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NEW QUESTION 24
What is one key target vertical (or HPE Machine Learning Development solutions?

• A. K-12education
• B. Hospitality
• C. Retail
• D. Manufacturing

Answer: D

Explanation:
One key target vertical for HPE Machine Learning Development solutions is Manufacturing. Manufacturing businesses are using machine learning to automate processes, reduce costs, and improve safety and quality control. HPE ML solutions provide the tools and technologies to help manufacturers develop and deploy ML models in their production environments, enabling them to optimize and automate their operations.

NEW QUESTION 25
A customer is deploying HPE Machine learning Development Environment on on-prem infrastructure. The customer wants to run some experiments on servers with 8 NVIDIA A too GPUs and other experiments on servers with only Z NVIDIA T4 GPUs. What should you recommend?

• A. Deploying two HPE Machine Learning Development Environment clusters, one tor each server type
• B. Deploying servers with 8 GPUs as agents and using the conductor to run experiments that require only 2 GPUs
• C. Establishing multiple compute resource pools on the cluster, one tor servers or each type
• D. Letting the conductor automatically determine which servers to use for each experiment, based on the number of resource slots required

Answer: C

NEW QUESTION 26
Refer to the exhibit.

You are demonstrating HPE Machine Learning Development Environment, and you show details about an experiment, as shown in the exhibits. The customer asks about what "validation loss' means. What should you respond?

• A. Validation refers to testing how well the current model performs on new data; file lower the loss the better the performance.
• B. Validation loss is metadata that indicates how many updates were lost between the conductor and agents.
• C. Validation loss refers to the loss detected during the backward pass of training, while training loss refers to loss during the forward pass.
• D. Validation refers to an assessment of how efficient the model code is; the lower the loss the lower the demand on GPU memory resources.

Answer: A

Explanation:
Validation loss is a metric used to measure how well the model is performing on unseen data. It is calculated by taking the difference between the predicted values and the actual values. The lower the validation loss, the better the model's performance on new data.

NEW QUESTION 27
Compared to Asynchronous Successive Halving Algorithm (ASHA), what is an advantage of Adaptive ASHA?

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

Answer: B

NEW QUESTION 28
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. Tests how accurately the model performs on a wide array of real world data
• D. Adjusts the model's parameter weights such that the model can Better perform its tasks

Answer: A

NEW QUESTION 29
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