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| Section | Weight | Objectives |
|---|---|---|
| Describe features of computer vision workloads on Azure | 15-20% | - Identify Azure AI services for computer vision - Describe Azure capabilities for computer vision - Identify common computer vision tasks |
| Describe fundamental principles of machine learning on Azure | 30-35% | - Identify common machine learning tasks - Describe Azure Machine Learning capabilities - Describe features of no-code automated ML - Describe core machine learning concepts |
| Describe features of Generative AI workloads on Azure | 15-20% | - Describe generative AI concepts - Identify responsible AI considerations for generative AI - Describe Azure OpenAI Service capabilities |
| Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify Azure AI services for NLP - Describe Azure capabilities for NLP - Identify common NLP tasks |
| Describe AI workloads and considerations | 15-20% | - Identify features of common AI workloads - Identify guiding principles for responsible AI |
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NEW QUESTION # 165
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer
NEW QUESTION # 166
What should you use to identify similar faces in a set of images?
Answer: A
Explanation:
The correct service to identify similar faces in a set of images is Azure AI Vision, which includes the Face API capability. According to the Microsoft Learn module "Analyze images with Azure AI Vision", this service provides prebuilt models for face detection, facial recognition, and similarity matching.
The Face API can detect individual faces in images and extract unique facial features to create a face embedding (a numerical representation of the face). It then compares these embeddings across multiple images to determine whether faces are similar or belong to the same person. This functionality is commonly used in identity verification, photo management systems, and security solutions.
The other options are incorrect:
* B. Azure AI Custom Vision is used for custom image classification or object detection but does not provide face similarity or recognition features.
* C. Azure AI Language processes text-based data (sentiment, entities, key phrases) - not visual content.
* D. Azure OpenAI Service focuses on text generation, summarization, and conversation, not facial analysis.
Therefore, the Microsoft-verified service for identifying similar faces across images is A. Azure AI Vision.
NEW QUESTION # 167
You plan to deploy an Azure Machine Learning model as a service that will be used by client applications.
Which three processes should you perform in sequence before you deploy the model? To answer, move the appropriate processes from the list of processes to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation
Graphical user interface, text, application, chat or text message Description automatically generated
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines
NEW QUESTION # 168
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
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
NEW QUESTION # 169
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," regression is a supervised machine learning technique used to predict continuous numeric values based on input data.
In this scenario, the goal is to predict how many hours of overtime a delivery person will work depending on the number of orders received. The output - the number of overtime hours - is a continuous variable (for example, 1.5 hours, 3.2 hours, etc.), not a category. This makes it a regression problem, where the model learns patterns from historical data and uses those patterns to estimate a continuous numeric outcome.
Why Regression Applies Here:
Regression models work by finding the mathematical relationship between input features (independent variables) and output values (dependent variables). In this case:
* Input (feature): Number of orders received
* Output (label): Predicted overtime hours
Azure Machine Learning supports several regression algorithms, including Linear Regression, Decision Tree Regression, and Neural Network Regression, all of which can handle scenarios where a numeric prediction is required.
Why Not the Other Options:
* Classification: Used for predicting discrete categories or labels (e.g., "on-time" vs. "late"). It does not output continuous numbers.
* Clustering: An unsupervised learning technique used to group data points with similar characteristics, not to make numeric predictions.
Thus, when the output variable is a numeric prediction (such as hours, prices, quantities, or time), the correct machine learning task is Regression.
NEW QUESTION # 170
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