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NEW QUESTION 32
A customer mentions that the ML team wants to avoid overfitting models. What does this mean?
- A. The team wants to spend less time figuring out which CPUs are available for training models.
- B. The team wants to avoid training models to the point where they perform less well on new data.
- C. The team wants to spend less time on creating the code tor models and more time training models.
- D. The team wants to avoid wasting resources on training models with poorly selected hyperparameters.
Answer: A
NEW QUESTION 33
What common challenge do ML teams lace in implementing hyperparameter optimization (HPO)?
- A. Implementing HPO manually can be time-consuming and demand a great deal of expertise.
- B. ML teams struggle to find large enough data sets to make HPO feasible and worthwhile.
- C. HPO is a joint ml and IT Ops effort, and engineers lack deep enough integration with the IT team.
- D. They cannot implement HPO on TensorFlow models, so they must move their models to a new framework.
Answer: C
NEW QUESTION 34
An HPE Machine Learning Development Environment cluster has this resource pool:
Name: pool 1
Location: On-prem
Agents: 2
Aux containers per agent: 100
Total slots: 0
Which type of workload can run In pool I?
- A. Validation
- B. CPU-only Jupyter Notebook
- C. Training
- D. GPU Jupyter Notebook
Answer: B
NEW QUESTION 35
The 10 agents in "my-compute-poor nave 8 GPUs each, you want to change an experiment config to run on multiple GPUs at once. What Is a valid setting tor "resources_per_trial?
Answer: D
NEW QUESTION 36
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