01. 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 trial tails, and the ML engineer must restart it manually by re-running the experiment.
b) The concluded reschedules the trial on another available GPU in the pool, and the trial restarts from the state of the latest training workload.
c) The conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.
d) The trial fails, and the ML engineer must manually restart it from the latest checkpoint using the WebUI.
02. You want to open the conversation about HPE Machine Learning Development Environment with an IT contact at a customer. What can be a good discovery question?
a) How long does it currently take for a DL training to run the backward pass?
b) How much do you understand about building ML and DL models?
c) How much time do you spend managing the ML infrastructure?
d) What frustrations do you have with existing ML deployment and differencing solutions?
03. What is a benefit or HPE Machine Learning Development Environment, beyond open source Determined AI?
a) Experiment tracking
b) Model Inferencing
c) Distributed training
d) Premium dedicated support
04. 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; 1 trial (Trial 3) that needs 24 slots; priority 1
What happens?
a) Trial 1 is allowed to finish. Then Trial 3 is scheduled.
b) Trial 2 is scheduled on 8 of the slots. Then, alter Trial 1 has finished, it receives 16 more slots.
c) Trial 1 is allowed to finish. Then Trial 2 is scheduled.
d) Trial 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots.
05. What is one of the responsibilities of the conductor of an HPE Machine Learning Development Environment cluster?
a) It downloads datasets for training.
b) It uploads model checkpoints.
c) It validates trained models.
d) It ensures experiment metadata is stored.
06. An ML engineer is running experiments on HPE Machine Learning Development Environment. The engineer notices all of the checkpoints for a trial except one disappear after the trial ends. The engineer wants to Keep more of these checkpoints.
What can you recommend?
a) Adjusting how many of the latest and best checkpoints are saved in the experiment config's checkpoint storage settings.
b) Monitoring ongoing trials In the WebUl and clicking checkpoint nags to auto-save the desired checkpoints.
c) Double-checking that the checkpoint storage location is operating under 90% of total capacity.
d) Adjusting the checkpoint storage settings to save checkpoints to a shared file system instead of cloud storage.
07. 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) Letting the conductor automatically determine which servers to use for each experiment, based on the number of resource slots required
b) Establishing multiple compute resource pools on the cluster, one tor servers or each type
c) Deploying servers with 8 GPUs as agents and using the conductor to run experiments that require only 2 GPUs
d) Deploying two HPE Machine Learning Development Environment clusters, one tor each server type
08. What common challenge do ML teams lace in implementing hyperparameter optimization (HPO)?
a) HPO is a joint ml and IT Ops effort, and engineers lack deep enough integration with the IT team.
b) They cannot implement HPO on TensorFlow models, so they must move their models to a new framework.
c) Implementing HPO manually can be time-consuming and demand a great deal of expertise.
d) ML teams struggle to find large enough data sets to make HPO feasible and worthwhile.
09. You want to set up a simple demo cluster for HPE Machine Learning Development Environment for the open source Determined all on a local machine. Which OS Is supported?
a) HP-UX v11i
b) Windows Server 2016 or above
c) Windows 10 or above
d) Red Hat 7-based Linux
10. What is a benefit of HPE Machine Learning Development Environment mat tends to resonate with executives?
a) It uses a centralized training architecture that is highly efficient
b) It automatically cleans up data to create better end results.
c) It helps companies deploy models and generate revenue.
d) It helps DL projects complete faster for a faster ROI.
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