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NEW QUESTION: 1
Which of the following layer of an enterprise data flow architecture is responsible for data copying, transformation in Data Warehouse (DW) format and quality control?
A. Data Staging and quality layer
B. Data Mart layer
C. Desktop Access Layer
D. Data access layer
Answer: A
Explanation:
Explanation/Reference:
Data Staging and quality layer -This layer is responsible for data copying, transformation into DW format and quality control. It is particularly important that only reliable data into core DW. This layer needs to be able to deal with problems periodically thrown by operational systems such as change to account number format and reuse of old accounts and customer numbers.
For CISA exam you should know below information about business intelligence:
Business intelligence(BI) is a broad field of IT encompasses the collection and analysis of information to assist decision making and assess organizational performance. To deliver effective BI, organizations need to design and implement a data architecture. The complete data architecture consists of two components The enterprise data flow architecture (EDFA) A logical data architecture
Various layers/components of this data flow architecture are as follows:
Presentation/desktop access layer - This is where end users directly deal with information. This layer includes familiar desktop tools such as spreadsheets, direct querying tools, reporting and analysis suits offered by vendors such as Congas and business objects, and purpose built application such as balanced source cards and digital dashboards.
Data Source Layer - Enterprise information derives from number of sources:
Operational data - Data captured and maintained by an organization's existing systems, and usually held in system-specific database or flat files.
External Data - Data provided to an organization by external sources. This could include data such as customer demographic and market share information.
Nonoperational data - Information needed by end user that is not currently maintained in a computer accessible format.
Core data warehouse -This is where all the data of interest to an organization is captured and organized to assist reporting and analysis. DWs are normally instituted as large relational databases. A property constituted DW should support three basic form of an inquiry.
Drilling up and drilling down - Using dimension of interest to the business, it should be possible to aggregate data as well as drill down. Attributes available at the more granular levels of the warehouse can also be used to refine the analysis.
Drill across - Use common attributes to access a cross section of information in the warehouse such as sum sales across all product lines by customer and group of customers according to length of association with the company.
Historical Analysis - The warehouse should support this by holding historical, time variant data. An example of historical analysis would be to report monthly store sales and then repeat the analysis using only customer who were preexisting at the start of the year in order to separate the effective new customer from the ability to generate repeat business with existing customers.
Data Mart Layer- Data mart represents subset of information from the core DW selected and organized to meet the needs of a particular business unit or business line. Data mart can be relational databases or some form on-line analytical processing (OLAP) data structure.
Data Staging and quality layer -This layer is responsible for data copying, transformation into DW format and quality control. It is particularly important that only reliable data into core DW. This layer needs to be able to deal with problems periodically thrown by operational systems such as change to account number format and reuse of old accounts and customer numbers.
Data Access Layer -This layer operates to connect the data storage and quality layer with data stores in the data source layer and, in the process, avoiding the need to know to know exactly how these data stores are organized. Technology now permits SQL access to data even if it is not stored in a relational database.
Data Preparation layer -This layer is concerned with the assembly and preparation of data for loading into data marts. The usual practice is to per-calculate the values that are loaded into OLAP data repositories to increase access speed. Data mining is concern with exploring large volume of data to determine patterns and trends of information. Data mining often identifies patterns that are counterintuitive due to number and complexity of data relationships. Data quality needs to be very high to not corrupt the result.
Metadata repository layer - Metadata are data about data. The information held in metadata layer needs to extend beyond data structure names and formats to provide detail on business purpose and context. The metadata layer should be comprehensive in scope, covering data as they flow between the various layers, including documenting transformation and validation rules.
Warehouse Management Layer -The function of this layer is the scheduling of the tasks necessary to build and maintain the DW and populate data marts. This layer is also involved in administration of security.
Application messaging layer -This layer is concerned with transporting information between the various layers. In addition to business data, this layer encompasses generation, storage and targeted communication of control messages.
Internet/Intranet layer - This layer is concerned with basic data communication. Included here are browser based user interface and TCP/IP networking.
Various analysis models used by data architects/ analysis follows:
Activity or swim-lane diagram - De-construct business processes.
Entity relationship diagram -Depict data entities and how they relate. These data analysis methods obviously play an important part in developing an enterprise data model. However, it is also crucial that knowledgeable business operative are involved in the process. This way proper understanding can be obtained of the business purpose and context of the data. This also mitigates the risk of replication of suboptimal data configuration from existing systems and database into DW.
The following were incorrect answers:
Desktop access layer or presentation layer is where end users directly deal with information. This layer includes familiar desktop tools such as spreadsheets, direct querying tools, reporting and analysis suits offered by vendors such as Congas and business objects, and purpose built application such as balanced source cards and digital dashboards.
Data Mart layer - Data mart represents subset of information from the core DW selected and organized to meet the needs of a particular business unit or business line. Data mart can be relational databases or some form on-line analytical processing (OLAP) data structure.
Data access layer - his layer operates to connect the data storage and quality layer with data stores in the data source layer and, in the process, avoiding the need to know to know exactly how these data stores are organized. Technology now permits SQL access to data even if it is not stored in a relational database.
The following reference(s) were/was used to create this question:
CISA review manual 2014 Page number 188

NEW QUESTION: 2
회사에는 IT 애플리케이션을 호스팅하는 여러 AWS 계정이 있습니다. Amazon CloudWatch Logs 에이전트는 모든 Amazon EC2 인스턴스에 설치됩니다. 이 회사는 모든 보안 이벤트를 로그 스토리지 전용의 중앙 집중식 AWS 계정으로 집계하려고 합니다.
보안 관리자는 여러 AWS 계정에서 거의 실시간으로 이벤트를 수집하고 상관 관계를 수행해야 합니다.
어떤 솔루션이 이러한 요구 사항을 충족합니까?
A. 로깅 AWS 계정의 CloudWatch Logs로 이벤트를 전달하도록 각 애플리케이션 AWS 계정에서 CloudWatch Logs 스트림을 구성합니다. 로깅 AWS 계정에서 Amazon Kinesis Data Firehose 스트림을 Amazon CloudWatch Events에 가입하고 스트림을 사용하여 Amazon S3에서 로그 데이터를 유지합니다.
B. 로깅 AWS 계정의 Amazon Kinesis Data Firehose 스트림에 데이터를 게시하고 AWS Lambda 함수를 사용하여 스트림에서 메시지를 읽고 메시지를 Data Firehose로 푸시하고 Amazon S3에 데이터를 유지하도록 CloudWatch Logs 에이전트를 구성합니다.
C. CloudWatch Logs를 볼 수있는 권한으로 각 애플리케이션 AWS 계정에서 Log Audit IAM 역할 생성, Log Audit 역할을 수행하도록 AWS Lambda 기능 구성 및 CloudWatch Logs 데이터를 Amazon S3 버킷으로 매시간 내보내기 수행 AWS 계정 로깅.
D. 로깅 계정에서 Amazon Kinesis Data Streams 생성, 각 애플리케이션 AWS 계정의 CloudWatch Logs 스트림에 스트림 구독, Data Streams를 소스로 사용하여 Amazon Kinesis Data Firehose 전송 스트림을 구성하고 로그 데이터를 로깅 AWS 계정 내의 Amazon S3 버킷.
Answer: D
Explanation:
Explanation
The solution uses Amazon Kinesis Data Streams and a log destination to set up an endpoint in the logging account to receive streamed logs and uses Amazon Kinesis Data Firehose to deliver log data to the Amazon Simple Storage Solution (S3) bucket. Application accounts will subscribe to stream all (or part) of their Amazon CloudWatch logs to a defined destination in the logging account via subscription filters.https://aws.amazon.com/blogs/architecture/central-logging-in-multi-account-environments/

NEW QUESTION: 3
AWSアカウントの所有者が複数のIAMユーザーを設定しました。 1人のIAMユーザーにはCloudWatchアクセスのみがあります。彼はアラーム動作を設定しました。これは、CPU使用率がしきい値制限を下回ったときにEC2インスタンスを停止します。この場合はどうなりますか？
A. CloudWatchはアクションが実行されるとインスタンスを停止します
B. CloudWatchアラームを使用してインスタンスを停止することはできません
C. ユーザーはアクションを設定できますが、ユーザーにEC2権限がない場合は実行されません
D. 権限がないため、ユーザーはEC2にアラームを設定できません
Answer: C