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Microsoft Designing and Implementing a Data Science Solution on Azure : DP-100
Exam Code: DP-100
Exam Name: Designing and Implementing a Data Science Solution on Azure
Updated: Oct 14, 2026
Q & A: 528 Questions and Answers
DP-100 Free Demo download
About Microsoft DP-100 Exam
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Microsoft DP-100 exam is designed for data scientists who want to demonstrate their ability to design and implement data science solutions on Microsoft Azure. DP-100 exam is an important step towards earning the Microsoft Certified: Azure Data Scientist Associate certification. It covers a range of topics including data exploration, data preparation, modeling, and deployment. Passing DP-100 exam requires a deep understanding of Azure services and tools, as well as strong data science skills.
Microsoft DP-100 certification exam is designed to test the skills and knowledge required to design and implement a data science solution on Azure. DP-100 exam is aimed at professionals who are involved in building and implementing data science solutions, including data scientists, data engineers, and machine learning engineers. Passing the exam demonstrates that the candidate has the skills and knowledge required to design and implement a data science solution on Azure.
Reference: https://www.microsoft.com/en-us/learning/exam-dp-100.aspx
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Microsoft DP-100 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Deploy and retrain models | 10-15% | - Monitor deployed models
|
| Prepare a model for deployment | 20-25% | - Deploy machine learning models
|
| Explore data and train models | 35-40% | - Optimize model performance
|
| Design and prepare a machine learning solution | 20-25% | - Design an Azure Machine Learning workspace
|
Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions:
Question #1
You are analyzing a raw dataset that requires cleaning.
You must perform transformations and manipulations by using Azure Machine Learning Studio.
You need to identify the correct modules to perform the transformations.
Which modules should you choose? To answer, drag the appropriate modules to the correct scenarios. Each module may be used once, more than once, or not at all.
You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Box 1: Clean Missing Data
Box 2: SMOTE
Use the SMOTE module in Azure Machine Learning Studio to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.
Box 3: Convert to Indicator Values
Use the Convert to Indicator Values module in Azure Machine Learning Studio. The purpose of this module is to convert columns that contain categorical values into a series of binary indicator columns that can more easily be used as features in a machine learning model.
Box 4: Remove Duplicate Rows
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/convert-to-indicator-values
Question #2
You have an Azure Machine Learning workspace.
You plan to tune a model hyperparameter when you train the model.
You need to define a search space that returns a normally distributed value.
Which parameter should you use?
- A. LogUniform
- B. QLogNormal
- C. QUniform
- D. Uniform
Correct Answer: C 🗳️
Question #3
You are using a Git repository to track work in an Azure Machine Learning workspace.
You need to authenticate a Git account by using SSH.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Correct Answer:
Explanation:
Authenticate your Git Account with SSH:
Step 1: Generating a public/private key pair
Generate a new SSH key
1. Open the terminal window in the Azure Machine Learning Notebook Tab.
2. Paste the text below, substituting in your email address.
ssh-keygen -t rsa -b 4096 -C " [email protected] "
This creates a new ssh key, using the provided email as a label.
> Generating public/private rsa key pair.
Step 2: Add the public key to the Git Account
In your terminal window, copy the contents of your public key file.
Step 3: Clone the Git repository by using an SSH repository URL
1. Copy the SSH Git clone URL from the Git repo.
2. Paste the url into the git clone command below, to use your SSH Git repo URL. This will look something like:
git clone [email protected]:GitUser/azureml-example.git
Cloning into ' azureml-example ' .
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-train-model-git-integration
Question #4
You manage an Azure Machine Learning workspace. You create an experiment named experiment1 by using the Azure Machine Learning Python SDK v2 and MLflow.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
Correct Answer:
Explanation:
Question #5
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are creating a model to predict the price of a student's artwork depending on the following variables: the student's length of education, degree type, and art form.
You start by creating a linear regression model.
You need to evaluate the linear regression model.
Solution: Use the following metrics: Accuracy, Precision, Recall, F1 score and AUC.
Does the solution meet the goal?
- A. Yes
- B. No
Correct Answer: B 🗳️
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