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NEW QUESTION: 1
プロジェクトチームは、社内チームと仮想チームメンバーが混在して構成されています。
複雑なアナウンスをプロジェクトチーム全体に出す必要がある場合、以下のどれがそれを伝える最も効果的な方法ですか?
A. Web会議ミーティングを開催するのが最善の方法です。 それはプロジェクトマネージャーに発表を説明し、チームからの質問に答える機会を与えます。
B. ファックスを送信することが最善の方法です。 それは、プロジェクトマネージャーに発表を説明し、チームが持つ可能性のある質問と回答を提示する機会を与えます。
C. 発表の詳細を含むプロジェクトチームの電子メールを作成することが最善の方法です。 それはプロジェクトマネージャーに説明する機会を与えるが、チームからの質問で克服することはできない。
D. 発表をするためにテキストメッセージを使用することが最善の方法です。 プロジェクトマネージャーは、プロジェクトマネージャーがどこにいてもプロジェクトチームメンバーに連絡することができます。
Answer: A
Explanation:
Explanation
References: Kim Heldman, CompTIA Project+ Study Guide, 2nd Edition, Sybex, Indianapolis, 2017, p. 260
NEW QUESTION: 2
Many approaches to Knowledge Discovery in Databases (KDD) are used to identify valid and useful patterns in data. This is an evolving field of study that includes a variety of automated analysis solutions such as Data Mining. Which of the following is not an approach used by KDD?
A. Probabilistic
B. Classification
C. Oriented
D. Deviation
Answer: C
Explanation:
The Oriented approach does not correctly describe a KDD approach.
The main approaches of KDD according to CBK are:
* Probabilistic approach: uses graphical representation models to compare different knowledge representations. The models are based on probabilities and data independencies. The probabilistic models are useful for applications involving uncertainty, such as those used in planning and control systems.
* Statistical approach: uses rule discovery and is based on data relationships. Learning algorithm can automatically select useful data relationship paths and attributes. These paths and attributes are then used to construct rules for discovering meaningful information. This approach is used to generalize patterns in the data and to construct rules from the noted patterns. An example of the statistical approach is OLAP.
* Classification approach: groups data according to similarities. One example is a pattern discovery and data-cleaning model that reduces a large database to only a few specific records. By eliminating redundant and non-important data, the discovery of patterns in the data is simplified.
* Deviation and trend analysis: uses filtering techniques to detect patterns. An example is an intrusion detection system that filters a large volume of data so that only the pertinent data is analyzed.
* Neural networks: methods used to develop classification, regression, association, and segmentation models. A neural net method organizes data into nodes that are arranged in layers, and links between the nodes have specific weighting classifications. The neural net is helpful in detecting the associations among the input patterns or relationships. It is also considered a learning system because new information is utomatically incorporated into the system. However, the value and relevance of the decisions made by the neural network are only as good as the experience it is given. The greater the experience, the better the decision. Note that neural nets have a specific problem in terms of an individual's ability to substantiate processing in that they are subject to superstitious knowledge, which is a tendency to identify relations when no relations actually exist. More sophisticated neural nets are less subject to this problem.
* Expert system approach: uses a knowledge base (a collection of all the data, or knowledge, on a particular matter) and a set of algorithms and/or rules that infer new facts from knowledge and incoming data. The knowledge base could be the human experience that is available in an organization. Because the system reacts to a set of rules, if the rules are faulty, the response will also be faulty. Also, because human decision is removed from the point of action, if an error were to occur, the reaction time from a human would be longer.
* Hybrid approach: a combination of more than one approach that provides a more powerful and useful system.
The following answers are incorrect:
The other options describes some of the KDD possible approaches but were not the right choice.
The following reference(s) were/was used to create this question:
OFFICIAL (ISC)2 GUIDE TO THE CISSP EXAM - First Edition, page 309.
and
https://en.wikipedia.org/wiki/Data_mining
NEW QUESTION: 3
회사에는 Amazon CloudFront, Amazon API Gateway 및 AWS Lambda 기능으로 구성된 서버리스 애플리케이션이 있습니다. 애플리케이션 코드의 현재 배포 프로세스는 새 버전 번호의 Lambda 함수를 생성하고 AWS CLI 스크립트를 실행하여 업데이트하는 것입니다. 새 기능 버전에 오류가있는 경우 이전 작업 버전의 기능을 배치하여 다른 CLI 스크립트가 복귀합니다. 이 회사는 Lambda 함수가 제공하는 새로운 버전의 응용 프로그램 논리를 배포하는 시간을 줄이고 오류가 식별 될 때이를 감지하고 되 돌리는 시간을 줄이려고 합니다.
이것이 어떻게 이루어질 수 있습니까?
A. AWS SAM 및 내장 AWS CodeDeploy를 사용하여 새 Lambda 버전을 배포하고 점차 새 버전으로 트래픽을 이동하며 트래픽 전 및 트래픽 후 테스트 기능을 사용하여 코드를 확인하십시오. Amazon CloudWatch 경보가 트리거되는 경우 롤백
B. 새로운 Lambda 버전을 참조하는 새로운 API 게이트웨이 엔드 포인트로 구성된 AWS CloudFormation 스택을 생성 및 배포합니다. CloudFront 오리진을 새 API Gateway 엔드 포인트로 변경하고 오류를 모니터링 한 후 감지되면 AWS CloudFront 오리진을 이전 API Gateway 엔드 포인트로 변경하십시오.
C. AWS CloudFront 배포 및 API 게이트웨이로 구성된 상위 스택과 Lambda 함수가 포함 된 하위 스택으로 중첩 된 AWS CloudFormation 스택을 생성 및 배포합니다. Lambda를 변경하려면 AWS CloudFormation 변경 세트를 생성하고 배포하십시오. 오류가 발생하면 AWS CloudFormation 변경 세트를 이전 버전으로 되돌립니다.
D. AWS CLI 스크립트를 새로운 Lambda 버전을 배포하는 단일 스크립트로 리팩터링하십시오. 배포가 완료되면 스크립트 테스트가 실행됩니다. 오류가 감지되면 이전 Lambda 버전으로 되돌립니다.
Answer: A
Explanation:
Explanation
https://aws.amazon.com/about-aws/whats-new/2017/11/aws-lambda-supports-traffic-shifting-and-phased-deplo
https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/automating-updates-to-serverl
NEW QUESTION: 4
Refer to the exhibit.
Which statement is correct regarding this vPC environment?
A. The rote priority uses its default value.
B. The vPC peer keep alive uses a non-VRF path.
C. The system priority has been configured to a lower number than its default value.
D. ip arp synchronize is automatically configured when a vPC domain is created.
Answer: C