最新的Microsoft Azure AI Fundamentals - AI-900免費考試真題
What is an example of the Microsoft responsible Al principle of transparency?
正確答案: B
說明：（僅 Fast2test 成員可見）
You have a solution that analyzes social media posts to extract the mentions of city names and the city names discussed most frequently.
Which type of natural language processing (NLP) workload does the solution use?
Which type of natural language processing (NLP) workload does the solution use?
正確答案: A
You have a website that includes customer reviews.
You need to store the reviews in English and present the reviews to users in their respective language by recognizing each user's geographical location.
Which type of natural language processing workload should you use?
You need to store the reviews in English and present the reviews to users in their respective language by recognizing each user's geographical location.
Which type of natural language processing workload should you use?
正確答案: C
說明：（僅 Fast2test 成員可見）
To complete the sentence, select the appropriate option in the answer area.
正確答案:
Explanation:
In the Microsoft Azure AI Fundamentals (AI-900) and Azure Machine Learning (AML) learning paths, deploying a real-time inference pipeline refers to making a trained machine learning model available as a web service that can process incoming data and return predictions instantly. To achieve this, the model must be deployed to an infrastructure capable of handling continuous, low-latency requests with high reliability and scalability.
Microsoft's official guidance from Azure Machine Learning documentation specifies that:
* For testing or development, you can deploy to Azure Container Instances (ACI) because it provides a lightweight, temporary environment suitable for small-scale or non-production workloads.
* For production-grade, real-time inference, the deployment should be made to Azure Kubernetes Service (AKS).
AKS provides enterprise-level scalability, load balancing, and high availability, which are critical for serving real-time predictions to multiple consumers simultaneously. It manages containerized applications using Kubernetes orchestration, allowing the model to scale automatically based on traffic demands.
Azure Machine Learning Compute is mainly used for model training and batch inference pipelines, not real- time endpoints. A local web service is typically used only for debugging or offline testing on a developer machine and cannot be shared for external consumption.
Therefore, when deploying a real-time inference pipeline as a service for others to consume, the correct and Microsoft-verified option is Azure Kubernetes Service (AKS). This environment ensures production readiness, secure endpoint management, and scalability for live AI applications, fully aligning with best practices outlined in the Azure Machine Learning designer documentation and AI-900 exam objectives.
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer#deploy
Match the Al solution to the appropriate task.
To answer, drag the appropriate solution from the column on the left to its task on the right. Each solution may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
To answer, drag the appropriate solution from the column on the left to its task on the right. Each solution may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
正確答案:
Explanation:
This question evaluates your understanding of how different Azure AI workloads correspond to specific tasks in image, text, and content generation scenarios, as explained in the Microsoft Azure AI Fundamentals (AI-
900) study guide and Microsoft Learn modules covering common AI workloads and Azure services.
* Generate a caption from a given image # Computer VisionThis is a computer vision task because it involves analyzing the visual elements of an image and producing descriptive text (a caption). Azure AI Vision provides image analysis and captioning capabilities through its Describe Image API, which uses deep learning models to recognize objects, scenes, and actions in an image and automatically generate natural-language descriptions (e.g., "A cat sitting on a sofa").
* Generate an image from a given caption # Generative AIThis task belongs to Generative AI, which focuses on creating new content such as text, code, or images based on prompts. Tools like Azure OpenAI Service with DALL-E can interpret text descriptions and generate realistic images that match the given caption. Generative AI is capable of creative synthesis, not just analysis, making it the appropriate category.
* Generate a 200-word summary from a 2,000-word article # Text AnalyticsText analytics (a subset of natural language processing) allows summarization, sentiment analysis, and entity recognition from large text corpora. Azure AI Language includes text summarization capabilities that condense long documents into concise summaries while preserving meaning and key information.
Match the machine learning models to the appropriate deceptions.
To answer, drag the appropriate model from the column on the left to its description on the right Each model may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
To answer, drag the appropriate model from the column on the left to its description on the right Each model may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
正確答案:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of common machine learning types", the three main machine learning model types differ by their purpose and the kind of data they use - whether supervised (using labeled data) or unsupervised (using unlabeled data).
* Regression # A supervised machine learning model used to predict numeric values.Regression is a type of supervised learning that predicts continuous numerical outcomes. It learns the relationship between input features (independent variables) and a continuous target variable (dependent variable). Examples include predicting house prices, sales revenue, or temperature. The AI-900 curriculum emphasizes regression for "predicting numeric values based on known data," using algorithms such as linear regression or decision tree regression.
* Classification # A supervised machine learning model used to predict categories.Classification is also a supervised learning technique, but it predicts discrete outcomes (classes) instead of continuous values.
It assigns input data to one or more categories based on learned patterns. Typical examples include spam detection (spam vs. not spam), sentiment analysis (positive, neutral, negative), or predicting loan approval (approved/denied). The AI-900 study materials describe classification as "predicting a category or label for new observations."
* Clustering # An unsupervised machine learning model used to group similar entities based on features.
Clustering is an unsupervised learning approach, meaning it works on unlabeled data. It automatically identifies patterns and groups similar data points into clusters based on shared characteristics. Examples include customer segmentation (grouping customers by behavior) and grouping similar documents. The AI-900 learning module explains clustering as "discovering natural groupings in data without predefined labels." Thus, per Microsoft's official AI-900 learning objectives:
* Regression # Predicts numeric/continuous values.
* Classification # Predicts categories/labels.
* Clustering # Groups similar entities (unsupervised).
# Final Verified Configuration:
* Regression # Predict numeric values
* Classification # Predict categories
* Clustering # Group similar entities based on features
Which two actions can you perform by using the Azure OpenAI DALL-E model? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.
NOTE: Each correct answer is worth one point.
正確答案: B,C
說明：（僅 Fast2test 成員可見）
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
NOTE: Each correct selection is worth one point.
正確答案:
Explanation:
Box 1: Yes
Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.
Box 2: No
Box 3: Yes
During training, Azure Machine Learning creates a number of pipelines in parallel that try different algorithms and parameters for you. The service iterates through ML algorithms paired with feature selections, where each iteration produces a model with a training score. The higher the score, the better the model is considered to " fit " your data. It will stop once it hits the exit criteria defined in the experiment.
Box 4: No
Apply automated ML when you want Azure Machine Learning to train and tune a model for you using the target metric you specify.
The label is the column you want to predict.
Reference:
https://azure.microsoft.com/en-us/services/machine-learning/automatedml/#features