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NEW QUESTION: 1
GoodCreditLoans is a financial company that wants to create an automated loan approval application. They currently have an application that uses hard-coded logic and manual checks to assess loan applications, generate payment reminders, send marketing emails to potential clients, and create monthly sales reports. The existing application uses these entities: MarketingContact, Equipment, Loan, Reminder, Applicant, SalesReport.
Here are some example rules harvested during rule discovery for the new automated loan approval application:

What two entities are needed to write the loan approval business rules? (Choose two.)
A. SalesReport
B. Reminder
C. Applicant
D. Loan
E. MarketingContact
Answer: B,C

NEW QUESTION: 2
What word is missing from the following excerpt of a named.conf file?

A. networks
B. acl
C. net
D. group
E. list
Answer: B

NEW QUESTION: 3
If there are four I/O module links from the Cisco Unified Computing System 5108 Blade Chassis to the Cisco Unified Computing System 6248UP Fabric Interconnect, which dynamic vNICs can be defined?
A. 0
B. 1
C. 2
D. 3
E. 4
F. 5
Answer: A

NEW QUESTION: 4
글로벌 제화업자는 자신을 관리하고 다른 할인 매장의 브로커를 통해 판매하는 1,000 개의 소매점을 운영하며 온라인으로도 판매합니다. 이러한 채널은 완전히 독립적입니다. COO가 포괄적 인 통찰력을 가질 수 있도록 이 데이터 세트를 통합하는 시스템은 없습니다. 채널 전반에 대한 전사적 인 그림을 제공하고 필요한 경우 애드혹 분석을 수행 할 수 있어야 합니다. 어떻게 구성 할 수 있습니까? 두 가지를 선택하십시오.
2 정답을 선택하십시오.
A. Amazon EMR을 사용하여 데이터를 테이블로로드, 정렬, 배포 및 압축하여 분석 쿼리를 효율적으로 병렬로 실행할 수 있습니다. 마지막으로 분석을 시각화하기 위해 Amazon QuickSight 또는 Amazon EMR에 대한 OBDC / JDBC 연결을 통해 여러 파트너 시각화 플랫폼 중 하나를 사용할 수 있습니다.
B. 먼저 다양한 소스의 모든 데이터를 Amazon DynamoDB에 가져옵니다. 그런 다음 Amazon Redshift를 사용하여 소스 형식의 데이터를 대상 및 형식으로 변환하고 정제합니다.
C. 먼저 다양한 소스의 모든 데이터를 Amazon S3에 가져옵니다. 그런 다음 Amazon EMR을 사용하여 소스 형식의 데이터를 대상 및 원하는 형식으로 변환하고 정제합니다.
D. Amazon Redshift를 사용하여 데이터를 테이블에로드, 정렬, 배포 및 압축하여 분석 쿼리를 효율적으로 병렬로 실행할 수 있습니다. 마지막으로, 분석을 시각화하기 위해 Amazon Redshift에 대한 OBDC / JDBC 연결을 통해 Amazon QuickSight 또는 여러 파트너 시각화 플랫폼 중 하나를 사용할 수 있습니다.
Answer: C,D
Explanation:
설명:
엔터프라이즈 데이터웨어 하우스는이 문제를 해결하는 훌륭한 방법입니다. 데이터웨어 하우스는 세 가지 채널의 다양한 시스템과 날씨 및 경제 데이터에 대한 공개 기록에서 데이터를 수집해야합니다. 각 데이터 원본은 데이터웨어 하우스가 매일 소비하는 데이터를 보냅니다. 각 데이터 소스는 다르게 구조화 될 수 있으므로 추출, 변환 및로드 (ETL) 프로세스가 수행되어 데이터를 공통 구조로 다시 형식화합니다. 그런 다음 모든 소스의 데이터에서 동시에 분석을 수행 할 수 있습니다. 참조 :
https://d0.awsstatic.com/whitepapers/Big_Data_Analytics_Options_on_AWS.pdf