A team needs to deploy a model for real-time scoring with low latency. They also need to scale automatically based on demand. Which compute target should they use?

Azure Machine Learning compute instance

Azure Container Instances (ACI)

A data scientist needs to run an automated ML experiment that trains multiple models in parallel. They want to track all runs and compare metrics. What should they configure in Azure Machine Learning?

A single-node compute instance and a dataset

A compute cluster with multiple nodes and a run history

An inference cluster and a model registry

A company wants to use automated ML to train a model on data that is updated daily. They need a central location to store the data that is accessible from Azure ML. Which data service should they use?

Azure Blob Storage registered as a datastore

A machine learning team has trained several versions of a model for a churn prediction task. They need to compare the performance of each version, track which dataset and hyperparameters were used, and then deploy the best version to production. What should they do in Azure Machine Learning?

Retrain the model each time a new version is needed and overwrite the previous model in the workspace.

Use the Runs and Models pages to compare metrics, register the best model with a version number, and deploy it to an endpoint.

Store each model as a separate file in Azure Blob Storage and manually track performance in a spreadsheet.

Use Azure Container Instances to host all model versions simultaneously and route traffic manually.

A data engineer wants to use automated ML with data stored in an Azure SQL Database. What must they do first?

Export the data to CSV and upload to Blob Storage

Register the SQL database as a datastore in Azure ML

Microsoft Certified:Azure AI Fundamentals

Domain 2Objective 3

Describe Azure Machine Learning Capabilities AI-900 Practice Questions (Page 3)

Part of the Describe fundamental principles of machine learning on Azure domain, which accounts for 15–20% of the AI-900 exam. Microsoft does not publish an official question count, but from its 60-minute exam (~25–40 total, ~4–8 in this domain), expect 1–3 from this objective — we provide 47 practice questions to prepare you well beyond it. (estimate)

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47questions here

10free pages

7concepts

15–20%of the exam

Questions 11–15

11application · medium
A team needs to deploy a model for real-time scoring with low latency. They also need to scale automatically based on demand. Which compute target should they use?
Select an answer first
12application · medium
A data scientist needs to run an automated ML experiment that trains multiple models in parallel. They want to track all runs and compare metrics. What should they configure in Azure Machine Learning?
Select an answer first
13application · medium
A company wants to use automated ML to train a model on data that is updated daily. They need a central location to store the data that is accessible from Azure ML. Which data service should they use?
Select an answer first
14application · medium
A machine learning team has trained several versions of a model for a churn prediction task. They need to compare the performance of each version, track which dataset and hyperparameters were used, and then deploy the best version to production. What should they do in Azure Machine Learning?
Select an answer first
15application · medium
A data engineer wants to use automated ML with data stored in an Azure SQL Database. What must they do first?
Select an answer first

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