從Google Drive中免費下載最新的VCESoft DP-100 PDF版考試題庫：https://drive.google.com/open?id=1Axo8T3gpmer5ybtUuSc-YXXCqC4R3J-z
每每談及到 VCESoft 網站的 DP-100 考題，很多人都稱贊其出題率是很高的，讓許多人的 Microsoft 證照之路沒有後顧之憂。“萬事俱備，只欠東風。”如果你沒有最新的 DP-100 考題作參照，再多的努力，是沒有用的，畢竟我們的 DP-100 考題可以作為真實考題題型的參照，讓大家順利進入了理想的單位。
| Section | Objectives |
|---|---|
| Deploy and consume models | - Deploy models to endpoints - Monitor deployed models and endpoints |
| Optimize and manage models | - Improve model performance - Track experiments and manage model lifecycle |
| Explore and analyze data | - Ingest and prepare data for modeling - Perform exploratory data analysis |
| Train machine learning models | - Tune hyperparameters and evaluate models - Train models using Azure Machine Learning |
| Design and prepare a machine learning solution | - Manage compute and data assets - Plan and configure Azure Machine Learning workspace - Select appropriate Azure services for machine learning workloads |
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問題 #359
A biomedical research company plans to enroll people in an experimental medical treatment trial.
You create and train a binary classification model to support selection and admission of patients to the trial.
The model includes the following features: Age, Gender, and Ethnicity.
The model returns different performance metrics for people from different ethnic groups.
You need to use Fairlearn to mitigate and minimize disparities for each category in the Ethnicity feature.
Which technique and constraint should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
Explanation
Graphical user interface, text, application, chat or text message Description automatically generated
Box 1: Grid Search
Fairlearn open-source package provides postprocessing and reduction unfairness mitigation algorithms:
ExponentiatedGradient, GridSearch, and ThresholdOptimizer.
Note: The Fairlearn open-source package provides postprocessing and reduction unfairness mitigation algorithms types:
Reduction: These algorithms take a standard black-box machine learning estimator (e.g., a LightGBM model) and generate a set of retrained models using a sequence of re-weighted training datasets.
Post-processing: These algorithms take an existing classifier and the sensitive feature as input.
Box 2: Demographic parity
The Fairlearn open-source package supports the following types of parity constraints: Demographic parity, Equalized odds, Equal opportunity, and Bounded group loss.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml
問題 #360
You need to visually identify whether outliers exist in the Age column and quantify the outliers before the outliers are removed.
Which three Azure Machine Learning Studio modules should you use in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.
答案：
解題說明：
Explanation
Create Scatterplot
Summarize Data
Clip Values
You can use the Clip Values module in Azure Machine Learning Studio, to identify and optionally replace data values that are above or below a specified threshold. This is useful when you want to remove outliers or replace them with a mean, a constant, or other substitute value.
References:
https://blogs.msdn.microsoft.com/azuredev/2017/05/27/data-cleansing-tools-in-azure-machine-learning/
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clip-values
問題 #361
YOU have a data-set that contains over 150 features. You use the dataset to train a Support Vector Machine (SVM) binary classifirer.
You need to use the Permutation Feature Importance module in Azure Machine Learning Studio to compute a set of feature importance scores for the dataset.
In which order should you perform the actions? To answer move al actions from from the list of Actions to the answer area and arrange them in the correct order.
答案：
解題說明：
問題 #362
You need to set up the Permutation Feature Importance module according to the model training requirements.
Which properties should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
答案：
解題說明：
Explanation:
Box 1: Accuracy
Scenario: You want to configure hyperparameters in the model learning process to speed the learning phase by using hyperparameters. In addition, this configuration should cancel the lowest performing runs at each evaluation interval, thereby directing effort and resources towards models that are more likely to be successful.
Box 2: R-Squared
問題 #363
You monitor an Azure Machine Learning classification training experiment named train-classification on Azure Notebooks.
You must store a table named table as an artifact in Azure Machine Learning Studio during model training.
You need to collect and list the metrics by using MLfow.
how should you complete the code segment? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth on* point.
答案：
解題說明：
Explanation:
問題 #364
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DP-100考古題更新: https://www.vcesoft.com/DP-100-pdf.html
此外，這些VCESoft DP-100考試題庫的部分內容現在是免費的：https://drive.google.com/open?id=1Axo8T3gpmer5ybtUuSc-YXXCqC4R3J-z