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Microsoft DP-100 (Designing and Implementing a Data Science Solution on Azure) Exam is a certification exam that focuses on assessing the skills and knowledge required to design and implement data science solutions using Azure technologies. DP-100 exam is ideal for professionals who want to demonstrate their expertise in the field of data science and enhance their career prospects.

Microsoft DP-100 (Designing and Implementing a Data Science Solution on Azure) Exam is a certification exam that measures a candidate's ability to design and implement data science solutions using Microsoft Azure technologies. DP-100 Exam is intended for data scientists, data engineers, and other professionals who work with data and want to validate their skills and knowledge in using Azure to solve data-related problems.

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DP-100 test questions: Designing and Implementing a Data Science Solution on Azure & DP-100 pass for sure

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q241-Q246):

NEW QUESTION # 241
You need to configure the Feature Based Feature Selection module based on the experiment requirements and datasets.
How should you configure the module properties? To answer, select the appropriate options in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/filter-based-feature-selection

NEW QUESTION # 242
You run an experiment that uses an AutoMLConfig class to define an automated machine learning task with a maximum of ten model training iterations. The task will attempt to find the best performing model based on a metric named accuracy.
You submit the experiment with the following code:
You need to create Python code that returns the best model that is generated by the automated machine learning task. Which code segment should you use?

Answer: A

Explanation:
The get_output method returns the best run and the fitted model.
Reference:
https://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/automated- machine-learning/classification/auto-ml-classification.ipynb

NEW QUESTION # 243
You have a dataset created for multiclass classification tasks that contains a normalized numerical feature set with 10,000 data points and 150 features.
You use 75 percent of the data points for training and 25 percent for testing. You are using the scikit-learn machine learning library in Python. You use X to denote the feature set and Y to denote class labels.
You create the following Python data frames:

You need to apply the Principal Component Analysis (PCA) method to reduce the dimensionality of the feature set to 10 features in both training and testing sets.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: PCA(n_components = 10)
Need to reduce the dimensionality of the feature set to 10 features in both training and testing sets.
Example:
from sklearn.decomposition import PCA
pca = PCA(n_components=2) ;2 dimensions
principalComponents = pca.fit_transform(x)
Box 2: pca
fit_transform(X[, y])fits the model with X and apply the dimensionality reduction on X.
Box 3: transform(x_test)
transform(X) applies dimensionality reduction to X.
References:
https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html

NEW QUESTION # 244
You have a dataset created for multiclass classification tasks that contains a normalized numerical feature set with 10,000 data points and 150 features.
You use 75 percent of the data points for training and 25 percent for testing. You are using the scikit-learn machine learning library in Python. You use X to denote the feature set and Y to denote class labels.
You create the following Python data frames:
You need to apply the Principal Component Analysis (PCA) method to reduce the dimensionality of the feature set to 10 features in both training and testing sets.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html

NEW QUESTION # 245
You deploy a real-time inference service for a trained model.
The deployed model supports a business-critical application, and it is important to be able to monitor the data submitted to the web service and the predictions the data generates.
You need to implement a monitoring solution for the deployed model using minimal administrative effort.
What should you do?

A. View the log files generated by the experiment used to train the model.
B. Create an ML Flow tracking URI that references the endpoint, and view the data logged by ML Flow.
C. Enable Azure Application Insights for the service endpoint and view logged data in the Azure portal.
D. View the explanations for the registered model in Azure ML studio.

Answer: C

Explanation:
Configure logging with Azure Machine Learning studio
You can also enable Azure Application Insights from Azure Machine Learning studio. When you're ready to deploy your model as a web service, use the following steps to enable Application Insights:
1. Sign in to the studio at https://ml.azure.com.
2. Go to Models and select the model you want to deploy.
3. Select +Deploy.
4. Populate the Deploy model form.
5. Expand the Advanced menu.
6. Select Enable Application Insights diagnostics and data collection.

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-enable-app-insights

NEW QUESTION # 246
......

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DP-100 VALID DUMPS | DP-100 RELIABLE TEST VOUCHER

DP-100 Valid Dumps | DP-100 Reliable Test Voucher