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NEW QUESTION: 1
회사에서는 IP 패킷 데이터에 유효하지 않거나 악의적 인 내용이 있는지 검사해야 합니다.
다음 중 어떤 접근 방식이이 요구 사항을 충족합니까? (2 개를 선택하십시오.)
A. VPC 내의 각 EC2 인스턴스에서 CloudWatch Logs 에이전트를 구성하십시오. CloudWatch Logs의 로그 데이터에서 검사를 수행하십시오.
B. VPC의 모든 서브넷에 대해 VPC 흐름 로그를 활성화합니다. Amazon CloudWatch Logs 내의 흐름 로그 데이터에서 검사를 수행하십시오.
C. ELB (Elastic Load Balancing) 액세스 로그를 구성합니다. ELB 액세스 로그 파일 내의 로그 데이터에서 검사를 수행하십시오.
D. VPC 내의 각 EC2 인스턴스에서 호스트 기반 에이전트를 구성하십시오. 호스트 기반 에이전트 내에서 검사를 수행하십시오.
E. Amazon EC2에서 프록시 솔루션을 구성하고 모든 아웃 바운드 VPC 트래픽을 라우팅하십시오. EC2 인스턴스의 프록시 소프트웨어 내에서 검사를 수행하십시오.
Answer: D,E
Explanation:
Explanation
"EC2 Instance IDS/IPS solutions offer key features to help protect your EC2 instances. This includes alerting administrators of malicious activity and policy violations, as well as identifying and taking action against attacks. You can use AWS services and third party IDS/IPS solutions offered in AWS Marketplace to stay one step ahead of potential attackers."

NEW QUESTION: 2
Given that the elements of a PriorityQueue are ordered according to natural ordering, and:
2.import java.util.*;
3.public class GetInLine {
4.public static void main(String[] args) {
5.PriorityQueue<String> pq = new PriorityQueue<String>();
6.pq.add("banana");
7.pq.add("pear");
8.pq.add("apple");
9.System.out.println(pq.poll() + " " + pq.peek());
10.}
11.}
What is the result?
A. apple banana
B. apple apple
C. banana banana
D. apple pear
E. banana pear
Answer: A

NEW QUESTION: 3
Streaming Analyticsの要件を満たすには、GROUP BY句をどのように完了する必要がありますか？
A. GROUP BY SessionWindow(Second, 30, 60)
B. GROUP BY SlidingWindow(Second, 30)
C. GROUP BY TumblingWindow(Second, 30)
D. GROUP BY HoppingWindow(Second, 60, 30)
Answer: C
Explanation:
Scenario: You plan to use a 30-second period to calculate the average temperature reading of the sensors.
Tumbling window functions are used to segment a data stream into distinct time segments and perform a function against them, such as the example below. The key differentiators of a Tumbling window are that they repeat, do not overlap, and an event cannot belong to more than one tumbling window.
InAnswers:
A: Hopping window functions hop forward in time by a fixed period. It may be easy to think of them as Tumbling windows that can overlap, so events can belong to more than one Hopping window result set.
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-window-functions
Topic 1, Contoso
To start the case study
To display the first question on 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.
Existing Environment. Current State of Development
Contoso produces a set of Bluetooth sensors that read the temperature and humidity. The sensors connect to IoT gateway devices that relay the data.
All the IoT gateway devices connect to an Azure IoT hub named iothub1.
Existing Environment. Device Twin
You plan to implement device twins by using the following JSON sample.

Existing Environment. Azure Stream Analytics
Each room will have between three to five sensors that will generate readings that are sent to a single IoT gateway device. The IoT gateway device will forward all the readings to iothub1 at intervals of between 10 and 60 seconds.
You plan to use a gateway pattern so that each IoT gateway device will have its own IoT Hub device identity.
You draft the following query, which is missing the GROUP BY clause.
SELECT
AVG(temperature),
System.TimeStamp() AS AsaTime
FROM
Iothub
You plan to use a 30-second period to calculate the average temperature reading of the sensors.
You plan to minimize latency between the condition reported by the sensors and the corresponding alert issued by the Stream Analytics job.
Existing Environment. Device Messages
The IoT gateway devices will send messages that contain the following JSON data whenever the temperature exceeds a specified threshold.

The level property will be used to route the messages to an Azure Service Bus queue endpoint named criticalep.
Existing Environment. Issues
You discover connectivity issues between the IoT gateway devices and iothub1, which cause IoT devices to lose connectivity and messages.
Requirements. Planning Changes
Contoso plans to make the following changes:
Use Stream Analytics to process and view data.
Use Azure Time Series Insights to visualize data.
Implement a system to sync device statuses and required settings.
Add extra information to messages by using message enrichment.
Create a notification system to send an alert if a condition exceeds a specified threshold.
Implement a system to identify what causes the intermittent connection issues and lost messages.
Requirements. Technical Requirements
Contoso must meet the following requirements:
Use the built-in functions of IoT Hub whenever possible.
Minimize hardware and software costs whenever possible.
Minimize administrative effort to provision devices at scale.
Implement a system to trace message flow to and from iothub1.
Minimize the amount of custom coding required to implement the planned changes.
Prevent read operations from being negatively affected when you implement additional services.

NEW QUESTION: 4
개발 팀은 회사 내에 서버없는 응용 프로그램으로 새로운 API를 배포하고 있습니다. 팀은 현재 AWS Management Console을 사용하여 Amazon API Gateway, AWS Lambda 및 Amazon DynamoDB 리소스를 프로비저닝하고 있습니다. Solutions Architect는 이러한 서버리스 API의 향후 배포를 자동화하는 업무를 맡았습니다.
이것이 어떻게 이루어질 수 있습니까?
A. Lambda 지원 커스텀 리소스와 함께 AWS CloudFormation을 사용하여 API 게이트웨이를 프로비저닝하십시오.
AWS :: DynamoDB :: Table 및 AWS :: Lambda :: Function 리소스를 사용하여 Amazon DynamoDB 테이블 및 Lambda 함수를 생성하십시오. CloudFormation 템플릿 배포를 자동화하는 스크립트를 작성하십시오.
B. AWS CloudFormation을 사용하여 서버리스 애플리케이션을 정의하십시오. Lambda 함수에서 버전 관리를 구현하고 버전을 가리키는 별칭을 만듭니다. 배포시 가중치를 구성하여 트래픽을 최신 버전으로 전환하고 트래픽이 이동할 때 가중치를 점차 업데이트하십시오.
C. AWS Serverless Application Model을 사용하여 리소스를 정의하십시오. YAML 템플릿 및 애플리케이션 파일을 코드 저장소에 업로드하십시오. AWS CodePipeline을 사용하여 코드 리포지토리에 연결하고 AWS CodeBuild를 사용하여 구축 할 작업을 생성하십시오. CodePipeline에서 AWS CloudFormation 배포 공급자를 사용하여 솔루션을 배포하십시오.
D. 애플리케이션 코드를 AWS CodeCommit 코드 리포지토리에 커밋합니다. AWS CodePipeline을 사용하고 CodeCommit 코드 리포지토리에 연결하십시오. AWS CodeDeploy를 사용하여 Lambda 함수를 구축 및 배포하려면 AWS CodeBuild를 사용하십시오. 트래픽을 새 버전으로 점차 이동하려면 CodeDeploy에서 배포 환경 설정 유형을 지정하십시오.
Answer: C