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NEW QUESTION 42
What common challenge do ML teams lace in implementing hyperparameter optimization (HPO)?

  • A. Implementing HPO manually can be time-consuming and demand a great deal of expertise.
  • B. They cannot implement HPO on TensorFlow models, so they must move their models to a new framework.
  • C. ML teams struggle to find large enough data sets to make HPO feasible and worthwhile.
  • D. HPO is a joint ml and IT Ops effort, and engineers lack deep enough integration with the IT team.

Answer: D

 

NEW QUESTION 43
A company has an HPE Machine Learning Development Environment cluster. The ML engineers store training and validation data sets in Google Cloud Storage (GCS). What is an advantage of streaming the data during a trial, as opposed to downloading the data?

  • A. Streaming requires just one bucket, while downloading requires many.
  • B. Setting up streaming is easier that setting up downloading.
  • C. The trial can better separate training and validation data.
  • D. The trial can more quickly start up and begin training the model.

Answer: D

Explanation:
Streaming the data during a trial allows the data to be processed more quickly, as it does not need to be downloaded onto the cluster before training can begin. This means that the trial can start up faster and the model can begin training more quickly.

 

NEW QUESTION 44
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 an aux pool
  • C. Any of the agent's in a compute pool
  • D. The conductor's

Answer: D

Explanation:
The WebUI for an HPE Machine Learning Development cluster can be accessed at the FQDN or IP address of the conductor. The conductor is responsible for managing the cluster and providing access to the WebUI.

 

NEW QUESTION 45
What common challenge do ML teams lace in implementing hyperparameter optimization (HPO)?

  • A. Implementing HPO manually can be time-consuming and demand a great deal of expertise.
  • B. They cannot implement HPO on TensorFlow models, so they must move their models to a new framework.
  • C. HPO is a joint ml and IT Ops effort, and engineers lack deep enough integration with the IT team.
  • D. ML teams struggle to find large enough data sets to make HPO feasible and worthwhile.

Answer: A

Explanation:
Implementing hyperparameter optimization (HPO) manually can be time-consuming and demand a great deal of expertise. HPO is not a joint ML and IT Ops effort and it can be implemented on TensorFlow models, so these are not the primary challenges faced by ML teams. Additionally, ML teams often have access to large enough data sets to make HPO feasible and worthwhile.

 

NEW QUESTION 46
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