최신 AI-900日本語 무료덤프 - Microsoft Azure AI Fundamentals (AI-900日本語版)

Explanation:

These matches are based on the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore Azure Cognitive Services." Microsoft Azure provides Cognitive Services that enable developers to integrate artificial intelligence capabilities-such as vision, speech, language understanding, and decision-making-into applications without requiring in-depth AI expertise.
* Convert a user's speech to text # Speech ServiceThe Azure Speech Service supports speech-to-text (STT) conversion, which transcribes spoken language into written text. This feature is commonly used in voice assistants, transcription systems, and voice-enabled apps. The service uses advanced speech recognition models to handle different accents, languages, and background noises.
* Identify a user's intent # Language ServiceThe Azure AI Language Service (which includes capabilities from LUIS - Language Understanding) is used to interpret what a user means or wants to achieve based on their words. It identifies intents (the goal or action behind the input) and entities (key pieces of information) from natural language text. This is a key component in conversational AI applications, allowing chatbots and virtual assistants to respond intelligently.
* Provide a spoken response to the user # Speech ServiceThe Speech Service also supports text-to-speech (TTS) functionality, which converts textual responses into natural-sounding speech. This enables applications to communicate audibly with users, completing the conversational loop.
Translator Text is not used here because it's primarily designed for language translation between different languages, not for speech recognition or intent understanding.

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn documentation for Azure AI Custom Vision, this service is a specialized part of the Azure AI Vision family that enables developers to train custom image classification and object detection models. It allows organizations to build tailored computer vision models that recognize images or specific objects relevant to their business needs.
* Detect objects in an image # YesThe Azure AI Custom Vision service supports both image classification (assigning an image to one or more categories) and object detection (identifying and locating objects within an image using bounding boxes). This means it can indeed detect and differentiate multiple objects in a single image, making this statement true.
* Requires your own data to train the model # YesThe Custom Vision service is designed to be customizable. Unlike prebuilt Azure AI Vision models that work out of the box, Custom Vision requires you to upload and label your own dataset for training. The model then learns from your examples to perform specialized image recognition tasks relevant to your domain. Thus, this statement is also true.
* Analyze video files # NoWhile Custom Vision can analyze images, it does not directly process or analyze video files. Video analysis is handled by a different service-Azure Video Indexer-which can extract insights such as spoken words, scenes, and faces from videos.
In summary:
# Yes - Detect objects in images
# Yes - Requires your own data
# No - Does not analyze video files.

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

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Explanation:

In the context of Microsoft Azure AI Fundamentals (AI-900) and general machine learning principles, regression refers to a type of supervised learning used to predict continuous numerical values based on historical data. The goal of regression is to model the relationship between input variables (features) and a continuous output variable (target).
In this scenario, the task is to predict how many vehicles will travel across a bridge on a given day. The number of vehicles is a numerical value that can vary continuously depending on factors such as time of day, weather, weekday/weekend, or traffic trends. Because the output is numeric and not categorical, this problem type clearly fits into regression analysis.
Microsoft's official learning content for AI-900, under "Identify features of regression and classification machine learning models," specifies that regression models are used to predict values such as sales forecasts, demand estimation, temperature prediction, or traffic volume-all of which share the same underlying objective: predicting a quantity.
To clarify other options:
* Classification is used when predicting categories or discrete classes, such as determining whether an email is spam or not spam, or if an image contains a cat or a dog.
* Clustering is an unsupervised learning technique used to group similar data points without predefined labels (for example, grouping customers by purchasing behavior).
Since predicting the number of vehicles results in a continuous numerical output, it aligns precisely with the regression workload type described in the Microsoft AI-900 study materials.

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Explanation:

In Azure OpenAI Service, the temperature parameter directly controls the creativity and determinism of responses generated by models such as GPT-3.5. According to the Microsoft Learn documentation for Azure OpenAI models, temperature is a numeric value (typically between 0.0 and 2.0) that determines how
"random" or "deterministic" the output should be.
* A lower temperature value (for example, 0 or 0.2) makes the model's responses more deterministic, meaning the same prompt consistently produces nearly identical outputs.
* A higher temperature value (for example, 0.8 or 1.0) encourages creativity and variety, causing the model to generate different phrasing or interpretations each time it responds.
When a question specifies the need for more deterministic responses, Microsoft's guidance is to decrease the temperature parameter. This adjustment makes the model focus on the most probable tokens (words) rather than exploring less likely options, improving reliability and consistency-ideal for business or technical applications where consistent answers are essential.
The other parameters serve different purposes:
* Frequency penalty reduces repetition of the same phrases but does not control randomness.
* Max response (max tokens) limits the maximum length of the generated output.
* Stop sequence defines specific tokens that tell the model when to stop generating text.
Thus, the correct and Microsoft-verified completion is:
"You can modify the Temperature parameter to produce more deterministic responses from a chat solution that uses the Azure OpenAI GPT-3.5 model."

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