Online Access Free HPE2-N69 Exam Questions

What type of interconnect does HPE Machine learning Development System use for high-speed, agent-to-agent communications?

• A.Remote Direct Memory Access (RDMA) overconverged Ethernet (RoCE)
• B.Data Center Bridging (OCB)-enabled Ethernet
• C.Slingshot
• D.InfiniBand

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.Double-checking that the checkpoint storage location is operating under 90% of total capacity.
• C.Monitoring ongoing trials In the WebUl and clicking checkpoint nags to auto-save the desired checkpoints.
• D.Adjusting the checkpoint storage settings to save checkpoints to a shared file system instead of cloud storage.

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 2 is scheduled on 8 of the slots. Then, alter Trial 1 has finished, it receives 16 more slots.
• B.Trial 3 is scheduled on 8 of the slots. Then, after 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.

At what FQDN (or IP address) do users access the WebUI Tor an HPE Machine Learning Development cluster?

• A.A virtual one assigned to the cluster
• B.Any of the agent's in a compute pool
• C.The conductor's
• D.Any of the agent's in an aux pool

Compared to Asynchronous Successive Halving Algorithm (ASHA), what is an advantage of Adaptive ASHA?

• A.ASHA selects hyperparameter configs entirely at random while Adaptive ASHA clones higher-performing configs.
• B.Adaptive ASHA can train more trials in certain amount of time, as compared to ASHA.
• C.Adaptive ASHA can handle hyperparameters related to neural architecture while ASHA cannot.
• D.Adaptive ASHA tries multiple exploration/exploitation tradeoffs oy running multiple Instances of ASHA.