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AI-900資訊 - AI-900考古題介紹

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>> AI-900資訊 <<

AI-900考古題介紹，AI-900認證考試解析

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最新的 Microsoft Certified: Azure AI Fundamentals AI-900 免費考試真題 (Q263-Q268):

問題 #263
Select the answer that correctly completes the sentence.

答案：

解題說明：

問題 #264
You have the process shown in the following exhibit.

Which type AI solution is shown in the diagram?

A. a machine learning model
B. a sentiment analysis solution
C. a computer vision application
D. a chatbot

答案：D

問題 #265
For which two workloads can you use computer vision? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. creating visual representations of numerical data
B. creating photorealistic images by using three-dimensional models
C. detecting inconsistencies and anomalies in a stream of data
D. assigning the color pixels in an image to object names
E. describing the contents of an image

答案：D,E

解題說明：
The correct answers are B. assigning the color pixels in an image to object names and C. describing the contents of an image.
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Describe features of computer vision workloads on Azure," computer vision is a branch of AI that enables systems to analyze, interpret, and understand visual data from images and videos. It allows machines to identify objects, people, text, and even describe scenes automatically.
* Option B: Assigning color pixels in an image to object names represents image classification or object detection, which are key computer vision workloads. In these tasks, AI analyzes pixel patterns to determine which pixels correspond to specific objects (for example, classifying pixels as "car," "tree," or "road").
* Option C: Describing the contents of an image corresponds to image captioning, another computer vision workload. It involves using AI models trained to generate natural language descriptions of what is visible in an image, such as "A group of people sitting at a dining table." Azure's Computer Vision service provides this functionality through its "Describe Image" API.
Incorrect options:
* A. Creating photorealistic images involves generative AI and 3D modeling, not traditional computer vision.
* D. Detecting inconsistencies and anomalies in a data stream relates to anomaly detection, not computer vision.
* E. Creating visual representations of numerical data involves data visualization, not AI-driven image analysis.

問題 #266
To complete the sentence, select the appropriate option in the answer area.

答案：

解題說明：

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

問題 #267
Select the answer that correctly completes the sentence.

答案：

解題說明：

Explanation:
When building a K-means clustering model, all features (variables) used in the model must be numeric in nature. According to the Microsoft Azure AI Fundamentals (AI-900) study materials and standard machine learning theory, K-means clustering is an unsupervised learning algorithm that groups data points into clusters based on their similarity - specifically by minimizing the Euclidean distance between data points and their assigned cluster centroids.
Because the K-means algorithm depends on distance calculations, it requires numeric data types. The Euclidean distance (or similar measures) can only be computed between numerical values. Therefore, all categorical or text data must first be converted into numeric form through feature engineering techniques such as one-hot encoding, label encoding, or embedding vectors, depending on the nature of the data.
Here's how K-means works in summary:
* The algorithm initializes a predefined number of centroids (K).
* Each data point is assigned to the nearest centroid based on numeric distance.
* The centroids are recalculated as the mean of the points in each cluster.
* The process repeats until convergence.
If non-numeric data (e.g., text or Boolean) were provided, the model would not be able to calculate distances accurately, leading to computational errors.
Other options are incorrect:
* Boolean and integer types can represent numeric values but are considered special cases; the algorithm requires general numeric representation (e.g., continuous values).
* Text cannot be processed directly without conversion.
Thus, according to Azure Machine Learning and AI-900 official concepts, all features in a K-means clustering model must be numeric to ensure valid mathematical operations and clustering accuracy.

問題 #268
......

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