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NEW QUESTION: 1
What are the two business benefits of a BYOD solution?
A. simplification of network and security complexity
B. increased productivity using own devices
C. less IT effort to support and secure different operating systems
D. reduced OpEx
E. less IT effort to support and secure different devices
Answer: B,D
NEW QUESTION: 2
You are reviewing a thick client application and come upon File Injection findings in a function that opens zip files and extracts data from them, but the customer you are working with tells you that the data is sanitized using a method mySanitizer.validateZip(..). You confirm this and decideto remove this vulnerability and other File Injection findings with sanitized data using the Remove functionality of the Trace section in the Filter Editor.
What do you need to do in the Trace Rule Entry dialog to ensure that the rule you create applies only to this application's zip extractor and not all File Inclusion findings?
A. Specify Sink method name.
B. Specify File Inclusion as Source property.
C. Add validateZipO to the Prohibited Calls section.
D. Add validateZipO to the Required Calls section.
E. Specify File Inclusion as Sink property.
Answer: E
NEW QUESTION: 3
Amazon EC2 Container Serviceコンポーネントでは、タスクを配置できるコンテナインスタンスの論理グループの名前は何ですか？
A. コンテナインスタンス
B. クラスター
C. タスク定義
D. コンテナ
Answer: B
Explanation:
Amazon ECS contains the following components:
A Cluster is a logical grouping of container instances that you can place tasks on. A Container instance is an Amazon EC2 instance that is running the Amazon ECS agent and has been registered into a cluster.
A Task definition is a description of an application that contains one or more container definitions.
A Scheduler is the method used for placing tasks on container instances. A Service is an Amazon ECS service that allows you to run and maintain a specified number of instances of a task definition simultaneously.
A Task is an instantiation of a task definition that is running on a container instance. A Container is a Linux container that was created as part of a task.
Reference: http://docs.aws.amazon.com/AmazonECS/latest/developerguide/Welcome.html
NEW QUESTION: 4
You are with a time series dataset in Azure Machine Learning Studio.
You need to split your dataset into training and testing subsets by using the Split Data module.
Which splitting mode should you use?
A. Split Rows with the Randomized split parameter set to true
B. Regular Expression Split
C. Recommender Split
D. Relative Expression Split
Answer: A
Explanation:
Topic 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.