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最新更新DP-100下載|第一次嘗試輕鬆學習並通過考試和熱門的DP-100信息資訊

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=1p5FTxAyRhaJL6wYIXVA3UfMKX71LIUhF

在這裏我要說明的是這VCESoft一個有核心價值的問題，所有Microsoft的DP-100考試都是非常重要的，但在個資訊化快速發展的時代，VCESoft只是其中一個，為什麼大多數人選擇VCESoft，是因為VCESoft所提供的考題資料一定能幫助你通過測試，，為什麼呢，因為它提供的資料都是最新的培訓工具不斷更新，不斷變換的認證考試目標，為你提供最新的考試認證研究資料，有了VCESoft Microsoft的DP-100，你看到考試將會信心百倍，不用擔心任何考不過的風險，讓你毫不費力的獲得認證。

為了每位IT認證考試的考生切身利益，我們網站提供VCESoft Microsoft的DP-100考試培訓資料是根據考生的需要而定做的，由我們VCESoft資質深厚的IT專家專門研究出來的，他們的奮鬥結果不僅僅是為了幫助你們通過考試，而且是為了讓你們有一個更好的明天。

>> DP-100下載 <<

DP-100信息資訊 & DP-100題庫最新資訊

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最新的 Microsoft Azure DP-100 免費考試真題 (Q298-Q303):

問題 #298
You create a binary classification model to predict whether a person has a disease.
You need to detect possible classification errors.
Which error type should you choose for each description? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

答案：

解題說明：

Reference:
https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative

問題 #299
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 have a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load a training data file which is also located in the scripts folder.
You must run the script as an Azure ML experiment on a compute cluster named aml-compute.
You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named aml-compute that references the target compute cluster.
Solution: Run the following code:

Does the solution meet the goal?

A. Yes
B. No

答案：B

解題說明：
There is a missing line: conda_packages=['scikit-learn'], which is needed.
Correct example:
sk_est = Estimator(source_directory='./my-sklearn-proj',
script_params=script_params,
compute_target=compute_target,
entry_script='train.py',
conda_packages=['scikit-learn'])
Note:
The Estimator class represents a generic estimator to train data using any supplied framework.
This class is designed for use with machine learning frameworks that do not already have an Azure Machine Learning pre-configured estimator. Pre-configured estimators exist for Chainer, PyTorch, TensorFlow, and SKLearn.
Example:
from azureml.train.estimator import Estimator
script_params = {
# to mount files referenced by mnist dataset
'--data-folder': ds.as_named_input('mnist').as_mount(),
'--regularization': 0.8
}
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.estimator.estimator

問題 #300
You have an existing GitHub repository containing Azure Machine Learning project files.
You need to clone the repository to your Azure Machine Learning shared workspace file system.
Which four 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.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.

答案：

解題說明：

1 - From the terminal window in the Azure Machine Learning interface, run the git clone command.
2 - From the terminal window in the Azure Machine Learning interface, run the cat ~/ .ssh.id_rsa.pub command.
3 - From the terminal window in the Azure Machine Learning interface, run ssh-keygen command.
4 - Add a public key to the GitHub account.

問題 #301
You are building an experiment using the Azure Machine Learning designer.
You split a dataset into training and testing sets. You select the Two-Class Boosted Decision Tree as the algorithm.
You need to determine the Area Under the Curve (AUC) of the model.
Which three 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:
Step 1: Train Model
Two-Class Boosted Decision Tree
First, set up the boosted decision tree model.
1. Find the Two-Class Boosted Decision Tree module in the module palette and drag it onto the canvas.
2. Find the Train Model module, drag it onto the canvas, and then connect the output of the Two-Class Boosted Decision Tree module to the left input port of the Train Model module.
The Two-Class Boosted Decision Tree module initializes the generic model, and Train Model uses training data to train the model.
3. Connect the left output of the left Execute R Script module to the right input port of the Train Model module (in this tutorial you used the data coming from the left side of the Split Data module for training).
This portion of the experiment now looks something like this:

Step 2: Score Model
Score and evaluate the models
You use the testing data that was separated out by the Split Data module to score our trained models. You can then compare the results of the two models to see which generated better results.
Add the Score Model modules
1. Find the Score Model module and drag it onto the canvas.
2. Connect the Train Model module that's connected to the Two-Class Boosted Decision Tree module to the left input port of the Score Model module.
3. Connect the right Execute R Script module (our testing data) to the right input port of the Score Model module.

Step 3: Evaluate Model
To evaluate the two scoring results and compare them, you use an Evaluate Model module.
1. Find the Evaluate Model module and drag it onto the canvas.
2. Connect the output port of the Score Model module associated with the boosted decision tree model to the left input port of the Evaluate Model module.
3. Connect the other Score Model module to the right input port.

問題 #302
You have a binary classifier that predicts positive cases of diabetes within two separate age groups.
The classifier exhibits a high degree of disparity between the age groups.
You need to modify the output of the classifier to maximize its degree of fairness across the age groups and meet the following requirements:
* Eliminate the need to retrain the model on which the classifier is based.
* Minimize the disparity between true positive rates and false positive rates across age groups.
Which algorithm and panty constraint should you use? To answer, select the appropriate options in the answer are a. NOTE: Each correct selection is worth one point.

答案：

解題說明：

問題 #303
......

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DP-100信息資訊: https://www.vcesoft.com/DP-100-pdf.html

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P.S. VCESoft在Google Drive上分享了免費的、最新的DP-100考試題庫：https://drive.google.com/open?id=1p5FTxAyRhaJL6wYIXVA3UfMKX71LIUhF

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