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Amazon Yapay Zeka Testi

Model erişimi, bölgeler, veri akışı ve işletme maliyetleri dahil olmak üzere Amazon'nin yapay zeka hizmetlerini ve yapılandırma sorumluluklarını birbirinden ayırır.

İlgili kılavuz yollarıAmazon Yapay Zeka
Soru 1 arasında 8

How does Amazon Bedrock differ from Amazon SageMaker AI at a high level?

Her soruyu önizleyin8 sorular

Testi çözdüğünüzde cevaplar ve açıklamalar görünür.

  1. How does Amazon Bedrock differ from Amazon SageMaker AI at a high level?
    • Bedrock offers managed access to foundation models, while SageMaker AI supports building, training and deploying models
    • They are identical products with different names
    • Bedrock is a physical GPU and SageMaker is a database
    • SageMaker AI is Amazon’s home voice assistant
  2. What should a developer verify before relying on a Bedrock model feature?
    • That every foundation model is available in every region
    • That the model and feature are available for the account, region and chosen configuration
    • That the feature works without permissions
    • That model access guarantees a particular output quality
  3. Which statement about data handling across AWS AI services is supported by the guide?
    • A managed service removes every data responsibility
    • All AWS services use one identical data policy
    • Storage, logging, retrieval and model calls can have separate controls to review
    • Permission settings never affect model requests
  4. What does using a managed AI service change for the application owner?
    • It guarantees legal compliance for every workflow
    • It removes the need to validate model outputs
    • It means no application configuration is needed
    • It reduces some infrastructure work but does not remove responsibility for authorized access and data use
  5. Which deployment record helps reproduce an AWS AI evaluation?
    • The endpoint, model version and configuration used
    • Only the service’s marketing name
    • The user’s browser theme
    • A copy of unrelated training logs
  6. What should a team include when comparing the cost of an AWS AI workflow?
    • Only the model’s parameter count
    • Storage, data transfer, failures, throughput and operational behavior
    • Only the first successful request
    • The number of tabs open in the console
  7. Why can a model offered through Bedrock differ operationally from using its provider’s direct API?
    • The underlying model name makes all terms identical
    • Bedrock guarantees the provider’s exact interface
    • The managed platform can have different integration details, access conditions and configuration
    • Using a managed endpoint removes model versioning
  8. Before production rollout, which test best follows the guide’s service-selection advice?
    • Assume the public feature list applies to every account
    • Test only a model’s training accuracy
    • Skip tests if the service is managed
    • Evaluate representative tasks and failure handling in the intended account and region