Blog - Passiveearningit

Chris Martin Chris Martin

0 Course Enrolled • 0 Course Completed

Biography

최신버전DP-100합격보장가능덤프공부공부문제

한번에Microsoft인증DP-100시험을 패스하고 싶으시다면 완전 페펙트한 준비가 필요합니다. 완벽한 관연 지식터득은 물론입니다. 우리Pass4Test의 자료들은 여러분의 이런 시험준비에 많은 도움이 될 것입니다.

Microsoft DP-100 자격증 시험은 Azure 기술과 관련하여 데이터 전문가가 데이터 과학 솔루션을 설계하고 구현하는 능력을 검증하고자 하는 경우 유용한 자격증입니다. 시험은 데이터 과학 및 Azure 서비스와 관련된 다양한 주제를 다루며, 후보자는 교육 과정을 수강하고 온라인 자료에 접근하여 시험 준비를 할 수 있습니다. DP-100 시험을 통과하는 것은 데이터 전문가가 진로를 발전시키고 잠재적인 고용주에게 자신의 전문성을 증명하는 데 큰 도움이 됩니다.

>> DP-100합격보장 가능 덤프공부 <<

Microsoft DP-100유효한 덤프공부 & DP-100덤프문제은행

Pass4Test의 Microsoft인증 DP-100시험덤프는 실제시험의 기출문제와 예상문제를 묶어둔 공부자료로서 시험문제커버율이 상당히 높습니다.IT업계에 계속 종사하려는 IT인사들은 부단히 유력한 자격증을 취득하고 자신의 자리를 보존해야 합니다. Pass4Test의 Microsoft인증 DP-100시험덤프로 어려운 Microsoft인증 DP-100시험을 쉽게 패스해보세요. IT자격증 취득이 여느때보다 여느일보다 쉬워져 자격증을 많이 따는 꿈을 실현해드립니다.

Microsoft DP-100 (Azure에서 데이터 과학 솔루션 설계 및 구현) 시험은 Microsoft Azure Technologies를 사용하여 후보자의 데이터 과학 솔루션을 설계하고 구현할 수있는 후보자의 능력을 측정하는 인증 시험입니다. 이 시험은 데이터 과학자, 데이터 엔지니어 및 데이터를 사용하고 데이터 관련 문제를 해결하기 위해 Azure를 사용하는 기술과 지식을 검증하려는 다른 전문가를위한 것입니다.

최신 Microsoft Azure DP-100 무료샘플문제 (Q46-Q51):

질문 # 46
You create an Azure Machine Learning pipeline named pipeline1 with two steps that contain Python scripts.
Data processed by the first step is passed to the second step.
You must update the content of the downstream data source of pipeline1 and run the pipeline again You need to ensure the new run of pipeline1 fully processes the updated content.
Solution: Set the allow_reuse parameter of the PythonScriptStep object of both steps to False Does the solution meet the goal?

A. Yes
B. No

정답：B

질문 # 47
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.

정답：

설명：

Reference:
https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py

질문 # 48
You have an Azure blob container that contains a set of TSV files. The Azure blob container is registered as a datastore for an Azure Machine Learning service workspace. Each TSV file uses the same data schema.
You plan to aggregate data for all of the TSV files together and then register the aggregated data as a dataset in an Azure Machine Learning workspace by using the Azure Machine Learning SDK for Python.
You run the following code.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

정답：

설명：

Explanation:

Box 1: No
FileDataset references single or multiple files in datastores or from public URLs. The TSV files need to be parsed.
Box 2: Yes
to_path() gets a list of file paths for each file stream defined by the dataset.
Box 3: Yes
TabularDataset.to_pandas_dataframe loads all records from the dataset into a pandas DataFrame.
TabularDataset represents data in a tabular format created by parsing the provided file or list of files.
Note: TSV is a file extension for a tab-delimited file used with spreadsheet software. TSV stands for Tab Separated Values. TSV files are used for raw data and can be imported into and exported from spreadsheet software. TSV files are essentially text files, and the raw data can be viewed by text editors, though they are often used when moving raw data between spreadsheets.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset

질문 # 49
You have a Jupyter Notebook that contains Python code that is used to train a model.
You must create a Python script for the production deployment. The solution must minimize code maintenance.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. Refactor the Jupyter Notebook code into functions
B. Save each function to a separate Python file
C. Define a main() function in the Python script
D. Remove all comments and functions from the Python script

정답：A,C

설명：
Reference:
https://www.guru99.com/learn-python-main-function-with-examples-understand-main.html
https://towardsdatascience.com/from-jupyter-notebook-to-deployment-a-straightforward-example-1838c203a437

질문 # 50
You need to implement a scaling strategy for the local penalty detection data.
Which normalization type should you use?

A. Weight
B. Cosine
C. Streaming
D. Batch

정답：D

설명：
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 Prepare data for modeling Testlet 2 Case study 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, with the following columns:

The two datasets have been added to Azure Machine Learning Studio as separate datasets and included as the starting point of the experiment.
Dataset issues
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 dataset also contains 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. An initial investigation showed 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. 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-parameters statistics to measure the relationships.
You must use a feature selection algorithm to analyze the relationship between the MedianValue and AvgRoomsinHouse columns.
Model training
Given a trained model and a test dataset, you need to compute the permutation feature importance scores of feature variables. You need to set up the Permutation Feature Importance module to select the correct metric to investigate the model's accuracy and replicate the findings.
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.
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, you need to 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. 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. The data that identifies that a property is near a river is held in the column named NextToRiver. You want to complete this task before the data goes through the sampling process.
When you train a Linear Regression module using a property dataset that shows data for property prices for a large city, you need to 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. You must ensure that the distribution of the features across multiple training models is 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.
Prepare data for modeling
Question Set 3

질문 # 51
......

DP-100유효한 덤프공부: https://www.pass4test.net/DP-100.html