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NEW QUESTION: 1
대기업에는 많은 사업부가 있습니다. 각 사업부는 서로 다른 목적으로 여러 개의 AWS 계정을 가지고 있습니다. 회사의 CIO는 각 사업부에 회사의 다른 부분과 공유하는 데 유용한 데이터가 있음을 알았습니다. 총 1,000 개의 AWS 계정에서 사용자와 공유해야 하는 약 10PB의 데이터가 있습니다. 데이터는 독점적이므로 일부 작업은 특정 작업 유형의 사용자 만 사용할 수 있습니다.
일부 데이터는 시뮬레이션과 같은 집중적 인 워크로드의 처리량에 사용됩니다. 새로운 이니셔티브, 인수 및 매각으로 인해 계정 수가 자주 변경됩니다.
솔루션 아키텍트는 회사의 모든 직원과 AWS에서 사용할 데이터를 공유 할 수있는 시스템을 설계하라는 요청을 받았습니다.
확장 가능한 방식으로 데이터를 안전하게 공유 할 수있는 방법은 무엇입니까?
A. 단일 Amazon S3 버킷에 데이터를 저장하십시오. 각 사용자의 사업부 및 작업 유형에 따라 적절한 위치에서 읽기 및 쓰기 액세스 권한을 부여하는 버킷 정책을 작성하십시오. 버킷에 액세스하는 AWS 계정과 IAM 사용자 이름에 접두사가있는 작업 유형을 가진 사업부를 결정하십시오. 사용자는 S3 클라이언트가있는 사업부의 AWS 계정에서 IAM 자격 증명을 사용하여 데이터에 액세스 할 수 있습니다.
B. 데이터를 단일 Amazon S3 버킷에 저장합니다. S3 버킷의 객체 접두사를 기반으로 적절한 읽기 / 쓰기 액세스를 허용하는 모든 작업 유형 및 업무 부서 조합에 대해 IAM 역할을 생성하십시오. 역할에는 업무 부서의 AWS 계정이 역할을 맡을 수있는 신뢰 정책이 있어야 합니다. 각 업무 부서의 AWS 계정에서 IAM을 사용하여 다른 작업 유형에 대한 역할을 가정하지 않도록합니다.
사용자는 업무 부서의 AWS 계정에서 AssumeRole을 사용하여 데이터에 액세스 할 수있는 자격 증명을 얻습니다.
그런 다음 사용자는 해당 자격 증명을 S3 클라이언트와 함께 사용할 수 있습니다.
C. 일련의 Amazon S3 버킷에 데이터를 저장합니다. 회사의 자격 증명 공급자 (IdP)와 통합 된 AWS STS 토큰 자동 판매기를 만듭니다. 사용자가 로그인 할 때 토큰 자동 판매기에서 사용자의 액세스를 제한하는 역할을 담당하는 IAM 정책을 연결하거나 사용자에게 액세스 권한이있는 데이터 만 업로드하도록합니다. 사용자는 토큰 자동 판매기의 웹 사이트 또는 API를 인증하여 자격 증명을 얻은 다음 해당 자격 증명을 S3 클라이언트와 함께 사용할 수 있습니다.
D. 일련의 Amazon S3 버킷에 데이터를 저장합니다. Amazon EC2에서 실행되는 사용자를 인증하고 애플리케이션을 통해 데이터를 다운로드하거나 업로드 할 수 있는 회사의 자격 증명 공급자 (IdP)와 통합 된 애플리케이션을 생성합니다. 응용 프로그램은 IdP의 업무 부서 및 작업 유형 정보를 사용하여 사용자가 응용 프로그램을 통해 업로드 및 다운로드 할 수있는 대상을 제어합니다. 사용자는 애플리케이션의 API를 통해 데이터에 액세스 할 수 있습니다.
Answer: B

NEW QUESTION: 2



A. NO
B. YES
Answer: B

NEW QUESTION: 3
You need to implement a feature engineering strategy for the crowd sentiment local models.
What should you do?
A. Apply a linear discriminant analysis.
B. Apply a Pearson correlation coefficient.
C. Apply a Spearman correlation coefficient.
D. Apply an analysis of variance (ANOVA).
Answer: A
Explanation:
Explanation/Reference:
Explanation:
The linear discriminant analysis method works only on continuous variables, not categorical or ordinal variables.
Linear discriminant analysis is similar to analysis of variance (ANOVA) in that it works by comparing the means of the variables.
Scenario:
Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.
Experiments for local crowd sentiment models must combine local penalty detection data.
All shared features for local models are continuous variables.
Incorrect Answers:
B: The Pearson correlation coefficient, sometimes called Pearson's R test, is a statistical value that measures the linear relationship between two variables. By examining the coefficient values, you can infer something about the strength of the relationship between the two variables, and whether they are positively correlated or negatively correlated.
C: Spearman's correlation coefficient is designed for use with non-parametric and non-normally distributed data. Spearman's coefficient is a nonparametric measure of statistical dependence between two variables, and is sometimes denoted by the Greek letter rho. The Spearman's coefficient expresses the degree to which two variables are monotonically related. It is also called Spearman rank correlation, because it can be used with ordinal variables.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/fisher-linear- discriminant-analysis
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/compute-linear- correlation 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.