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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Designing and Implementing a Data Science Solution on Azure |
| Exam Number: | DP-100 |
| Passing Score: | 700 |
| Available Languages: | Italian, Russian, German, French, Arabic (Saudi Arabia), Korean, Spanish, Portuguese (Brazil), Chinese (Simplified), Japanese, English, Chinese (Traditional), Indonesian (Indonesia) |
| Real Exam Qty: | 40-60 |
| Exam Format: | Yes/No, Multiple select, Drag and drop, Multiple choice, Case studies |
| Exam Price: | $165 USD |
| Related Certifications: | Microsoft Certified: Azure Data Engineer Associate Microsoft Certified: Azure AI Engineer Associate |
| Exam Duration: | 100 minutes |
| Certificate Validity Period: | 1 year |
| Recommended Training: | Course DP-100T01-A: Designing and Implementing a Data Science Solution on Azure Microsoft Learn Learning Path |
| Exam Registration: | Pearson VUE Scheduling Microsoft Learn Registration |
| Sample Questions: | Microsoft DP-100 Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | No mandatory prerequisites; recommended knowledge: Azure fundamentals, Python programming, data science concepts, machine learning frameworks (Scikit-learn, PyTorch, Tensorflow) |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/dp-100 |
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The DP-100 Exam covers a range of topics related to data science, including data preparation, feature engineering, model training, and deployment. Candidates will be tested on their ability to use Azure services to build end-to-end machine learning solutions that can be deployed at scale.
NEW QUESTION # 452
You are performing feature scaling by using the scikit-learn Python library for x.1 x2, and x3 features.
Original and scaled data is shown in the following image.
Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: StandardScaler
The StandardScaler assumes your data is normally distributed within each feature and will scale them such that the distribution is now centred around 0, with a standard deviation of 1.
Example:
All features are now on the same scale relative to one another.
Box 2: Min Max Scaler
Notice that the skewness of the distribution is maintained but the 3 distributions are brought into the same scale so that they overlap.
Box 3: Normalizer
References:
http://benalexkeen.com/feature-scaling-with-scikit-learn/
NEW QUESTION # 453
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.
An IT department creates the following Azure resource groups and resources:
The IT department creates an Azure Kubernetes Service (AKS)-based inference compute target named aks-cluster in the Azure Machine Learning workspace.
You have a Microsoft Surface Book computer with a GPU. Python 3.6 and Visual Studio Code are installed.
You need to run a script that trains a deep neural network (DNN) model and logs the loss and accuracy metrics.
Solution: Attach the mlvm virtual machine as a compute target in the Azure Machine Learning workspace.
Install the Azure ML SDK on the Surface Book and run Python code to connect to the workspace. Run the training script as an experiment on the mlvm remote compute resource.
Answer: B
Explanation:
Explanation
Use the VM as a compute target.
Note: A compute target is a designated compute resource/environment where you run your training script or host your service deployment. This location may be your local machine or a cloud-based compute resource.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target
NEW QUESTION # 454
You have an Azure Machine learning workspace. The workspace contains a dataset with data in a tabular form.
You plan to use the Azure Machine Learning SDK for Python vl to create a control script that will load the dataset into a pandas dataframe in preparation for model training The script will accept a parameter designating the dataset You need to complete the script.
How should you complete the script? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 455
You have an Azure Machine Learning workspace. You are running an experiment on your local computer.
You need to ensure that you can use MLflow Tracking with Azure Machine Learning Python SDK v2 to store metrics and artifacts from your local experiment runs in the workspace.
In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
1 - Go to the workspace in the Azure portal.
2 - Retrieve the tracking URI of the workspace.
3 - Import MLflow and MLClient classes.
4 - Set the MLflow tracking URI and the experiment name.
NEW QUESTION # 456
You tram and register a model by using the Azure Machine Learning Python SDK v2 in a local workstation.
Python 3.7 and Visual Studio Code are instated on the workstation.
When you try to deploy the model into production to a Kubernetes online endpoint you experience an error in the scoring script that causes deployment to fail.
You need to debug the service on the local workstation before deploying the service to production.
Which three actions should you perform m sequence? To answer, move the appropriate actions from the list of actions from the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation
NEW QUESTION # 457
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