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NEW QUESTION # 28
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?
- A. Under "searcher," set "divisor- to 2 to reduce the share of the resource slots that the experiment receives.
- B. Under "searcher," set "max_concurrent_trails" to cap the number of trials run at once by this experiment.
- 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.
Answer: B
NEW QUESTION # 29
You are in a directory on your machine with your experiment config file and your model code. You enter this command:
det experiment create myfile.yaml
You receive this error:
det experiment create: error: the following arguments are required: model_def What should you do?
- A. Make sure that the myfile.yaml tile includes code tor a PyTorchTrial or TFKerasTrial class.
- B. Re-enter the command with "-m" in which is the code filename.
- C. Make sure that you have already logged into the cluster with the "det login'' command.
- D. Re-enter the command with a period (.) at the end.
Answer: A
Explanation:
Make sure that the myfile.yaml tile includes code for a PyTorchTrial or TFKerasTrial class. When creating an experiment with the det experiment create command, you need to specify the model_def parameter to provide the code for the PyTorchTrial or TFKerasTrial class. This code should be specified in the myfile.yaml file, so make sure that the myfile.yaml file includes the code for the model you want to use.
NEW QUESTION # 30
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?
- 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)
Answer: D
Explanation:
The HPE Machine Learning Development Environment specifically addresses Complex and time-consuming hyperparameter optimization (HPO). HPO is a process used to identify the most effective set of hyperparameters for a given machine learning model. HPE's ML Development Environment provides a suite of tools that allow users to quickly and easily design and deploy deep learning models, as well as optimize their hyperparameters to get the best results.
NEW QUESTION # 31
Where does TensorFlow fit in the ML/DL Lifecycle?
- A. it helps engineers use a language like Python to code and trail DL models.
- B. it provides pipelines to manage the complete lifecycle.
- C. It adds system and GPU monitoring to the training process.
- D. It is primarily used to transport trained models to a deployment environment.
Answer: B
Explanation:
TensorFlow provides pipelines to manage the complete lifecycle of ML/DL models, from data ingestion to model training, evaluation, and deployment. It helps engineers use a language like Python to code and train DL models, and it also adds system and GPU monitoring to the training process. Additionally, it can be used to transport trained models to a deployment environment.
NEW QUESTION # 32
What are the mechanics of now a model trains?
- A. Decides which algorithm can best meet the use case for the application in question
- B. Detects Data drift of content drift that might compromise the ML model's performance
- C. Adjusts the model's parameter weights such that the model can Better perform its tasks
- D. Tests how accurately the model performs on a wide array of real world data
Answer: A
NEW QUESTION # {0}33
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