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NEW QUESTION: 1
Your customer wants to run a report to review account balances for both inventory valuation and cost of goods sold.
Which two Oracle Transactional Business Intelligence reports would you run so the customer can review these balances?
A. Costing Balances Report
B. COGS Account Balances Report
C. Inventory Valuation Report
D. Revenue and COGS Matching Report
E. Inventory Account Balances Report
Answer: E

NEW QUESTION: 2
Azure Machine Learning Studioを使用して、2つのデータセットが互いに著しく異なるかどうかを判断しています。
1つのデータセットの推定値は、他のデータセットの参照値よりも大きい場合も小さい場合もあります。定数を持つ分布を作成する必要があります。相関の関数としてのタイプIエラー。
ディストリビューションを作成する必要があります。
どのタイプの配布を作成する必要がありますか？
A. 片側オプション付きの対応のあるt検定
B. 片側オプション付きの対応のないt検定
C. ツーテールオプションを使用した対応のないt検定
D. 両側オプション付きの対応のあるt検定
Answer: D
Explanation:
Explanation
Choose a one-tail or two-tail test. The default is a two-tailed test. This is the most common type of test, in which the expected distribution is symmetric around zero.
Example: Type I error of unpaired and paired two-sample t-tests as a function of the correlation. The simulated random numbers originate from a bivariate normal distribution with a variance of 1.

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/test-hypothesis-using-t-test
https://en.wikipedia.org/wiki/Student%27s_t-test
Topic 1, Case Study 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.

NEW QUESTION: 3
HOTSPOT
You need to design the mobile service storage architecture for NorthRideFinder.
Which solutions should you recommend? To answer, select the appropriate solutions in the answer area.

Answer:
Explanation:

From scenario: NorthRideFinder App
Northwind Electric Cars needs a customer-facing website and mobile app that allows customers to schedule pickups. Customers should also be able to create profiles that will help ensure the customer gets a ride faster by storing customer information.
Topic 6, Fourth Coffee Background You are the new cloud architect for Fourth Coffee. I he company hosts an on-premises ASP.NET MVC web application to allow online purchases and to support their retail store operations.
The new chief information officer (CIO) has announced several initiatives for the new year, including a new mobile application, online training for retail store employees, and moving the current web application and other services to the cloud.
The marketing team hopes to see an increase in the up-time for the web application. The team would also like to allow users to use social-Nogms in addition to the current username and password system.
Fourth Coffee has chosen Microsoft Azure to support their initiatives.
Current environment
In the Azure portal, you create an Azure Mobile App for the API. You create a Service Bus queue in Azure and install the Azure Storage SDK for Nodejs.
Problem statements
The mobile team attempts to use continuous deployment with the Azure App Service and the new API project. They receive the following error message: "Unable to access
'http://fourthcoffeeapi.azurewebsites.net/': Failed to connect to
https://fourthcoffeeapi.scm.azurewebsites.net/"
Business requirement
Web Application
*You must increase up-time for the application.
*The application must support additional regions and languages.
*Marketing must be able to validate the web application before updates to the application are published to the production environment.
Mobile
*The marketing team must be able to send frequent and timely updates to specific users and devices including Apple iPad. iPhone, Android. Windows, and Windows Phone devices.
*Users must be able to use their social accounts to sign in to the application. You must support Linkedln, Facebook and Google logons.
*The application must remain responsive, even during peak periods.
Training
Video streaming content must be made available and streamed to employee's browsers.
Training content must only include on-demand streaming. There will be no live content.
Technical requirement
Web Application
*You must update the deployment process to support cloud deployments.
*All data must be formatted as JSON during transport.
*You must implement Team Foundation Version Control (TFVC) as the version control system for the web application.
*Incoming messages to the API must be persisted to queue storage to ensure they are delivered and processed. You must restrict the size of messages between the mobile app and the API to no more than 5 gigabytes (GB).
*The web application must use geo-redundant replication.
Mobile
*You must use Node.js as a technology platform. You must support all mobile initiatives when possible.
*You must implement Git as the version control system for the mobile app.
*You must develop a REST API by using Node.js. Express, and MongoDB. You must use the Mobile Apps feature of the Azure App Service to host the API in Standard mode.
*You must implement the following Push Notification Services by using Azure Media Services:
*Apple Push Notification Service (APNS) for iPad and iPhone devices
*Google Cloud Messaging service (GCM) for Android devices
*Windows Notification Service (WNS) for Windows devices
*Microsoft Push Notification Service (MPNS) for Windows Phone devices
Security and Disaster Recovery
*You must integrate the on-premises Active Directory Domain Services with Azure Active Directory (Azure AD).
*You must implement the latest federated identity standards to provide authentication and authorization to applications.
*You must implement Multi-Factor Authentication.
*The web application and the API must be able to recover from a disaster.
Scaling
The web application and API must auto-scale according to the following rules:
*Scale up by one instance if CPU is above 70%.
*Scale down by one instance if CPU is below 50%.
Training
*Streaming must include Content Delivery Network (CDN) capabilities to support global locations.
*Content must be encrypted and protected by using AES and PlayReady.
*Streaming must include one gigabit (GB) per second of dedicated egress capacity.
*All videos must use adaptive bitrate MP4 encoded content and include a streaming manifest file (.ism).
*You must support the following streaming formats for video files: MPEG DASH, HI_S, Smooth Streaming, HDS. You must not need to re-encode the content.