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NEW QUESTION: 1







Which statement about the router configurations is correct?
A. Tunnel keepalives are not configured for the tunnel0 interface on Branch2 and R2.
B. The Branch2 LAN network 192.168.11 0/24 is not advertised into the EIGRP network.
C. PPP PAP is authentication configured between Branch2 and R1.
D. PPP CHAP is authentication configured between Branch1 and R1.
E. The Branch3 LAW network 192.168.10.0/24 is not advertised into the EIGRP network.
Answer: E
Explanation:
When we check Branch3 router we notice that "network 192.168.10.0" command is missing under
"router eigrp 100"

NEW QUESTION: 2
You have an Azure SQL database named DB1.
You need to ensure that DB1 will support automatic failover without data loss if a datacenter fails. The solution must minimize costs.
Which deployment option and pricing tier should you configure?
A. Azure SQL Database Standard
B. Azure SQL Database Premium
C. Azure SQL Database managed instance Business Critical
D. Azure SQL Database serverless
E. This feature is not available in SQL Managed Instance.
Answer: B
Explanation:
By default, the cluster of nodes for the premium availability model is created in the same datacenter. With the introduction of Azure Availability Zones, SQL Database can place different replicas of the Business Critical database to different availability zones in the same region. To eliminate a single point of failure, the control ring is also duplicated across multiple zones as three gateway rings (GW). The routing to a specific gateway ring is controlled by Azure Traffic Manager (ATM). Because the zone redundant configuration in the Premium or Business Critical service tiers does not create additional database redundancy, you can enable it at no extra cost. By selecting a zone redundant configuration, you can make your Premium or Business Critical databases resilient to a much larger set of failures, including catastrophic datacenter outages, without any changes to the application logic. You can also convert any existing Premium or Business Critical databases or pools to the zone redundant configuration.
Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/database/high-availability-sla
Topic 1, Contoso Ltd
To start the case study
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.
Overview
Existing Environment
Contoso, Ltd. is a financial data company that has 100 employees. The company delivers financial data to customers.
Active Directory
Contoso has a hybrid Azure Active Directory (Azure AD) deployment that syncs to on-premises Active Directory.
Database Environment
Contoso has SQL Server 2017 on Azure virtual machines shown in the following table.

SQL1 and SQL2 are in an Always On availability group and are actively queried. SQL3 runs jobs, provides historical data, and handles the delivery of data to customers.
The on-premises datacenter contains a PostgreSQL server that has a 50-TB database.
Current Business Model
Contoso uses Microsoft SQL Server Integration Services (SSIS) to create flat files for customers. The customers receive the files by using FTP.
Requirements
Planned Changes
Contoso plans to move to a model in which they deliver data to customer databases that run as platform as a service (PaaS) offerings. When a customer establishes a service agreement with Contoso, a separate resource group that contains an Azure SQL database will be provisioned for the customer. The database will have a complete copy of the financial data. The data to which each customer will have access will depend on the service agreement tier. The customers can change tiers by changing their service agreement.
The estimated size of each PaaS database is 1 TB.
Contoso plans to implement the following changes:



Business Goals
Contoso identifies the following business requirements:







Technical Requirements
Contoso identifies the following technical requirements:




Monitoring Requirements
Contoso identifies the following monitoring requirements:




PaaS Prototype
During prototyping of the PaaS solution in Azure, you record the compute utilization of a customer's Azure SQL database as shown in the following exhibit.

Role Assignments
For each customer's Azure SQL Database server, you plan to assign the roles shown in the following exhibit.

NEW QUESTION: 3
注：この質問は、同じシナリオを提示する一連の質問の一部です。シリーズの各質問には、述べられた目標を達成する可能性のある独自の解決策が含まれています。一部の質問セットには複数の正しい解決策がある場合がありますが、他の質問セットには正しい解決策がない場合があります。
このセクションの質問に回答した後は、その質問に戻ることはできません。その結果、これらの質問はレビュー画面に表示されません。
Pythonスクリプトを使用して、Azure MachineLearningの実験を実行することを計画しています。スクリプトは、実験実行コンテキストへの参照を作成し、ファイルからデータをロードし、ラベル列の一意の値のセットを識別して、実験実行を完了します。
azureml.coreからインポート実行
パンダをpdとしてインポートする
run = Run.get_context（）
データ= pd.read_csv（ 'data.csv'）
label_vals = data ['label']。unique（）
＃ここに指標を記録するコードを追加
run.complete（）
実験では、後で確認できる実行のメトリックとして、データ内の一意のラベルを記録する必要があります。
コメントで示されたポイントで実行メトリックとして一意のラベル値を記録するには、スクリプトにコードを追加する必要があります。
解決策：コメントを次のコードに置き換えます。
run.upload_file（ 'outputs / labels.csv'、 '。/ data.csv'）
ソリューションは目標を達成していますか？
A. はい
B. いいえ
Answer: B
Explanation:
Explanation
label_vals has the unique labels (from the statement label_vals = data['label'].unique()), and it has to be logged.
Note:
Instead use the run_log function to log the contents in label_vals:
for label_val in label_vals:
run.log('Label Values', label_val)
Reference:
https://www.element61.be/en/resource/azure-machine-learning-services-complete-toolbox-ai

NEW QUESTION: 4
The company wants all opportunities associated with an account to be added in and displayed on the account record detail page. Is it possible?
A. Yes, custom summary field
B. Yes, roll up summary field
C. Yes, workflow rule
D. No, accounts cannot be edited
Answer: B