Deal of The Day! Hurry Up, Grab the Special Discount - Save 25% - Ends In 00:00:00 Coupon code: SAVE25
Welcome to Pass4Success

- Free Preparation Discussions
Mail Us support@pass4success.com
Location US
Mob_nav_barsMENU

Microsoft Exam DP-100 Topic 4 Question 114 Discussion

Actual exam question for Microsoft's DP-100 exam
Question #: 114
Topic #: 4
[All DP-100 Questions]

You create an Azure Machine Learning workspace named woricspace1. The workspace contains a Python SDK v2 notebook that uses MLflow to collect model training metrics and artifacts from your local computer.

You must reuse the notebook to run on Azure Machine Learning compute instance in workspace1.

You need to continue to log metrics and artifacts from your data science code.

What should you do?

Show Suggested Answer Hide Answer
Suggested Answer: A

by Avery at Sep 15, 2024, 05:09 PM

Limited Time Offer

25%

Off

Get Premium DP-100 Questions as Interactive Web-Based Practice Test or PDF

Contribute your Thoughts:

Submit Cancel
Rikki
10 months ago
Option B is an interesting choice, but it doesn't seem to be the right answer here. Instantiating the job class is more for managing the execution of your training job, not for logging metrics and artifacts.
upvoted 0 times
Lore
9 months ago
D) Instantiate the MLCIient class.
upvoted 0 times
...
Marylyn
9 months ago
C) Log into workspace!
upvoted 0 times
...
Raul
9 months ago
A) Configure the tracking URI.
upvoted 0 times
...
...
Cristina
10 months ago
C is a bit suspicious. Logging into the workspace seems unnecessary when you already have an Azure Machine Learning workspace set up. Unless there's some kind of authentication issue, I don't think that's the right answer.
upvoted 0 times
...
Jodi
10 months ago
I'm torn between A and D. Both seem to be the correct approach, but I'm curious to know if there's a specific reason why one might be preferred over the other.
upvoted 0 times
Toshia
9 months ago
D) Instantiate the MLCIient class.
upvoted 0 times
...
Zachary
9 months ago
C) Log into workspace!
upvoted 0 times
...
Johnson
9 months ago
B) Instantiate the job class.
upvoted 0 times
...
Tammi
9 months ago
A) Configure the tracking URI.
upvoted 0 times
...
...
Melodie
10 months ago
I'm not sure. Maybe we should also consider instantiating the MLCIient class for this task.
upvoted 0 times
...
Fernanda
10 months ago
I agree with Rebbecca. Configuring the tracking URI seems like the right step to take.
upvoted 0 times
...
Ettie
10 months ago
D looks good to me. Instantiating the MLClient class will give you the necessary interface to interact with MLflow from your Azure Machine Learning environment.
upvoted 0 times
Mozell
9 months ago
D) Instantiate the MLClient class.
upvoted 0 times
...
Asha
9 months ago
C) Log into workspace!
upvoted 0 times
...
Kirk
9 months ago
B) Instantiate the job class.
upvoted 0 times
...
Kina
10 months ago
A) Configure the tracking URI.
upvoted 0 times
...
...
Ryan
11 months ago
Option A is the way to go. Configuring the tracking URI will allow you to log metrics and artifacts to your Azure Machine Learning workspace.
upvoted 0 times
Lynelle
10 months ago
No, that's not the right step for logging metrics and artifacts.
upvoted 0 times
...
Remona
10 months ago
D) Instantiate the MLCIient class.
upvoted 0 times
...
Merilyn
10 months ago
Yes, logging into the workspace is important for tracking.
upvoted 0 times
...
Kallie
10 months ago
C) Log into workspace!
upvoted 0 times
...
Kenda
10 months ago
Great idea! That will allow you to log metrics and artifacts.
upvoted 0 times
...
Adelina
10 months ago
A) Configure the tracking URI.
upvoted 0 times
...
...
Rebbecca
11 months ago
I think we should configure the tracking URI to continue logging metrics and artifacts.
upvoted 0 times
...

Save Cancel