Azure数据科学解决方案设计与实现DP-100考试大纲

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"DP-100考试大纲主要涉及的是Microsoft Azure上的数据科学解决方案设计与实施，该考试专注于在Azure平台上进行容器技术（如Docker）的相关知识。考生需要了解和掌握的基础技能包括确保工作坊参与者能够在其设备上安装和使用Docker。题目中提到的预置要求对于成功进行Docker在Windows环境中的工作坊至关重要。正确答案选项C，硬件辅助虚拟化（Hardware-Assisted Virtualization），确保Windows系统支持这种技术并启用虚拟化功能。这是因为Docker依赖于底层虚拟化技术来创建和管理容器，如果硬件支持缺失，可能会影响Docker的运行效率。选项E，Windows 10 64-bit Professional版本，是因为64位操作系统是运行Docker的最低要求，Windows 7或更高版本且为64位才能支持复杂的软件和容器化应用。选项A，Microsoft Hardware-Assisted Virtualization Detection Tool，虽然提到了检测工具，但并不是安装在设备上的必要组件，而是用来检查系统是否具备硬件虚拟化能力的。选项B，Kitematic是一款用户友好的Docker客户端，但它不是设备上必须预先安装的组件，而是用于管理和操作Docker容器的软件。选项D，VirtualBox虽然也是一款虚拟化软件，但DP-100考试大纲明确指出，考生需要的是在Windows环境下安装Docker，而非其他虚拟机环境。因此，在准备DP-100考试时，考生应确保他们的Windows设备满足硬件虚拟化支持，并安装Windows 10 64-bit Professional版本，以便能够顺利进行Docker的开发和教学工作。同时，推荐使用官方文档、Microsoft Azure官方指南以及Docker的官方文档作为学习资源，以确保对最新版本的DP-100考试内容有全面理解。"

QUESTION 1

You need to implement a scaling strategy for the local penalty detection data.

Which normalization type should you use?

A. Streaming

B. Weight

C. Batch

D. Cosine

Correct Answer: C

Section: [none]

Explanation

Explanation/Reference:

Explanation:

Post batch normalization statistics (PBN) is the Microsoft Cognitive Toolkit (CNTK) version of how to evaluate

the population mean and variance of Batch Normalization which could be used in inference Original Paper.

In CNTK, custom networks are defined using the BrainScriptNetworkBuilder and described in the CNTK

network description language "BrainScript."

Scenario:

Local penalty detection models must be written by using BrainScript.

References:

https://docs.microsoft.com/en-us/cognitive-toolkit/post-batch-normalization-statistics

QUESTION 2

HOTSPOT

You need to use the Python language to build a sampling strategy for the global penalty detection models.

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.

Hot Area:

Correct Answer:

Section: [none]

Explanation

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Testlet 2

Case study

This is a case study. Case studies are not timed separately. You can use as much exam time as you would like

to complete each case. However, there may be additional case studies and sections on this exam. You must

manage your time to ensure that you are able to complete all questions included on this exam in the time

provided.

To answer the questions included in a case study, you will need to reference information that is provided in the

case study. Case studies might contain exhibits and other resources that provide more information about the

scenario that is described in the case study. Each question is independent of the other questions in this case

study.

At the end of this case study, a review screen will appear. This screen allows you to review your answers and

to make changes before you move to the next section of the exam. After you begin a new section, you cannot

return to this section.

To start the case study

To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore

the content of the case study before you answer the questions. Clicking these buttons displays information

such as business requirements, existing environment, and problem statements. If the case study has an All

Information tab, note that the information displayed is identical to the information displayed on the subsequent

tabs. When you are ready to answer a question, click the Question button to return to the question.

Overview

You are a data scientist for Fabrikam Residences, a company specializing in quality private and commercial

property in the United States. Fabrikam Residences is considering expanding into Europe and has asked you

to investigate prices for private residences in major European cities.

You use Azure Machine Learning Studio to measure the median value of properties. You produce a regression

model to predict property prices by using the Linear Regression and Bayesian Linear Regression modules.

Datasets

There are two datasets in CSV format that contain property details for two cities, London and Paris. You add

both files to Azure Machine Learning Studio as separate datasets to the starting point for an experiment. Both

datasets contain the following columns:

Free Dump − Don’t pay for it

An initial investigation shows that the datasets are identical in structure apart from the MedianValue column.

The smaller Paris dataset contains the MedianValue in text format, whereas the larger London dataset

contains the MedianValue in numerical format.

Data issues

Missing values

The AccessibilityToHighway column in both datasets contains missing values. The missing data must be

replaced with new data so that it is modeled conditionally using the other variables in the data before filling in

the missing values.

Columns in each dataset contain missing and null values. The datasets also contain many outliers. The Age

column has a high proportion of outliers. You need to remove the rows that have outliers in the Age column.

The MedianValue and AvgRoomsInHouse columns both hold data in numeric format. You need to select a

feature selection algorithm to analyze the relationship between the two columns in more detail.

Model fit

The model shows signs of overfitting. You need to produce a more refined regression model that reduces the

overfitting.

Experiment requirements

You must set up the experiment to cross-validate the Linear Regression and Bayesian Linear Regression

modules to evaluate performance. In each case, the predictor of the dataset is the column named

MedianValue. You must ensure that the datatype of the MedianValue column of the Paris dataset matches the

structure of the London dataset.

You must prioritize the columns of data for predicting the outcome. You must use non-parametric statistics to

measure relationships.

You must use a feature selection algorithm to analyze the relationship between the MedianValue and

AvgRoomsInHouse columns.

Model training

Permutation Feature Importance

Given a trained model and a test dataset, you must compute the Permutation Feature Importance scores of

feature variables. You must be determined the absolute fit for the model.

Hyperparameters

You must configure hyperparameters in the model learning process to speed the learning phase. 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.

You are concerned that the model might not efficiently use compute resources in hyperparameter tuning. You

also are concerned that the model might prevent an increase in the overall tuning time. Therefore, must

implement an early stopping criterion on models that provides savings without terminating promising jobs.

Testing

You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in

Azure Machine Learning Studio.

Free Dump − Don’t pay for it

Cross-validation

You must create three equal partitions for cross-validation. You must also configure the cross-validation

process so that the rows in the test and training datasets are divided evenly by properties that are near each

city’s main river. You must complete this task before the data goes through the sampling process.

Linear regression module

When you train a Linear Regression module, you must determine the best features to use in a model. You can

choose standard metrics provided to measure performance before and after the feature importance process

completes. The distribution of features across multiple training models must be consistent.

Data visualization

You need to provide the test results to the Fabrikam Residences team. You create data visualizations to aid in

presenting the results.

You must produce a Receiver Operating Characteristic (ROC) curve to conduct a diagnostic test evaluation of

the model. You need to select appropriate methods for producing the ROC curve in Azure Machine Learning

Studio to compare the Two-Class Decision Forest and the Two-Class Decision Jungle modules with one

another.

QUESTION 1

HOTSPOT

You need to replace the missing data in the AccessibilityToHighway columns.

How should you configure the Clean Missing Data module? To answer, select the appropriate options in the

answer area.

NOTE: Each correct selection is worth one point.

Hot Area:

Correct Answer:

Section: [none]

Explanation

Explanation/Reference:

Explanation:

Box 1: Replace using MICE

Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a

method described in the statistical literature as "Multivariate Imputation using Chained Equations" or "Multiple

Imputation by Chained Equations". With a multiple imputation method, each variable with missing data is

modeled conditionally using the other variables in the data before filling in the missing values.

Scenario: The AccessibilityToHighway column in both datasets contains missing values. The missing data

must be replaced with new data so that it is modeled conditionally using the other variables in the data before

filling in the missing values.

Box 2: Propagate

Cols with all missing values indicate if columns of all missing values should be preserved in the output.

References:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data

QUESTION 2

DRAG DROP

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