HPE0-S59 Practice Test

HPE0-S59 Practice Test practice questions, page 5 of 6, with answer review and explanations.

Question 21

Which challenge does distributed training with model parallelization address?

  1. Fine-tuning models when data scientists are unsure which pretrained model will work best for their use case
  2. Accelerating experimentation on servers with multi-core processors
  3. Training very large models that cannot fit on a single GPU
  4. Avoiding drift by training multiple different models that check each other's results

Question 22

What indicates that organization is more advanced than a beginner, but is still an early AI user?

  1. The organization has standardized AI development and deployment processes that they want to scale.
  2. The organization has deployed some models but lacks standardization for their tools and processes.
  3. The organization has a data scientist on staff but no models in production.
  4. The organization has identified use cases for AI, but does not yet have a team to work on the projects.

Question 23

An organization wants to deploy a computer vision model to analyze security video. Which NVIDIA GPU is well suited to this use case, providing the necessary performance in a cost-effective manner?

  1. NVIDIA GH200
  2. NVIDIA L4
  3. NVIDIA P100
  4. NVIDIA H100

Question 24

What is a requirement for supervised training?

  1. Collecting performance metrics during the training process
  2. Having a team with multiple members collaborate across the ML/DL lifecycle
  3. Using tools such as Prometheus to enhance visibility
  4. Adding labels to training data

Question 25

What benefit is provided by the buffer design in HPE SN 4000 Series (NVIDIA Spectrum) switches?

  1. It offers multiple shared pools with configurable sizes to meet the needs of different stages of the ML/DL lifecycle.
  2. It is segmented across port groups to ensure confidentiality for sensitive data.
  3. It is split across each port to ensure losslessness for critical traffic.
  4. It is shared to better handle bursty traffic, such as for AI workloads.