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NEW QUESTION: 1
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。会社は、サービスに登録されている複数のサプライヤーとユーザーから画像を受け取ります。会社はAWSに移行しており、Amazon Rekognitionを使用してデータを追加することにより、既存のメタデータを充実させ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、ユーザーに画像を直接Amazon S3にアップロードするように指示しました。
これらの要件をサポートするために、ソリューションアーキテクトは何をすべきですか?
A. S3イベント通知に基づいてAWS Lambdaをトリガーし、Amazon Rekognition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、データをAmazon DynamoDBおよびAmazon CloudSearchに保存し、インデックスを作成します。 CloudSearch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。
C. S3イベント通知に基づいてAWS Lambdaをトリガーし、Amazon Rekognition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、Amazon ESを使用してインデックスを作成します。ウェブフロントエンドを使用して、Amazon ESがサポートする検索機能を提供します。
D. S3イベント通知に基づいてAmazon SQSキューを開始します。次に、Amazon SQSからメタデータ情報をAmazon DynamoDBに送信します。 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およびAmazon ESにデータを追加します。ウェブフロントエンドを使用して、Amazon ESがサポートする検索機能を提供します。
Answer: C
Explanation:
Explanation
https://github.com/aws-samples/lambda-refarch-imagerecognition
NEW QUESTION: 2
ソリューションアーキテクトは、Amazon Linuxに基づいてハイパフォーマンスコンピューティング(HPC)環境用のストレージを設計しています。ワークロードは、共有ストレージと重いコンピューティングを必要とするエンジニアリング図面を大量に保存して処理します。
どのストレージオプションが最適なソリューションでしょうか?
A. Amazon EBS Provisioned IOPS SSD (io1)
B. Amazon Elastic File System (Amazon EFS)
C. Amazon FSx for Lustre
D. Amazon EC2 instance store
Answer: C
Explanation:
https://d1.awsstatic.com/whitepapers/AWS%20Partner%20Network_HPC%20Storage%20Option s_2019_FINAL.pdf (p.8)
NEW QUESTION: 3
You are a developer for a software as a service (SaaS) company that uses an Azure Function to process orders. The Azure Function currently runs on an Azure Function app that is triggered by an Azure Storage queue.
You are preparing to migrate the Azure Function to Kubernetes using Kubernetes-based Event Driven Autoscaling (KEDA).
You need to configure Kubernetes Custom Resource Definitions (CRD) for the Azure Function.
Which CRDs should you configure? To answer, drag the appropriate CRD types to the correct locations.
Each CRD type may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: Deployment
To deploy Azure Functions to Kubernetes use the func kubernetes deploy command has several attributes that directly control how our app scales, once it is deployed to Kubernetes.
Box 2: ScaledObject
With --polling-interval, we can control the interval used by KEDA to check Azure Service Bus Queue for messages.
Example of ScaledObject with polling interval
apiVersion: keda.k8s.io/v1alpha1
kind: ScaledObject
metadata:
name: transformer-fn
namespace: tt
labels:
deploymentName: transformer-fn
spec:
scaleTargetRef:
deploymentName: transformer-fn
pollingInterval: 5
minReplicaCount: 0
maxReplicaCount: 100
Box 3: Secret
Store connection strings in Kubernetes Secrets.
Example: to create the Secret in our demo Namespace:
# create the k8s demo namespace
kubectl create namespace tt
# grab connection string from Azure Service Bus
KEDA_SCALER_CONNECTION_STRING=$(az servicebus queue authorization-rule keys list \
-g $RG_NAME \
--namespace-name $SBN_NAME \
--queue-name inbound \
-n keda-scaler \
--query "primaryConnectionString" \
-o tsv)
# create the kubernetes secret
kubectl create secret generic tt-keda-auth \
--from-literal KedaScaler=$KEDA_SCALER_CONNECTION_STRING \
--namespace tt
Reference:
https://www.thinktecture.com/en/kubernetes/serverless-workloads-with-keda/