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NEW QUESTION: 1
あなたの会社には本社サイトと災害復旧サイトがあります。 2つのサイトは、冗長WANリンクを使用して互いに接続します。各サイトはインターネットに直接接続しています。各サイトには、インターネットからアクセスできる1つのExchange Server 2013サーバーがあります。
会社は©contoso.com Eメールアドレスサフィックスを使用します。インターネットから解決できるDNSレコードは、次の表に示すように構成されています。
本社サイトのインターネットリンクに障害が発生した場合、会社はインターネットからの受信電子メールを受信できません。
インターネットからの受信メールがServer1に配信されるようにする必要があります。 Server1が利用できない場合は、インターネットからの受信電子メールをServer2に配信する必要があります。
何を作るべきですか？
A. コストが20で、mail.contoso.comに解決されるMXレコード
B. mail.contoso.comという名前のAレコードで、131.107.101.1に解決されます。
C. コストが20で、server2.contoso.comに解決されるMXレコード
D. server2.contoso.comに解決されるmail.contoso.comという名前のCNAMEレコード
Answer: C
Explanation:
説明/参照：
Explanation:
CNAMEレコード
CNAMEレコードはCanonical Nameレコードの省略形で、ドメインネームシステム（DNS）のリソースレコードの一種で、ドメイン名が別の正規ドメイン名のエイリアスであることを指定します。ここで「標準的な」とは通常、より一般的に受け入れられているまたは標準の名前を意味します。
MXレコード
メールエクスチェンジャレコード（MXレコード）は、受信者のドメインに代わって電子メールメッセージを受け入れる責任を持つメールサーバーと、複数のメールサーバーがある場合にメール配信を優先するために使用される設定値を指定するドメインネームシステムのリソースレコードの一種です。ご利用いただけます。ドメイン名のMXレコードのセットは、SMTP（Simple Mail Transfer Protocol）を使用して電子メールをルーティングする方法を指定します。
記録
A（アドレス）レコードは、ドメイン名とホスト名を静的IPアドレスに指定するために使用できるDNSレコードです。
C：Server1がServer2よりも優先されますが、Server1が利用できない場合はServer2が使用されるように、Server2のMXレコードに関連するコストを増やす必要があります。
mail.contoso.comに関連付けられたServer1.contoso.comを指すCNAME（エイリアス）が既にあります
NEW QUESTION: 2
インシデントを削除するには、どの役割が必要ですか？
A. 管理者
B. itil_admin
C. itil
D. incident_manager
Answer: A
NEW QUESTION: 3
For this question, refer to the TerramEarth case study.
TerramEarth's 20 million vehicles are scattered around the world. Based on the vehicle's location its telemetry data is stored in a Google Cloud Storage (GCS) regional bucket (US. Europe, or Asia). The CTO has asked you to run a report on the raw telemetry data to determine why vehicles are breaking down after 100 K miles. You want to run this job on all the data. What is the most cost-effective way to run this job?
A. Launch a cluster in each region to preprocess and compress the raw data, then move the data into a regional bucket and use a Cloud Dataproc cluster .....
B. Launch a cluster in each region to preprocess and compress the raw data, then move the data into a multi region bucket and use a Dataproc cluster to finish the job.
C. Move all the data into 1 region, then launch a Google Cloud Dataproc cluster to run the job.
D. Move all the data into 1 zone, then launch a Cloud Dataproc cluster to run the job.
Answer: A
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