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NEW QUESTION: 1
When using IBM WebSphere Portal with a multi-server SSO to work on an IBM Lotus Domino Directory, which option describes the characters that cannot be used in the Lotus Domino server name?
A. special characters
B. underscores or other special characters
C. underscores
D. special characters or digits
Answer: B

NEW QUESTION: 2
You have databases in Azure as shown in the following table.

You are designing a data retention policy.
You need to identify which databases can retain a daily backup for up to 35 days and which databases can retain monthly backups for up to 120 months.
Which databases should you identify? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:


NEW QUESTION: 3
The primary consideration of product management is to know the _____.
A. ability the product has to perform its intended function
B. probability of product's performance with specified parameters
C. ability of a product to be produced within existing parameters
D. functionality the product will provide over its useful life
E. All of the other alternatives apply
Answer: D

NEW QUESTION: 4
커다란 채워진 동물 제조 회사가 급속도로 성장하면서 끊임없이 새로운 동물을 제품 라인에 추가하고 있습니다. COO는 각 새 동물에 대한 고객의 반응을 알고 싶어하며 고객이 제품을 즐기고 있는지 확인하고 향후 제품 아이디어에이 정보를 사용하고자합니다. 소셜 미디어 관리자는 피드백을 캡처해야합니다. 데이터가 수집 된 후, 회사는 비용 효율적이고 실제적인 방법으로 데이터를 분석하고 분류 할 수 있기를 원합니다. 어떻게 할 수 있니? 두 가지를 선택하십시오.
2 정답을 선택하십시오.
A. Amazon Kinesis Stream을 생성하고 하나의 Kinesis 스트림을 사용하여 원시 데이터를 Amazon S3에 복사합니다.
장기 분석 및 과거 참조를 위해 원시 데이터가 Amazon S3에 저장됩니다.
B. EMR을 사용하고 원시 데이터를 Amazon S3에 복사합니다. 장기 분석 및 과거 참조를 위해 원시 데이터가 Amazon S3에 저장됩니다.
C. Redshift를 사용하여 데이터를 처리 및 정규화하고 Amazon ML에서 예측을 요청합니다. Amazon SNS에 데이터가 전송되고 추가 조사를 위해 이메일을 통해 전달됩니다.
D. 람다를 사용하여 데이터를 처리 및 정규화하고 Amazon ML에서 예측을 요청합니다. 데이터는 Lambda를 사용하여 Amazon SNS로 전송되고 추가 조사를 위해 전자 메일을 통해 전달됩니다.
Answer: A,D
Explanation:
설명:
Amazon Kinesis Streams, Lambda, Amazon ML 및 Amazon SES의 조합을 사용하여 확장 가능하고 쉽게 사용자 정의 할 수있는 소셜 청취 플랫폼을 만들었습니다. 이 그림은 ML 모델을 만드는 작업을 묘사하지 않는다는 점에 유의해야합니다. 이 행동은 최소한 한 번은 수행되지만 일반적으로 모델을 최신 상태로 유지하기 위해 정기적으로 수행됩니다. 새 모델을 만드는 빈도는 작업 부하에 따라 다르며 실제로 상황이 변경 될 때 모델을보다 정확하게 만들기 위해 수행됩니다.
참고:
https://d0.awsstatic.com/whitepapers/Big_Data_Analytics_Options_on_AWS.pdf