HPE2-N69 試験問題を無料オンラインアクセス

What is the role of a hidden layer in an artificial neural network (ANN)?

• A.It is responsible for making the final decision about how to label a record, based on weighted input from preceding layers.
• B.It does not play a role during the forward pass of data through the ANN, but it helps to optimize during the backward pass.
• C.It is responsible for passively reformatting data for use in the ANN.
• D.It receives and weighs inputs from the preceding layer and produces outputs for the next layer.

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 I is allowed to finish. Then Trial 3 is scheduled.
• C.Trial 1 is allowed to finish. Then Trial 2 is scheduled.
• D.Trial 2 is scheduled on 8 of the slots. Then, alter Trial 1 has finished, it receives 16 more slots.

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 fails, and the ML engineer must manually restart it from the latest checkpoint using the WebUI.
• 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 trial tails, and the ML engineer must restart it manually by re-running the experiment.
• D.The conductor reschedules the trial on another available GPU in the pool, and the trial restarts from the latest checkpoint.

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.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.HPO is a joint ml and IT Ops effort, and engineers lack deep enough integration with the IT team.

You are proposing an HPE Machine Learning Development Environment solution for a customer. On what do you base the license count?

• A.The number of servers in the cluster
• B.The number of processor cores on all servers in the cluster
• C.The number of agent GPUs
• D.The number of processor cores on agents