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Free HPE2-N69 Exam Braindumps (page: 5)

Page 4 of 11
PDF with 40 Questions

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

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

Answer(s): B

Explanation:

Adaptive ASHA is an enhanced version of ASHA that uses a reinforcement learning approach to select hyperparameter configurations. This allows Adaptive ASHA to select higher-performing configs and clone those configurations, allowing for better performance than ASHA.



What is a benefit of HPE Machine Learning Development Environment mat tends to resonate with executives?

  1. It uses a centralized training architecture that is highly efficient.
  2. It helps DL projects complete faster for a faster ROI.
  3. It helps companies deploy models and generate revenue.
  4. It automatically cleans up data to create better end results.

Answer(s): B

Explanation:

HPE Machine Learning Development Environment is designed to deliver results more quickly than traditional methods, allowing companies to get a return on their investment sooner and benefit from their DL projects faster. This tends to be a benefit that resonates with executives, as it can help them realize their goals more quickly and efficiently.



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?

  1. In the experiment config that you used, look for the "bucket" field under "hyperparameters." This is the UUID for checkpoints.
  2. Use the "det experiment download -top-n I" command, referencing the experiment ID.
  3. In the Web Ul, go to the Task page and click the checkpoint task that has the experiment ID.
  4. Look for a "determined-checkpoint/" bucket within Amazon S3, referencing your experiment I

Answer(s): D

Explanation:

HPE Machine Learning Development Environment uses Amazon S3 to store checkpoints. To find the location of the best checkpoint created during an experiment, you need to look for a "determined- checkpoint/" bucket within Amazon S3, referencing your experiment ID. This bucket will contain all of the checkpoints that were created during the experiment.



What is a reason to use the best tit policy on an HPE Machine Learning Development Environment resource pool?

  1. Ensuring that all experiments receive their fair share of resources
  2. Minimizing costs in a cloud environment
  3. Equally distributing utilization across multiple agents
  4. Ensuring that the highest priority experiments obtain access to more resources

Answer(s): D

Explanation:

The best fit policy on an HPE Machine Learning Development Environment resource pool ensures that the highest priority experiments obtain access to more resources, while still ensuring that all experiments receive their fair share. This allows you to make the most of your resources and prioritize the experiments that are most important to you.






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