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NEW QUESTION: 1
EC2 호스팅 응용 프로그램에서 특정 도메인으로 아웃 바운드 HTTP의 연결을 제한하려는 회사에 URL 허용 목록 시스템을 구현하고 있습니다. 프록시 소프트웨어를 실행하는 단일 EC2 인스턴스를 배포하고 VPC의 모든 서브넷 및 EC2 인스턴스의 트래픽을 수락하도록 구성합니다. 화이트리스트 구성에서 정의한 도메인으로 만 트래픽을 통과하도록 프록시를 구성합니다. 야간 유지 관리 기간 또는 모든 인스턴스가 새 소프트웨어 업데이트를 가져 오는 10 분이 있습니다. 각 업데이트의 크기는 약 200MB이고 VPC에는 정기적으로 업데이트를 가져 오는 500 개의 인스턴스가 있습니다. 며칠 후 일부 시스템이 일부를 성공적으로 다운로드하지 못했지만 유지 관리 기간 내에 모든 업데이트가 아님을 알 수 있습니다. 이러한 업데이트에 사용 된 다운로드 URL은 프록시의 화이트리스트 구성에 올바르게 나열되며 인스턴스의 웹 브라우저를 사용하여 수동으로 액세스 할 수 있습니다.
무슨 일이야? (2 선택)
A. 영향을받는 EC2 인스턴스를 포함하는 서브넷의 라우팅 테이블이 소프트웨어 업데이트 위치의 네트워크 트래픽을 프록시로 보내도록 구성되어 있지 않습니다.
B. 프록시를 실행하는 EC2 인스턴스에 충분한 스토리지를 할당하지 않아 네트워크 버퍼가 가득 차서 일부 요청이 실패 할 수 있습니다.
C. 프라이빗 서브넷의 충분한 크기의 EC2 인스턴스에서 프록시를 실행하고 있으며 크기가 작은 EC2 인스턴스에서 실행되는 NAT가 네트워크 처리량을 조절하고 있습니다.
D. 소규모 EC2 인스턴스 유형에서 프록시를 실행하고 있으므로 모든 인스턴스가 업데이트를 적시에 다운로드하기에 네트워크 처리량이 충분하지 않습니다.
E. 퍼블릭 서브넷에서 프록시를 실행하고 있지만 인터넷 게이트웨이 (IGW)를 통해 필요한 네트워크 처리량을 지원하기에 충분한 EIP를 할당하지 않았습니다.
Answer: C,D

NEW QUESTION: 2
モデルの適合の問題を修正する必要があります。
順番に実行する必要がある3つのアクションはどれですか？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エリアに移動し、正しい順序に並べます。

Answer:
Explanation:

Explanation

Step 1: Augment the data
Scenario: Columns in each dataset contain missing and null values. The datasets also contain many outliers.
Step 2: Add the Bayesian Linear Regression module.
Scenario: You produce a regression model to predict property prices by using the Linear Regression and Bayesian Linear Regression modules.
Step 3: Configure the regularization weight.
Regularization typically is used to avoid overfitting. For example, in L2 regularization weight, type the value to use as the weight for L2 regularization. We recommend that you use a non-zero value to avoid overfitting.
Scenario:
Model fit: The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.

NEW QUESTION: 3
When the optional features in the install.addon.xml file are uncommented, what will happen to these features?
A. The features will be deleted on installation.
B. The features become part of the base client install package.
C. The features will not be installed.
D. The client will prompt to user to confirm installation.
Answer: B

NEW QUESTION: 4
The network administrator at an enterprise reported a large data leak. One compromised server was used to aggregate data from several critical application servers and send it out to the Internet using HTTPS. Upon investigation, there have been no user logins over the previous week and the endpoint protection software is not reporting any issues. Which of the following BEST provides insight into where the compromised server collected the information?
A. Correlate data loss prevention logs for anomalous communications from the server.
B. Review the flow data against each server's baseline communications profile.
C. Configure the server logs to collect unusual activity including failed logins and restarted services.
D. Setup a packet capture on the firewall to collect all of the server communications.
Answer: B
Explanation:
Network logging tools such as Syslog, DNS, NetFlow, behavior analytics, IP reputation, honeypots, and DLP solutions provide visibility into the entire infrastructure. This visibility is important because signature-based systems are no longer sufficient for identifying the advanced attacker that relies heavily on custom malware and zero-day exploits. Having knowledge of each host's communications, protocols, and traffic volumes as well as the content of the data in question is key to identifying zero-day and APT (advance persistent threat) malware and agents.
Data intelligence allows forensic analysis to identify anomalous or suspicious communications by comparing suspected traffic patterns against normal data communication behavioral baselines. Automated network intelligence and next-generation live forensics provide insight into network events and rely on analytical decisions based on known vs. unknown behavior taking place within a corporate network.