Exam DP-100 topic 6 question 2 discussion

Actual exam question from Microsoft's DP-100

Question #: 2
Topic #: 6

HOTSPOT
-

You plan to implement an Azure Machine Learning solution.

You have the following requirements:

• Run a Jupyter notebook to interactively train a machine learning model.
• Deploy assets and workflows for machine learning proof of concept by using scripting rather than custom programming.

You need to select a development technique for each requirement.

Which development technique should you use? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

Show Suggested Answer

Suggested Answer:

by PI_Team at Sept. 13, 2023, 9:30 p.m.

Comments

Submit Cancel

A_PL300

Highly Voted 1 year, 10 months ago

On September 4, 2023 exam.

upvoted 5 times

...

jefimija

Most Recent 9 months, 2 weeks ago

I wonder if an actual question like this in the exam can have two correct answers?

upvoted 1 times

...

sl_mslconsulting

1 year, 2 months ago

Train models using SDK:https://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-model?view=azureml-api-2&tabs=python using studio: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-train-with-ui?view=azureml-api-2 I will pick SDK for the first requirement as you need that to submit a job in a Notebook

upvoted 1 times

...

ferren

1 year, 9 months ago

bard says the answeres are correct

upvoted 1 times

sar77

1 month ago

1. Run a Jupyter notebook to interactively train a machine learning model: Use the Notebooks feature in Azure Machine Learning studio. This allows you to run Jupyter notebooks directly within your Azure ML workspace, providing an interactive environment for training and experimentation without additional setup. 2. Deploy assets and workflows for machine learning proof of concept by using scripting rather than custom programming: Use the Azure CLI (Command-Line Interface). The Azure CLI enables you to deploy machine learning assets and workflows through scripts, which is ideal for proof of concept scenarios and does not require custom programming.

upvoted 1 times

...

PI_Team

1 year, 10 months ago

For the requirement to run a Jupyter notebook to interactively train a machine learning model, you can use the Azure Machine Learning Python SDK. This SDK provides an interactive environment for training machine learning models and it integrates well with Jupyter notebooks. For deploying assets and workflows for machine learning proof of concept by using scripting rather than custom programming, you can use the Azure CLI. It allows you to manage Azure resources, including Machine Learning assets, using scripts which is ideal for proof of concept deployments.

upvoted 3 times

InversaRadice

1 year, 7 months ago

well question ask to interactively train so AML studio is correct

upvoted 2 times

PI_Team

1 year, 7 months ago

the question specifically mentions using a Jupyter notebook, it does imply that you’ll be writing code. In this case, using the Azure Machine Learning Python SDK would be more appropriate.

upvoted 1 times

deyoz

1 year, 5 months ago

we can also write code via notebook in azure ml studio. I believe both sdk and ml studio are correct options.

upvoted 1 times

...