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NEW QUESTION: 1
MJTelco Case Study
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the
world. The company has patents for innovative optical communications hardware. Based on these patents,
they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to
overcome communications challenges in space. Fundamental to their operation, they need to create a
distributed data infrastructure that drives real-time analysis and incorporates machine learning to
continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the
network allowing them to account for the impact of dynamic regional politics on location availability and
cost.
Their management and operations teams are situated all around the globe creating many-to-many
relationship between data consumers and provides in their system. After careful consideration, they
decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
Scale and harden their PoC to support significantly more data flows generated when they ramp to more

than 50,000 installations.
Refine their machine-learning cycles to verify and improve the dynamic models they use to control

topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production
- to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
Scale up their production environment with minimal cost, instantiating resources when and where

needed in an unpredictable, distributed telecom user community.
Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.

Provide reliable and timely access to data for analysis from distributed research workers

Maintain isolated environments that support rapid iteration of their machine-learning models without

affecting their customers.
Technical Requirements
Ensure secure and efficient transport and storage of telemetry data

Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows

each.
Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately

100m records/day
Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems

both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive
hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize
our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data
secure. We also need environments in which our data scientists can carefully study and quickly adapt our
models. Because we rely on automation to process our data, we also need our development and test
environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis.
Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on
automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to
work on our high-value problems instead of problems with our data pipelines.
MJTelco's Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000
installations. You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud
Dataflow pipeline configuration setting should you update?
A. The zone
B. The disk size per worker
C. The maximum number of workers
D. The number of workers
Answer: A

NEW QUESTION: 2
3-Ber 전자 상거래 웹 응용 프로그램은 현재 사내 구축 형으로 배포되어 있으며 확장 성과 탄력성을 높이기 위해 AWS로 마이그레이션됩니다. 웹 티어는 현재 네트워크 분산 파일 시스템을 사용하여 읽기 전용 데이터를 공유합니다. 앱 서버 계층은 IP 멀티 캐스트에 의존하는 검색 및 공유 세션 상태를 위해 클러스터링 메커니즘을 사용합니다. 데이터베이스 계층은 공유 스토리지 클러스터링을 사용하여 데이터베이스 장애 조치 기능을 제공하고 확장을 위해 여러 개의 읽기 슬레이브를 사용합니다. 모든 서버 및 분산 파일 시스템 디렉토리의 데이터는 매주 오프 사이트 테이프에 백업됩니다.
어떤 AWS 스토리지 및 데이터베이스 아키텍처가 애플리케이션의 요구 사항을 충족합니까?
A. 웹 서버 : S3에 읽기 전용 데이터를 저장하고 부팅 시간에 S3에서 루트 볼륨으로 복사합니다. 응용 프로그램 서버 : DynamoDB와 IP 유니 캐스트의 조합을 사용하여 상태를 공유합니다. 데이터베이스 : 다중 AZ 배포에서 RDS를 사용합니다. 백업 : 웹 및 응용 프로그램 서버는 매주 AMI를 통해 백업되고 데이터베이스는 DB 스냅 샷을 통해 백업됩니다.
B. 웹 서버 : 부팅시 읽기 전용 데이터를 S3에 저장하고 S3에서 루트 볼륨으로 복사합니다. 앱 서버 :
DynamoDB와 IP 유니 캐스트의 조합을 사용하여 상태를 공유합니다. 데이터베이스 : 다중 AZ 배포 및 하나 이상의 읽기 복제본과 함께 RDS를 사용합니다. 백업 : 웹 서버, 응용 프로그램 서버 및 데이터베이스가 스냅 샷을 사용하여 매주 Glacier에 백업됩니다.
C. 웹 서버 : 부팅시 읽기 전용 데이터를 S3에 저장하고 S3에서 루트 볼륨으로 복사합니다. 응용 프로그램 서버 : DynamoDB와 IP 유니 캐스트의 조합을 사용하여 상태를 공유합니다. 데이터베이스 : 다중 AZ 배포 및 하나 이상의 읽기 복제본과 함께 RDS를 사용합니다. 백업 : AMI를 통해 매주 백업되는 웹 및 앱 서버, DB 스냅 샷을 통해 백업 된 데이터베이스.
D. 웹 서버 : 읽기 전용 데이터를 EC2 NFS 서버에 저장하고 부팅시 각 ​​웹 서버에 마운트합니다. 응용 프로그램 서버 : DynamoDB와 IP 멀티 캐스트를 함께 사용하여 상태를 공유합니다. 데이터베이스 : 다중 AZ 배포 및 하나 이상의 읽기 복제본과 함께 RDS를 사용합니다. 백업 : AMI를 통해 매주 백업되는 웹 및 앱 서버, DB 스냅 샷을 통해 백업 된 데이터베이스.
Answer: B
Explanation:
설명
Amazon Glacier는 모든 보관 상황에 적합하지 않습니다. 다음은 아마존 빙하 대신 다른 AWS 스토리지 옵션을 고려해야하는 몇 가지 스토리지 요구 사항입니다.
매우 자주 업데이트해야하는 데이터는 Amazon EBS, Amazon RDS, Amazon DynamoDB 또는 EC2에서 실행되는 관계형 데이터베이스와 같이 읽기 / 쓰기 대기 시간이 적은 스토리지 솔루션에 더 적합 할 수 있습니다.
https://d0.awsstatic.com/whitepapers/Storage/AWS%20Storage%20Services%20Whitepaper-v9.pdf

NEW QUESTION: 3
ある会社は、規制がありセキュリティに敏感なビジネスをAWSに移行する予定です。セキュリティチームは、AWSのベストプラクティスと業界で認められたコンプライアンス基準の採用を検証するためのフレームワークを開発しています。 AWSマネジメントコンソールは、チームがリソースをプロビジョニングするのに推奨される方法です。
Solutions Architectがビジネス要件を満たし、AWSリソースの設定を継続的に評価、監査、監視するために使用する戦略はどれですか？ （2つ選んでください。）
A. Amazon CloudWatch Logsエージェントを使用してすべてのAWS SDKログを収集します。 API呼び出しを変更するマシンで定義されている一連のフィルタパターンを使用してログデータを検索します。意図しない変更が行われたときにAmazon CloudWatchアラームを使用して通知を送信します。 Amazon S3、次にAmazon Glacierへのバッチエクスポートを使用してログデータをアーカイブし、長期間の保存と監査を可能にします。
B. Amazon CloudWatch Eventsのリアルタイムに近い機能を使用してシステムイベントパターンを監視し、AWS Lambda関数をトリガーしてAWSリソースの未承認の変更を自動的に元に戻します。
また、Amazon SNSトピックをターゲットにして通知を有効にし、インシデントレスポンスのレスポンスタイムを改善します。
C. CloudTrailとAmazon SNSの統合を使用して、不正なAPIアクティビティを自動的に通知します。
すべてのアカウントと利用可能なAWSサービスでCloudTrailが有効になっていることを確認してください。 AWSリソース内の許可されていない変更を自動的に元に戻すためのLambda関数の使用法を評価します。
D. AWS CloudTrailイベントを使用して、すべてのAWSアカウントの管理活動を評価します。すべてのアカウントと利用可能なAWSサービスでCloudTrailが有効になっていることを確認してください。証跡を有効にし、CloudTrailイベントログファイルをAWS KMSキーで暗号化し、記録されたアクティビティをCloudWatch Logsで監視します。
E. AWS Configルールを使用して、AWSリソースへの変更を定期的に監査し、設定のコンプライアンスを監視します。テスト駆動開発アプローチを確立するためにAWS Lambdaを使用してAWS Configカスタムルールを開発し、さらに必要なコントロールに対する設定変更の評価を自動化します。
Answer: B,D

NEW QUESTION: 4
Which statement below describes the most correct strategy for implementing a Rule Base?
A. Place the most frequently used rules at the top of the Policy and the ones that are not frequently used further down.
B. Umit grouping to rules regarding specific access.
C. Place a network-traffic rule above the administrator access rule.
D. Add the Stealth Rule before the last rule.
Answer: A