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NEW QUESTION: 1
Refer to the exhibit.

R1 is configured as an eBGP neighbor to ISP-A and ISP-B. While the adjacency between R1 and ISP-B is okay, R1 and ISP-A are not able to establish an adjacency.
The ISP-A and ISP-B neighbor configurations on R1 are nearly identical. What is the problem between R1 and ISP-A?
A. R1 should redistribute static routes into the BGP process
B. R1 needs to add the ebgp-multihop option to the ISP-A neighbor command
C. R1 should be configured with the BGP AS 64750
D. R1 is missing the next-hop-self option under the neighbor command for ISP-A
Answer: B

NEW QUESTION: 2
写真共有および出版会社は、毎日10,000〜150,000枚の画像を受け取ります。同社は、サービスに登録されている複数のサプライヤーやユーザーから画像を受け取ります。同社はAWSに移行しており、AmazonRekognitionを使用してデータを追加することで既存のメタデータを強化したいと考えています。
以下は、追加データの例です。

クラウド移行プログラムの一環として、同社は既存の画像データをAmazon S3にアップロードし、ユーザーに画像をAmazonS3に直接アップロードするように指示しました。
ソリューションアーキテクトは、これらの要件をサポートするために何をすべきですか？
A. S3イベント通知に基づいてAWS Lambdaをトリガーし、AmazonRekognitionを使用して追加のメタデータを作成します。 Amazon RDS MySQL Multi-AZを使用してメタデータ情報を保存し、Lambdaを使用してインデックスを作成します。 Lambdaがサポートする検索機能を備えたWebフロントエンドを使用します。
B. Amazon Kinesisを使用して、S3イベントに基づいてデータをストリーミングします。 Amazon EC2で実行されているアプリケーションを使用して、画像からメタデータを抽出します。次に、AmazonDynamoDBとAmazonCloudSearchにデータを保存し、インデックスを作成します。 CloudSearchに裏打ちされた検索機能を備えたWebフロントエンドを使用します。
C. S3イベント通知に基づいてAmazonSQSキューを開始します。次に、AmazonSQSにメタデータ情報をAmazonDynamoDBに送信させます。 Amazon EC2で実行されているアプリケーションは、APIを使用してAmazon Rekognitionからデータを抽出し、DynamoDBとAmazonESにデータを追加します。 Webフロントエンドを使用して、AmazonESに裏打ちされた検索機能を提供します。
D. S3イベント通知に基づいてAWS Lambdaをトリガーし、AmazonRekognitionを使用して追加のメタデータを作成します。 Amazon DynamoDBを使用してメタデータを保存し、AmazonESを使用してインデックスを作成します。 Webフロントエンドを使用して、AmazonESに裏打ちされた検索機能を提供します。
Answer: D
Explanation:
Explanation
https://github.com/aws-samples/lambda-refarch-imagerecognition

NEW QUESTION: 3
eコマースアプリケーションはAmazonSimple Queue Service（Amazon SQS）キューに注文を出しますメッセージを受信すると、AmazonEC2ワーカーインスタンスがリクエストを処理しますEC2インスタンスはAutoScalingグループにあります236自動スケーリングをスケーリングするようにアーキテクチャを設計する方法運用オーバーヘッドが最小のグループ？
A. EC2CPUでAmazonCloudWatchアラームを使用して、AutoScalingグループをスケールアップおよびスケールダウンします
B. キュー内のメッセージ数に基づいてAmazon CloudWatchアラームを使用して、AutoScalingグループをスケールアップまたはスケールダウンします
C. CPUに基づくAmazon CloudWatchアラームを使用して、AutoScalingグループをスケールアップまたはスケールダウンします
D. EC2インスタンスで処理されたメッセージのAmazon EC2 Auto Scalingヘルスチェックを使用して、スケールアップまたはスケールダウンします。
Answer: B

NEW QUESTION: 4
Scenario:
CGE decided to virtualize its infrastructure workloads and provide a virtual solution to all end users. The infrastructure workloads include Delivery Controllers, StoreFront servers, License Servers, and Microsoft SQL Servers for databases.
Why is creating a resource pool comprising a maximum of eight XenServer hosts, backing up virtual workloads on a daily basis, and using third-party backups for the desktops the best design solution?
A. It separates infrastructure and virtual desktop workloads according to resource usage characteristics to avoid conflicts. In addition, it provides critical database services with sufficient workload-specific resources and failover capabilities.
B. It ensures the logical separation of infrastructure and virtual desktop workloads, and facilitates easier management and expansion capabilities.
C. It ensures the logical separation of all workloads and virtual desktops. In addition, the backup solution reduces the risk of data loss and minimizes downtime.
D. It provides workload flexibility to more efficiently leverage available resources within a resource pool, and facilitates expansion options. In addition, daily backups create minimal downtime for critical workloads.
Answer: D