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Using HPE AI and Machine Learning Question and Answers

Using HPE AI and Machine Learning

Last Update May 1, 2024
Total Questions : 40

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Questions 1

The ML engineer wants to run an Adaptive ASHA experiment with hundreds of trials. The engineer knows that several other experiments will be running on the same resource pool, and wants to avoid taking up too large a share of resources. What can the engineer do in the experiment config file to help support this goal?

Options:

A.  

Under "searcher," set "max_concurrent_trails" to cap the number of trials run at once by this experiment.

B.  

Under "searcher," set "divisor- to 2 to reduce the share of the resource slots that the experiment receives.

C.  

Set the "scheduling_unit" to cap the number of resource slots used at once by this experiment.

D.  

Under "resources.- set 'priority to I to reduce the share of the resource slots mat the experiment receives.

Discussion 0
Questions 2

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?

Options:

A.  

Trial I 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.

Discussion 0
Questions 3

A customer has Men expanding its deep learning (DO prefects and is confronting several challenges. Which of these challenges does HPE Machine Learning Development Environment specifically address?

Options:

A.  

Time-consuming data collection

B.  

Complex model deployment processes

C.  

Complex and time-consuming data cleansing process

D.  

Complex and time-consuming hyperparameter optimization (HPO)

Discussion 0
Questions 4

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?

Options:

A.  

A lack of understanding of the DL model architecture by the NL engineering team

B.  

The complexity of adjusting model code to distribute the training process across multiple GPUs

C.  

A lack of adequate power and cooling for the GPU-enabled servers

D.  

The requirement that the ML team must wait for the IT team to initiate each new training process

Discussion 0
Questions 5

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?

Options:

A.  

In the experiment config that you used, look for the "bucket" field under "hyperparameters." This is the UUID for checkpoints.

B.  

Use the "det experiment download -top-n I" command, referencing the experiment ID.

C.  

In the Web Ul, go to the Task page and click the checkpoint task that has the experiment ID.

D.  

Look for a "determined-checkpoint/" bucket within Amazon S3, referencing your experiment I

D.  

Discussion 0
Questions 6

What common challenge do ML teams lace in implementing hyperparameter optimization (HPO)?

Options:

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.

Discussion 0