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NEW QUESTION 25
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. The requirement that the ML team must wait for the IT team to initiate each new training process
- B. The complexity of adjusting model code to distribute the training process across multiple GPUs
- C. A lack of adequate power and cooling for the GPU-enabled servers
- D. A lack of understanding of the DL model architecture by the NL engineering team
NEW QUESTION 26
A company has an HPE Machine Learning Development Environment cluster. The ML engineers store training and validation data sets in Google Cloud Storage (GCS). What is an advantage of streaming the data during a trial, as opposed to downloading the data?
- A. The trial can more quickly start up and begin training the model.
- B. Streaming requires just one bucket, while downloading requires many.
- C. The trial can better separate training and validation data.
- D. Setting up streaming is easier that setting up downloading.
NEW QUESTION 27
A customer is deploying HPE Machine learning Development Environment on on-prem infrastructure. The customer wants to run some experiments on servers with 8 NVIDIA A too GPUs and other experiments on servers with only Z NVIDIA T4 GPUs. What should you recommend?
- A. Deploying servers with 8 GPUs as agents and using the conductor to run experiments that require only 2 GPUs
- B. Deploying two HPE Machine Learning Development Environment clusters, one tor each server type
- C. Establishing multiple compute resource pools on the cluster, one tor servers or each type
- D. Letting the conductor automatically determine which servers to use for each experiment, based on the number of resource slots required
NEW QUESTION 28
What is one key target vertical (or HPE Machine Learning Development solutions?
- A. Retail
- B. Hospitality
- C. K-12education
- D. Manufacturing
NEW QUESTION 29
What is a reason to use the best tit policy on an HPE Machine Learning Development Environment resource pool?
- A. Ensuring that the highest priority experiments obtain access to more resources
- B. Equally distributing utilization across multiple agents
- C. Ensuring that all experiments receive their fair share of resources
- D. Minimizing costs in a cloud environment
NEW QUESTION 30