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NEW QUESTION: 1
The user information from SuccessFactors HCM will be downloaded to a file that the SuccessFactors
Learning connectors will use.
What file naming convention must be used to set up this job?
Please choose the correct answer.
Response:
A. Employee_data.csv
B. user_data.txt
C. user_data_XXX.txt, where XXX is the tenant ID
D. XXX_user_data.txt, where XXX is the tenant ID
Answer: C

NEW QUESTION: 2
クリーニングが必要な生のデータセットを分析しています。
Azure Machine Learning Studioを使用して、変換と操作を実行する必要があります。
変換を実行するには、正しいモジュールを識別する必要があります。
どのモジュールを選択する必要がありますか？答えるには、適切なモジュールを正しいシナリオにドラッグします。各モジュールは、1回、複数回使用することも、まったく使用しないこともできます。
コンテンツを表示するには、ペイン間で分割バーをドラッグするか、スクロールする必要がある場合があります。
注：それぞれの正しい選択には1ポイントの価値があります。

Answer:
Explanation:

Explanation:
Box 1: Clean Missing Data
Box 2: SMOTE
Use the SMOTE module in Azure Machine Learning Studio to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.
Box 3: Convert to Indicator Values
Use the Convert to Indicator Values module in Azure Machine Learning Studio. The purpose of this module is to convert columns that contain categorical values into a series of binary indicator columns that can more easily be used as features in a machine learning model.
Box 4: Remove Duplicate Rows
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/convert-to-indicator-values

NEW QUESTION: 3
Azureサービスを正しい定義に一致させます。
手順：回答するには、適切なAzureサービスを左側の列から右側の説明にドラッグします。 各サービスは、1回、複数回、またはまったく使用されません。
注：それぞれの正しい選択には1ポイントの価値があります。

Answer:
Explanation:

Explanation

Box 1:
Azure Functions provides the platform for serverless code.
Azure Functions is a serverless compute service that lets you run event-triggered code without having to explicitly provision or manage infrastructure.
Box 2:
Azure Databricks is a big analysis service for machine learning.
Azure Databricks is an Apache Spark-based analytics platform. The platform consists of several components including 'MLib'. Mlib is a Machine Learning library consisting of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as underlying optimization primitives.
Box 3:
Azure Application Insights detects and diagnoses anomalies in web apps.
Application Insights, a feature of Azure Monitor, is an extensible Application Performance Management (APM) service for developers and DevOps professionals. Use it to monitor your live applications. It will automatically detect performance anomalies, and includes powerful analytics tools to help you diagnose issues and to understand what users actually do with your app.
Box 4:
Azure App Service hosts web apps.
Azure App Service is an HTTP-based service for hosting web applications, REST APIs, and mobile back ends.
You can develop in your favorite language, be it .NET, .NET Core, Java, Ruby, Node.js, PHP, or Python.
Applications run and scale with ease on both Windows and Linux-based environments.
References:
https://docs.microsoft.com/en-us/azure/azure-functions/
https://docs.microsoft.com/en-us/azure/azure-databricks/what-is-azure-databricks#apache-spark-based-analytics-
https://docs.microsoft.com/en-us/azure/azure-monitor/app/app-insights-overview
https://docs.microsoft.com/en-us/azure/app-service/overview

NEW QUESTION: 4
What is the default posture status for non-agent capable devices, such as Linux and iDevices?
A. Default
B. Compliant
C. Unknown
D. Validated
Answer: B