A financial services company is building a machine learning pipeline in Azure Machine Learning. They need to store large amounts of structured transaction data for training and also need to track the lineage of datasets used in each experiment. Which combination of Azure services should they use?

Azure Cosmos DB for raw data and Azure ML datastores for lineage tracking.

Azure SQL Database for raw data and Azure Data Lake Gen2 for versioned references.

Azure Blob Storage for raw data and Azure ML datasets for versioned references in experiments.

Azure Files for raw data and Azure ML pipelines for dataset versioning.

What is the primary purpose of automated machine learning (AutoML) in Azure Machine Learning?

To deploy a trained model to a web service without writing any code.

To manage the versioning and tracking of machine learning models.

To automatically generate and test multiple machine learning models to find the best-performing one for your data.

To provide a visual interface for building machine learning pipelines.

A team wants to run an Automated ML experiment using data that resides in an Azure SQL Database. They need to ensure the data is accessible to the experiment and that the best model is deployed to a real-time endpoint. What should they do?

Run Automated ML directly on the SQL database without creating a dataset.

Use Azure Data Factory to copy the data to Azure Blob Storage, then run Automated ML and deploy to ACI.

Create a dataset in Azure ML that references the SQL database, run Automated ML, and deploy the best model to an AKS endpoint.

Export the data to a CSV file and upload it to the compute instance's local disk, then run Automated ML.

Microsoft Certified:Azure AI Fundamentals

Domain 2Objective 3

Describe Azure Machine Learning Capabilities AI-900 Practice Questions (Page 9)

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

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7concepts

15–20%of the exam

Questions 41–45

41foundation · easy
Which compute option in Azure Machine Learning is best suited for interactive data science work such as Jupyter notebooks?
Select an answer first
42foundation · easy
Which Azure service can be used to collect and analyze logs and metrics from a deployed Azure Machine Learning endpoint?
Select an answer first
43application · medium
A financial services company is building a machine learning pipeline in Azure Machine Learning. They need to store large amounts of structured transaction data for training and also need to track the lineage of datasets used in each experiment. Which combination of Azure services should they use?
Select an answer first
44foundation · easy
What is the primary purpose of automated machine learning (AutoML) in Azure Machine Learning?
Select an answer first
45application · medium
A team wants to run an Automated ML experiment using data that resides in an Azure SQL Database. They need to ensure the data is accessible to the experiment and that the best model is deployed to a real-time endpoint. What should they do?
Select an answer first

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Describe Azure Machine Learning Capabilities AI-900 Practice Questions (Page 9)

Questions 41–45

Which compute option in Azure Machine Learning is best suited for interactive data science work such as Jupyter notebooks?

Which Azure service can be used to collect and analyze logs and metrics from a deployed Azure Machine Learning endpoint?

A financial services company is building a machine learning pipeline in Azure Machine Learning. They need to store large amounts of structured transaction data for training and also need to track the lineage of datasets used in each experiment. Which combination of Azure services should they use?

What is the primary purpose of automated machine learning (AutoML) in Azure Machine Learning?

A team wants to run an Automated ML experiment using data that resides in an Azure SQL Database. They need to ensure the data is accessible to the experiment and that the best model is deployed to a real-time endpoint. What should they do?

You’ve practiced. Now get ready to pass.

Keep studying — Describe fundamental principles of machine learning on Azure