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NEW QUESTION: 1
When organizing information in an Einstein Analytics dashboard, what does the "Progressive Disclosure' design principle mean'
A. Implement strict security predicates to minimize the amount of information displayed to users.
B. Only provide the user with the level of detail they need to see, with the option to drill down deeper into more details.
C. Intentionally omit specific details so that users can do ad-hoc exploration if needed for root-cause analysis.
D. Utilize the latest templates for the most modern look and feel.
Answer: B
NEW QUESTION: 2
한 회사가 인터넷 연결 웹 애플리케이션을 설계하고 있습니다. 이 애플리케이션은 Amazon RDS MySQL 다중 AZ DB 인스턴스에 민감한 사용자 데이터를 저장하는 Linux 기반 인스턴스 용 Amazon EC2에서 실행됩니다. EC2 인스턴스는 퍼블릭 서브넷에 있고 RDS DB 인스턴스는 프라이빗 서브넷에 있습니다. 보안 팀은 웹 기반 공격으로부터 DB 인스턴스를 보호하도록 의무화했습니다. 솔루션 설계자는 무엇을 권장해야 합니까?
A. EC2 인스턴스가 Auto Scaling 그룹의 일부이고 Application Load Balancer 뒤에 있는지 확인합니다. AWS WAF를 사용하여 위협에 대한 인바운드 웹 트래픽 모니터링 Auto Scaling 그룹 구성 lo 자동으로 트래픽이 많은 경우 새 DB 인스턴스 생성 RDS DB 인스턴스에 대한 보안 그룹을 생성합니다. 포트 3306 인바운드 만 허용하도록 RDS 보안 그룹 구성
B. EC2 인스턴스가 Auto Scaling 그룹의 일부이고 Application Load Balancer 뒤에 있는지 확인합니다. 의심스러운 웹 트래픽을 삭제하도록 EC2 인스턴스 iptables 규칙 구성 DB 인스턴스에 대한 보안 그룹 생성 개별 EC2 인스턴스에서 포트 3306 인바운드만 허용하도록 RDS 보안 그룹을 구성합니다.
C. EC2 인스턴스가 Auto Scaling 그룹의 일부이고 Application Load Balancer 뒤에 있는지 확인합니다. AWS WAF를 사용하여 위협에 대한 인바운드 웹 트래픽 모니터링 웹 애플리케이션 서버용 보안 그룹과 DB 인스턴스 용 보안 그룹을 생성합니다. 웹 애플리케이션 서버 보안 그룹에서 인바운드 포트 3306 만 허용하도록 RDS 보안 그룹을 구성합니다.
D. EC2 인스턴스가 Auto Scaling 그룹의 일부이고 Application Load Balancer 뒤에 있는지 확인합니다. DB 인스턴스를 EC2 인스턴스가 있는 동일한 서브넷으로 이동합니다. DB 인스턴스에 대한 보안 그룹을 생성합니다. 개별 EC2 인스턴스에서 인바운드 포트 3306 만 허용하도록 RDS 보안 그룹 구성
Answer: C
NEW QUESTION: 3
A. Set-CsPersistentChatRoom -Identity IT-Presenters @{Add="sip:[email protected]"}
B. New-CsTrustedApplicationEndpoint -Application pchat -TrustedApplicationPoolFqdn pchatpooLlitware.com -SipAddress sip: [email protected]
C. $cred1 = Get-Credential 1itwareinc\pilar"$cred2 = Get-Credential 1itwareinc\ocschatTest-CsPersistentChatMessage -TargetFqdn pchatpool. litwareinc.com -SenderSipAddress "sip:[email protected]" -SenderCredential Scredl -ReceiverSipAddress "sip:[email protected]" -ReceiverCredential $cred2
D. New-CsPersistentChatEndpoint -SipAddress sip:[email protected] -PersistentChatPoolFqdn pchatpool.litware.com
Answer: D
NEW QUESTION: 4
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have Azure IoT Edge devices that generate streaming data.
On the devices, you need to detect anomalies in the data by using Azure Machine Learning models. Once an anomaly is detected, the devices must add information about the anomaly to the Azure IoT Hub stream.
Solution: You expose a Machine Learning model as an Azure web service.
Does this meet the goal?
A. No
B. Yes
Answer: A
Explanation:
Explanation
Instead use Azure Stream Analytics and REST API.
Note. Available in both the cloud and Azure IoT Edge, Azure Stream Analytics offers built-in machine learning based anomaly detection capabilities that can be used to monitor the two most commonly occurring anomalies:
temporary and persistent.
Stream Analytics supports user-defined functions, via REST API, that call out to Azure Machine Learning endpoints.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-machine-learning-anomaly-detection
