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NEW QUESTION: 1
You want to configure a device to select an OSPF-learned route as the preferred path over an EBGP- learned route Which action must you take?
A. Decrease the OSPF cost
B. Decrease the OSPF administative distance
C. Increase the OSPF cost
D. Increase the OSPF administrative distance
Answer: B

NEW QUESTION: 2
사진 공유 모바일 앱을 디자인하면 응용 프로그램에서 모든 사진을 단일 Amazon S3 버킷에 저장합니다.
사용자는 모바일 장치에서 Amazon S3로 직접 사진을 업로드하고 Amazon S3에서 직접 자신의 사진을보고 다운로드 할 수 있습니다.
수백만 명의 잠재적 인 사용자를 가능한 가장 안전한 방법으로 처리하도록 보안을 구성하려고 합니다. 새로운 사용자가 사진 공유 모바일 응용 프로그램에 등록 할 때 서버 측 응용 프로그램은 어떻게 해야 합니까?
A. 적절한 권한을 가진 AWS Security Token Service를 사용하여 장기 자격 증명 집합 만들기 모바일 앱에 이러한 자격 증명을 저장하고이를 사용하여 Amazon S3에 액세스합니다.
B. Amazon DynamoDB에서 사용자의 정보를 기록합니다. 사용자가 모바일 앱을 사용하는 경우 AWS 보안 토큰 서비스 (적절한 권한 있음)를 사용하여 임시 자격 증명 만들기 모바일 앱의 메모리에 이러한 자격 증명을 저장하고이를 사용하여 Amazon S3에 액세스합니다. 다음에 사용자가 모바일 앱을 실행할 때 새 자격 증명을 생성합니다.
C. IAM 사용자를 만듭니다. IAM 사용자에 대한 적절한 권한으로 버킷 정책 업데이트 액세스 키 및 비밀 키 생성 IAM 사용자에 대한 키를 모바일 앱에 저장하고 이러한 자격 증명을 사용하여 Amazon S3에 액세스합니다.
D. Amazon RDS에 사용자의 정보를 기록하고 적절한 권한으로 IAM에 역할을 만듭니다. 사용자가 모바일 앱을 사용하는 경우 AWS 보안 토큰 서비스 'AssumeRole'기능을 사용하여 임시 자격 증명을 만듭니다. 모바일 앱의 메모리에 이러한 자격 증명을 저장하고이를 사용하여 Amazon S3에 액세스합니다. 다음에 사용자가 모바일 앱을 실행할 때 새 자격 증명을 생성합니다.
E. IAM 사용자를 만듭니다. IAM 사용자에게 적절한 권한 할당 IAM 사용자에 대한 액세스 키와 비밀 키를 생성하고 모바일 앱에 저장하고 이러한 자격 증명을 사용하여 Amazon S3에 액세스합니다.
Answer: D

NEW QUESTION: 3
Which of the following statements about the Wide & Deep Learning model are true? (Select 2 answers.)
A. The wide model is used for memorization, while the deep model is used for generalization.
B. A good use for the wide and deep model is a recommender system.
C. A good use for the wide and deep model is a small-scale linear regression problem.
D. The wide model is used for generalization, while the deep model is used for memorization.
Answer: A,B
Explanation:
Can we teach computers to learn like humans do, by combining the power of memorization and generalization? It's not an easy question to answer, but by jointly training a wide linear model (for memorization) alongside a deep neural network (for generalization), one can combine the strengths of both to bring us one step closer. At Google, we call it Wide & Deep Learning. It's useful for generic large-scale regression and classification problems with sparse inputs (categorical features with a large number of possible feature values), such as recommender systems, search, and ranking problems.
Reference: https://research.googleblog.com/2016/06/wide-deep-learning-better-together-with.html

NEW QUESTION: 4
What is the recommended way to treat a current customer capability weakness?
A. Highlight this in the next executive briefing, to get credit for finding out gaps
B. Use objective data to describe this gap, in a Business Capability Assessment
C. Expand scope, urgently staffing a deep dive so the customer can most quickly fix the weakness
D. Document this in a way so the IT executive avoids criticism
Answer: B