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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Describe fundamental principles of machine learning on Azure | 30-35% | - Describe core machine learning concepts - Describe Azure Machine Learning capabilities - Identify common machine learning tasks - Describe features of no-code automated ML |
| Topic 2: Describe features of Natural Language Processing (NLP) workloads on Azure | 15-20% | - Identify common NLP tasks - Describe Azure capabilities for NLP - Identify Azure AI services for NLP |
| Topic 3: Describe features of Generative AI workloads on Azure | 15-20% | - Identify responsible AI considerations for generative AI - Describe Azure OpenAI Service capabilities - Describe generative AI concepts |
| Topic 4: Describe features of computer vision workloads on Azure | 15-20% | - Identify Azure AI services for computer vision - Identify common computer vision tasks - Describe Azure capabilities for computer vision |
| Topic 5: 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 # 286
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
Azure Kubernetes Service (AKS).
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn documentation on Azure Machine Learning, the Azure Kubernetes Service is commonly used to host and deploy machine learning models, including Automated ML models, into production environments. Once a model is trained using Azure Machine Learning (Azure ML), it must be deployed as a web service endpoint so it can receive data and return predictions.
Azure ML offers two primary options for hosting and deploying models:
* Azure Kubernetes Service (AKS) - for high-scale, production-grade deployments that require fast response times, high availability, and scalability.
* Azure Container Instances (ACI) - for testing or low-scale workloads where cost and simplicity are more important than performance.
AKS provides a managed Kubernetes cluster that allows for automated container orchestration, load balancing, scaling, and monitoring of deployed machine learning models. When you use Automated ML in Azure ML Studio, the generated model can be containerized and deployed directly to AKS, making it accessible as a REST API endpoint. This enables applications, systems, or users to send data and receive predictions in real time.
The other options serve different purposes:
* Azure Data Factory is used for data integration and pipeline orchestration, not model hosting.
* Azure Automation focuses on automating administrative tasks and runbooks, not ML deployment.
* Azure Logic Apps is used to automate workflows and integrate services, not to serve ML models.
Therefore, the correct service to host automated machine learning (AutoML) models in production is Azure Kubernetes Service (AKS), as it provides a reliable, scalable, and secure environment for real-time inference and enterprise AI workloads.
NEW QUESTION # 287
You need to create a clustering model and evaluate the model by using Azure Machine Learning designer. What should you do?
Answer: B
NEW QUESTION # 288
You have the Predicted vs. True chart shown in the following exhibit.
Which type of model is the chart used to evaluate?
Answer: C
Explanation:
What is a Predicted vs. True chart?
Predicted vs. True shows the relationship between a predicted value and its correlating true value for a regression problem. This graph can be used to measure performance of a model as the closer to the y=x line the predicted values are, the better the accuracy of a predictive model.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-m
NEW QUESTION # 289
A medical research project uses a large anonymized dataset of brain scan images that are categorized into predefined brain haemorrhage types.
You need to use machine learning to support early detection of the different brain haemorrhage types in the images before the images are reviewed by a person.
This is an example of which type of machine learning?
Answer: B
Explanation:
Reference:
https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/int
NEW QUESTION # 290
Select the answer that correctly completes the sentence
Answer:
Explanation:
Explanation:
NEW QUESTION # 291
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