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NEW QUESTION: 1
솔루션 아키텍트가 AWS Fargate에서 실행될 컨테이너화 된 NET Core 애플리케이션을 구축하고 있습니다 애플리케이션의 백엔드에는 고 가용성을 갖춘 Microsoft SQL Server가 필요합니다. 애플리케이션의 모든 계층은 고 가용성이어야 합니다. SQL Server에 대한 연결 문자열에 사용되는 자격 증명은 .NET Core 프런트 엔드 컨테이너 내의 디스크에 저장됩니다.
Solutions Architect는 이러한 요구 사항을 충족하기 위해 어떤 전략을 사용해야 합니까?
A. Amazon RDS에서 SQL Server의 다중 AZ 배포 생성 RDS 데이터베이스에 대한 자격 증명을 위해 AWS Secrets Manager에서 암호를 생성합니다. Amazon을 생성합니다.
Fargate 작업 정의가 Secrets Manager의 RDS 데이터베이스에 대한 자격 증명에 대한 비밀 값을 가져 오도록 허용하는 ECS 작업 실행 역할 Fargate 작업 정의의 secrets 섹션에서 Secrets Manager의 비밀 ARN을 지정하여 민감한 데이터를 삽입 할 수 있도록 합니다. 연결 문자열을 구성하기 위해 애플리케이션을 읽기 위해 시작시 환경 변수로 컨테이너에 여러 가용 영역의 Application Load Balancer 뒤에서 서비스 자동 조정을 사용하여 Fargate에서 NET Core 서비스를 설정합니다.
B. Amazon RDS에서 SQL Server의 다중 AZ 배포 생성 RDS 데이터베이스에 대한 자격 증명을 위해 AWS Secrets Manager에서 암호 생성 Fargate 작업 정의에서 NET Core 컨테이너에 대한 비 영구적 빈 스토리지를 생성하여 민감한 정보를 저장합니다. 정보 Fargate 작업 정의가 Secrets Manager의 RDS 데이터베이스에 대한 자격 증명에 대한 비밀 값을 가져올 수 있는 Amazon ECS 작업 실행 역할을 생성합니다. Fargate 작업 정의의 secrets 섹션에서 Secrets Manager의 비밀 ARN을 지정하여 시작시 데이터를 비 영구 빈 스토리지에 기록하여 연결 문자열을 구성하기 위해 애플리케이션을 읽을 수 있습니다. 여러 가용 영역에서 Application Load Balancer 뒤의 서비스 자동 조정을 사용하여 NET Core 서비스를 설정합니다.
C. Fargate에서 Service Auto Scaling이 실행되도록 SQL Server를 설정합니다. Fargate 작업 정의가 Fargate에서 실행중인 SQL Server에 대한 자격 증명에 대한 비밀 값을 가져 오도록 허용하는 Amazon ECS 작업 실행 역할 생성 Fargate 작업 정의의 비밀 섹션에서 AWS Secrets Manager의 비밀 ARN을 지정하여 민감한 데이터 연결 문자열을 구성하기 위해 응용 프로그램에서 읽기 위해 시작시 환경 변수로 컨테이너에 주입 될 수 있습니다. 여러 가용 영역에서 Application Load Balancer 뒤의 Service Auto Scaling을 사용하여 NET Core 서비스를 설정합니다.
D. Amazon EC2에서 SQL Server를 실행하기 위한 Auto Scaling 그룹 생성 EC2에서 실행되는 SQL Server에 대한 자격 증명을 위해 AWS Secrets Manager에서 암호 생성 Fargate 작업 정의가 암호 값을 가져올 수 있도록 허용하는 Amazon ECS 작업 실행 역할 생성 EC2의 SQL Server에 대한 자격 증명 용 Fargate 작업 정의의 secrets 섹션에서 Secrets Manager의 ARN을 지정하여 시작시 민감한 데이터를 환경 변수로 컨테이너에 주입하여 연결을 구성하기 위해 애플리케이션을 읽을 수 있도록 합니다. string 여러 가용 영역에서 Application Load Balancer 뒤의 Service Auto Scaling을 사용하여 NET Core 서비스를 설정합니다.
Answer: A
Explanation:
https://aws.amazon.com/premiumsupport/knowledge-center/ecs-data-security-container-task/

NEW QUESTION: 2
A VPC has been created with a single subnet. Virtual server instances have been deployed on the subnet that have different functions which support a production application. The security team has now said that they want to ensure that network traffic between the different server types can be controlled. How can this be achieved?
A. Create multiple access control lists with rules that satisfy the security team's requirements and associate each VSI to the respective ACL
B. Create security groups for the different server types with inbound and outbound rules that satisfy the security team, into which the VSIs can be placed
C. Create a VPC load balancer and place each server type into its own back-end pool, then create Layer-7 routing policies that restrict network traffic flowing between the different pools
D. Use a Hardware Security Module to create advanced network routing tables that ensure traffic does not route between server types, as per the security team's wishes
Answer: D

NEW QUESTION: 3
マルチクラスの画像分類の深層学習モデルを作成します。
モデルは、パブリックWebポータルからフェッチされた新しい画像データで毎月再トレーニングする必要があります。 Azure Machine Learningパイプラインを作成して、新しいデータをフェッチし、画像のサイズを標準化し、モデルを再トレーニングします。
パイプラインのスケジュールを構成するには、Azure Machine Learning SDKを使用する必要があります。
どの4つのアクションを順番に実行する必要がありますか。回答するには、適切なアクションをアクションのリストから回答領域に移動し、正しい順序に並べます。

Answer:
Explanation:

Explanation:
Step 1: Publish the pipeline.
To schedule a pipeline, you'll need a reference to your workspace, the identifier of your published pipeline, and the name of the experiment in which you wish to create the schedule.
Step 2: Retrieve the pipeline ID.
Needed for the schedule.
Step 3: Create a ScheduleRecurrence..
To run a pipeline on a recurring basis, you'll create a schedule. A Schedule associates a pipeline, an experiment, and a trigger.
First create a schedule. Example: Create a Schedule that begins a run every 15 minutes:
recurrence = ScheduleRecurrence(frequency="Minute", interval=15)
Step 4: Define an Azure Machine Learning pipeline schedule..
Example, continued:
recurring_schedule = Schedule.create(ws, name="MyRecurringSchedule",
description="Based on time",
pipeline_id=pipeline_id,
experiment_name=experiment_name,
recurrence=recurrence)
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-schedule-pipelines

NEW QUESTION: 4
Information Warfare is:
A. Developing weapons systems based on artificial intelligence technology.
B. Generating and disseminating propaganda material.
C. Signal intelligence.
D. Attacking the information infrastructure of a nation to gain military and/or economic advantages.
Answer: D
Explanation:
*Answer "Developing weapons systems based on artificial intelligence technology" is a distracter and has to do with weapon systems development.
*Answer "Generating and disseminating propaganda material" is not applicable. Answer "Signal intelligence" is the conventional acquisition of information from radio signals.