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HP HPE2-N69 Exam Syllabus Topics:
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HP Using HPE Cray AI Development Environment Sample Questions (Q32-Q37):
NEW QUESTION # 32
An HPE Machine Learning Development Environment resource pool uses priority scheduling with preemption disabled. Currently Experiment 1 Trial I is using 32 of the pool's 40 total slots; it has priority 42. Users then run two more experiments:
* Experiment 2:1 trial (Trial 2) that needs 24 slots; priority 50
* Experiment 3; l trial (Trial 3) that needs 24 slots; priority I
What happens?
- A. Trial 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots.
- B. Trial 2 is scheduled on 8 of the slots. Then, alter Trial 1 has finished, it receives 16 more slots.
- C. Trial I is allowed to finish. Then Trial 3 is scheduled.
- D. Trial 1 is allowed to finish. Then Trial 2 is scheduled.
Answer: C
NEW QUESTION # 33
What is a benefit of HPE Machine Learning Development Environment, beyond open source Determined AI?
- A. Distributed training
- B. Pipeline-based data management
- C. Automated user provisioning
- D. Automated hyperparameter optimization (HPO)
Answer: B
NEW QUESTION # 34
What common challenge do ML teams lace in implementing hyperparameter optimization (HPO)?
- A. ML teams struggle to find large enough data sets to make HPO feasible and worthwhile.
- B. HPO is a joint ml and IT Ops effort, and engineers lack deep enough integration with the IT team.
- C. They cannot implement HPO on TensorFlow models, so they must move their models to a new framework.
- D. Implementing HPO manually can be time-consuming and demand a great deal of expertise.
Answer: D
Explanation:
Implementing hyperparameter optimization (HPO) manually can be time-consuming and demand a great deal of expertise. HPO is not a joint ML and IT Ops effort and it can be implemented on TensorFlow models, so these are not the primary challenges faced by ML teams. Additionally, ML teams often have access to large enough data sets to make HPO feasible and worthwhile.
NEW QUESTION # 35
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. A lack of understanding of the DL model architecture by the NL engineering team
- D. The requirement that the ML team must wait for the IT team to initiate each new training process
Answer: C
NEW QUESTION # 36
A trial is running on a GPU slot within a resource pool on HPE Machine Learning Development Environment. That GPU fails. What happens next?
- A. The conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.
- B. The trial fails, and the ML engineer must manually restart it from the latest checkpoint using the WebUI.
- C. The concluded reschedules the trial on another available GPU in the pool, and the trial restarts from the state of the latest training workload.
- D. The trial tails, and the ML engineer must restart it manually by re-running the experiment.
Answer: A
Explanation:
If a GPU fails during a trial running on a resource pool on HPE Machine Learning Development Environment, the conductor will reschedule the trial on another available GPU in the pool, and the trial will restart from the latest checkpoint. The trial will not fail, and the ML engineer will not have to manually restart it from the latest checkpoint using the WebUI.
NEW QUESTION # 37
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