Exam Code : HPE2-N69
Exam Name : Using HPE AI and Machine Learning
Vendor Name :
"HP"
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Using HPE AI and Machine Learning
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What is one of the responsibilities of the conductor of an HPE Machine Learning Development Environment cluster?
it downloads datasets for training.
It uploads model checkpoints.
It validates trained models.
It ensures experiment metadata is stored.
What type of interconnect does HPE Machine learning Development System use for high-speed, agent-to-agent communications?
Remote Direct Memory Access (RDMA) overconverged Ethernet (RoCE)
Slingshot
InfiniBand
Data Center Bridging (OCB)-enabled Ethernet
At what FQDN (or IP address) do users access the WebUI Tor an HPE Machine Learning Development cluster?
Any of the agent's in a compute pool
A virtual one assigned to the cluster
The conductor's
Any of the agent's in an aux pool
Your cluster uses Amazon S3 to store checkpoints. You ran an experiment on an HPE
Machine Learning Development Environment cluster, you want to find the location tor the best checkpoint created during the experiment.
What can you do?
In the experiment config that you used, look for the "bucket" field under "hyperparameters." This is the UUID for checkpoints.
Use the "det experiment download -top-n I" command, referencing the experiment I
In the Web Ul, go to the Task page and click the checkpoint task that has the experiment I
Look for a "determined-checkpoint/" bucket within Amazon S3, referencing your experiment I
A customer mentions that the ML team wants to avoid overfitting models. What does this mean?
The team wants to avoid wasting resources on training models with poorly selected hyperparameters.
The team wants to spend less time on creating the code tor models and more time training models.
The team wants to avoid training models to the point where they perform less well on new data.
The team wants to spend less time figuring out which CPUs are available for training models.
What is a benefit of HPE Machine Learning Development Environment, beyond open source Determined AI?
Automated user provisioning
Pipeline-based data management
Distributed training
Automated hyperparameter optimization (HPO)
What are the mechanics of now a model trains?
Decides which algorithm can best meet the use case for the application in question
Adjusts the model's parameter weights such that the model can Better perform its tasks
Tests how accurately the model performs on a wide array of real world data
Detects Data drift of content drift that might compromise the ML model's performance
An ml engineer wants to train a model on HPE Machine Learning Development Environment without implementing hyper parameter optimization (HPO).
What experiment config fields configure this behavior?
profiling: enabled: false
hyperparameters; optimizer:none
searcher: name: single
resources: slots_per_trial: 1
You are meeting with a customer how has several DL models deployed. Out wants to expand the projects.
The ML/DL team is growing from 5 members to 7 members. To support the growing team, the customer has assigned 2 dedicated IT start. The customer is trying to put together an on-prem GPU cluster with at least 14 CPUs.
What should you determine about this customer?
The customer is not ready for an HPE Machine Learning Development solution, but you could recommend open- source Determined Al.
The customer is not ready for an HPE Machine Learning Development solution. Out you could recommend an educational HPE Pointnext ASPS workshop.
The customer is a key target for HPE Machine Learning Development Environment, but not HPE Machine Learning Development System.
The customer is a key target for an HPE Machine Learning Development solution, and you should continue the discussion.
A customer is using fair-share scheduling for an HPE Machine Learning Development Environment resource pool. What is one way that users can obtain relatively more resource slots for their important experiments?
Set the weight to a higher than default value.
Set the weight to a lower than default value.
Set the priority to a lower than default value.
Set the priority to a higher than default value.