My Quick Study Guides For Exam DP-100 Designing and Implementing a Data Science Solution on Azure
On this blog post, I am going to share with you My Quick Learn Data Science and Study Strategy For Validate Skills on Microsoft Exam DP-100: Designing and Implementing a Data Science Solution on Azure.
Just to proof I actually passed this exam, here is a link to my digital certification.
Online Courses (25%)
Pluralsight
Microsoft Exam DP-100 : Designing and Implementing a Data Science Solution on Azure
Tip: Use this Pluralsight training benefits in Visual Studio subscriptions
Official Study Guide (15%)
Microsoft Learn
Learning Paths
Learning paths to gain the skills needed to become certified Exam DP-100: Designing and Implementing a Data Science Solution on Azure — Learn | Microsoft Docs
If you are new to Python or Machine Learning (ML), start with this Microsoft Learn track. Create machine learning models — Learn | Microsoft Docs. If you are already familiar with Python & ML, you can skip it.
In collaboration with the PyTorch team, Microsoft Learn is launching a new way for you to get familiar with machine learning. Learn in a Jupyter notebook — in the browser, without installing anything, for FREE! Get started with PyTorch Fundamentals
GitHub
Whether you’re a beginner at machine learning or not, this 12-week and 24-lesson curriculum of “Machine Learning for Beginners” is the absolute guide for your ML journey.
This course teaches you the basics of Classic ML without AI or neural networks, which will be covered by following curricula soon. microsoft/ML-For-Beginners: 12 weeks, 24 lessons, classic Machine Learning for all (github.com)
A comprehensive beginners’ cheat sheet on Python
Labs (10%)
GitHub
Practice Test Questions & Answers (35%)
mindhub™, a division of Pearson VUE™
DP-100: Designing and Implementing a Data Science Solution on Azure Microsoft Official Practice Test
Tip: Click on this link and complete the resulting steps to get 30% off your next Microsoft Certified Official Practice Test on Mindhub.com!
Sample Questions & Answers
Question 1
You train and register a model in your Azure Machine Learning workspace. You must publish a pipeline that enables client applications to use the model for batch inferencing. You must use a pipeline with a single ParallelRunStep step that runs a Python inferencing script to get predictions from the input data. You need to create the inferencing script for the ParallelRunStep pipeline step. Which two functions should you include? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
- A. run(mini_batch)
- B. main()
- C. batch()
- D. init()
- E. score(mini_batch)
Correct Answer: AD
Reference:
Question 2
You create a multi-class image classification deep learning model. You train the model by using PyTorch version 1.2. You need to ensure that the correct version of PyTorch can be identified for the inferencing environment when the model is deployed. What should you do?
- A. Save the model locally as a.pt file, and deploy the model as a local web service.
- B. Deploy the model on computer that is configured to use the default Azure Machine Learning conda environment.
- C. Register the model with a .pt file extension and the default version property.
- D. Register the model, specifying the model_framework and model_framework_version properties.
Correct Answer: D
- framework_version: The PyTorch version to be used for executing training code.
Reference: PyTorch Class
Question 3
You create an Azure Machine Learning compute resource to train models. The compute resource is configured as follows:
- Minimum nodes: 2
- Maximum nodes: 4
You must decrease the minimum number of nodes and increase the maximum number of nodes to the following values:
- Minimum nodes: 0
- Maximum nodes: 8
You need to reconfigure the compute resource. What are three possible ways to achieve this goal? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
- A. Use the Azure Machine Learning studio.
- B. Run the update method of the AmlCompute class in the Python SDK.
- C. Use the Azure portal.
- D. Use the Azure Machine Learning designer.
- E. Run the refresh_state() method of the BatchCompute class in the Python SDK.
Correct Answer: ABC
- A: You can manage assets and resources in the Azure Machine Learning studio.
- B: The update(min_nodes=None, max_nodes=None, idle_seconds_before_scaledown=None) of the AmlCompute class updates the ScaleSettings for this AmlCompute target.
- C: To change the nodes in the cluster, use the UI for your cluster in the Azure portal.
Reference: AmlCompute Class
I wish you luck in your journey to pass the Exam DP-100: Designing and Implementing a Data Science Solution on Azure. I hope this blog post inspires you or helps you by any means.
One thing I would like to ask you, if you pass this exam & you think this blog post helped you, please come back and let me know to share your achievement & success moment.
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