Task 3.1: Select Deployment Infrastructure Based on Existing Architecture and Requirements MLA-C01 Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   A team wants to deploy a model using a framework that is already supported by SageMaker's built-in algorithms. Which container option is the most straightforward and requires the least custom setup?

   • AECS container
   • BAWS Lambda container
   • CCustom container
   • DProvided container
   Reveal Solution Discussion Select an answer first
2. 17foundation · easy

   A data science team wants to deploy a trained model as a REST API that can scale automatically and integrate with other AWS services without managing the underlying servers. Which AWS service is most appropriate for this task?

   • AAWS Batch
   • BAmazon EC2
   • CAWS Lambda
   • DAmazon SageMaker AI
   Reveal Solution Discussion Select an answer first
4. 18foundation · easy

   A company wants to deploy a model that handles unpredictable traffic patterns, with no need for a persistent endpoint. They want to pay only for the compute time used during inference requests. Which SageMaker endpoint type is most appropriate?

   • AServerless endpoint
   • BBatch transform
   • CAsynchronous endpoint
   • DReal-time endpoint
   Reveal Solution Discussion Select an answer first
5. 19foundation · easy

   A team is training a deep learning model on large image datasets. Which type of compute resource is most appropriate for this training workload?

   • AStorage-optimized instances
   • BMemory-optimized instances
   • CGPU instances
   • DCPU instances
   Reveal Solution Discussion Select an answer first
6. 20foundation · easy

   What is the primary benefit of using SageMaker Neo for edge deployment?

   • AManaged endpoint scaling
   • BSimplified data labeling
   • CReduced model size and faster inference on edge devices
   • DAutomatic model retraining
   Reveal Solution Discussion Select an answer first