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NEW QUESTION 34
A customer mentions that the ML team wants to avoid overfitting models. What does this mean?
- A. The team wants to avoid wasting resources on training models with poorly selected hyperparameters.
- B. The team wants to spend less time on creating the code tor models and more time training models.
- C. The team wants to spend less time figuring out which CPUs are available for training models.
- D. The team wants to avoid training models to the point where they perform less well on new data.
NEW QUESTION 35
What are the mechanics of now a model trains?
- A. Detects Data drift of content drift that might compromise the ML model’s performance
- B. Tests how accurately the model performs on a wide array of real world data
- C. Decides which algorithm can best meet the use case for the application in question
- D. Adjusts the model’s parameter weights such that the model can Better perform its tasks
NEW QUESTION 36
What role do HPE ProLiant DL325 servers play in HPE Machine Learning Development System?
- A. They run validation and checkpoint workloads.
- B. They run training workloads that do not require GPUs.
- C. They run non-distributed training workloads.
- D. They host management software such as the conductor and HPCM.
NEW QUESTION 37
A company has recently expanded its ml engineering resources from 5 CPUs 1012 GPUs.
What challenge is likely to continue to stand in the way of accelerating deep learning (DU training?
- A. A lack of adequate power and cooling for the GPU-enabled servers
- B. The complexity of adjusting model code to distribute the training process across multiple GPUs
- C. The requirement that the ML team must wait for the IT team to initiate each new training process
- D. A lack of understanding of the DL model architecture by the NL engineering team
The complexity of adjusting model code to distribute the training process across multiple GPUs. Deep learning (DL) training requires a large amount of computing power and can be accelerated by using multiple GPUs. However, this requires adjusting the model code to distribute the training process across the GPUs, which can be a complex and time-consuming process. Thus, the complexity of adjusting the model code is likely to continue to be a challenge in accelerating DL training.
NEW QUESTION 38
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