI embeddings, embeddings are used to represent textual or semantic meaning in a numerical form that a machine learning model can process mathematically.
Each embedding captures the semantic relationships between words. Words or tokens with similar meanings (for example, "car" and "automobile") are represented by vectors that are close together in the multi- dimensional space, while unrelated words (like "tree" and "laptop") are farther apart. This vector representation enables the model to understand context, similarity, and relationships between different pieces of text.
Embeddings are fundamental in tasks such as:
* Semantic search: Finding documents or sentences with similar meaning.
* Clustering: Grouping related concepts together.
* Recommendation systems: Suggesting similar content based on text meaning.
* Contextual understanding: Helping generative models produce coherent and context-aware text.
Option review:
* Attention: A mechanism used within transformers to focus on relevant parts of input sequences but not a representation of words.
* A completion: Refers to the generated text output from a model, not the internal representation.
* A transformer: The architecture that powers models like GPT, not the vector representation of tokens.
Therefore, the correct term for a multi-dimensional vector assigned to each word or token in a large language model (LLM) is An embedding, which represents how meaning is numerically encoded and compared within language models.
NEW QUESTION # 231
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore fundamental principles of machine learning," a regression model is used when the goal is to predict a continuous numerical value based on historical data.
In this question, the task is to predict the sale price of auctioned items, which is a numeric output that can take on a wide range of values (for example, $50.25, $199.99, etc.). This makes it a regression problem because the output is continuous rather than categorical.
Regression models analyze the relationship between input features (such as item type, condition, age, bidding history, or demand) and a numerical target variable (the sale price). Common regression algorithms include linear regression, decision tree regression, and neural network regression. In Azure Machine Learning, these models are trained using labeled datasets containing known outcomes to learn patterns and make future predictions.
Let's review the incorrect options:
* Classification: Used to predict discrete categories or labels, such as "sold" vs. "unsold" or "low,"
"medium," "high." It cannot output continuous numeric predictions.
* Clustering: An unsupervised technique used to group similar data points based on shared characteristics, not to predict specific numeric outcomes.
Therefore, because predicting a sale price involves forecasting a continuous numerical value, the correct model type is Regression.
This aligns with Microsoft's AI-900 teaching that regression is used for tasks such as:
* Predicting house prices
* Forecasting sales revenue
* Estimating car values or auction prices
NEW QUESTION # 232
You need to predict the animal population of an area.
Which Azure Machine Learning type should you use?
Answer: B
NEW QUESTION # 233
Which type of machine learning should you use to predict the number of gift cards that will be sold next month?
Answer: B
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features of regression machine learning", regression is the machine learning technique used when the goal is to predict a continuous numeric value based on historical data. In this question, predicting the number of gift cards that will be sold next month involves forecasting a quantity-a numeric outcome- which is the hallmark of a regression problem.
Regression models learn patterns from past data (for example, previous months' gift card sales, seasonality, holidays, and marketing spend) and use that information to predict future sales. Common algorithms used for regression include linear regression, decision tree regression, and boosted regression trees. The output is a continuous value such as "2,450 gift cards expected next month." In contrast:
* A. Classification is used when the output is categorical, such as predicting whether a transaction is
"fraud" or "not fraud," or whether a customer will "renew" or "cancel." It answers questions with discrete classes rather than numeric values.
* C. Clustering is an unsupervised learning technique used to group similar data points together based on their characteristics-for example, segmenting customers into behavior-based clusters. Clustering doesn't predict future numeric outcomes.
The AI-900 curriculum explicitly explains that regression predicts numeric values, classification predicts categories, and clustering finds natural groupings in data.
Therefore, to predict the number of gift cards to be sold, the correct and verified machine learning type is Regression.
Final answer: B. Regression
Reference:Microsoft Learn - Identify the types of machine learning models: Regression, Classification, and Clustering (AI-900 Learning Path)
NEW QUESTION # 234
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features of common machine learning types", the term features refers to the input variables or independent variables used by a machine learning model to make predictions. These are the measurable properties or attributes of the data that influence the output (target) value.
In a supervised learning process, data is typically divided into two parts:
* Features # The input variables used by the model to learn patterns (e.g., customer age, income, credit score).
* Label (Target) # The outcome or value the model is trying to predict (e.g., whether a loan will be approved or the amount of a house price).
During training, the model uses the features to understand how input data correlates with the target output.
Once trained, the model applies the same learned relationships to predict outcomes for new, unseen data using only the features.
For example:
* In a regression model predicting house prices, features might include square footage, number of bedrooms, and location.
* In a classification model predicting loan approval, features might include applicant income, credit score, and debt ratio.
To contrast with other options:
* Dependent variables (or labels) are the outcomes the model predicts.
* Identifiers (like customer IDs) are unique values that do not help the model learn relationships and are typically excluded from features.
* Labels are the target outputs, not the inputs.
Therefore, in Azure Machine Learning and AI-900 terminology, data values used to make a prediction are called "features."
NEW QUESTION # 235
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