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NEW QUESTION: 1
Access restrictions have been modified and updated to honor any missing restriction scenarios. Some of these include:
Response:
A. All of above.
B. Competitor search in Feeds and Partner search in Feeds
C. Employee enterprise search and Contact search in Feeds
D. Employee search in Feeds
E. Partner enterprise search and Partner contact search in feeds and Partner contact enterprise search
Answer: A
NEW QUESTION: 2
A. acct (0) ;
B. acct.changeAmount(-acct.getAmount());
C. acct. getAmount () = 0;
D. acct.changeAmount(-acct.amount);
E. this.amount = 0;
F. acct.amount = 0;
G. acct.changeAmount(0);
H. amount = 0;
Answer: A,E,F
NEW QUESTION: 3
기계 학습 팀에는 Amazon S3에 여러 개의 대규모 CSV 데이터 세트가 있습니다. 역사적으로 Amazon SageMaker Linear Learner 알고리즘으로 구축 된 모델은 비슷한 크기의 데이터 세트를 학습하는 데 몇 시간이 걸렸습니다. 팀의 리더는 교육 과정을 가속화 해야 합니다.
기계 학습 전문가는 이 문제를 해결하기 위해 무엇을 할 수 있습니까?
A. Amazon SageMaker 파이프 모드를 사용합니다.
B. Amazon Machine Learning을 사용하여 모델을 교육합니다.
C. Amazon Kinesis를 사용하여 데이터를 Amazon SageMaker로 스트리밍합니다.
D. AWS Glue를 사용하여 CSV 데이터 세트를 JSON 형식으로 변환합니다.
Answer: A
Explanation:
Amazon SageMaker Pipe mode streams the data directly to the container, which improves the performance of training jobs. In Pipe mode, your training job streams data directly from Amazon S3. Streaming can provide faster start times for training jobs and better throughput. With Pipe mode, you also reduce the size of the Amazon EBS volumes for your training instances. B would not apply in this scenario. C is a streaming ingestion solution, but is not applicable in this scenario. D transforms the data structure