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HPE2-N69 Exam
- Topic 1: Explain how HPE Machine Learning Development Environment helps customers surmount their challenges/ Run a proof of concept (PoC)
- Topic 2: Explain how the Machine Learning Development Environment uses resources and schedules workloads/ Understand the challenges customers face in training DL models
- Topic 3: Demonstrate running a variety of experiment types on the HPE Machine Learning Development Environment/ Describe how HPE Machine Learning Development Environment fits in the market
- Topic 4: Describe the HPE Machine Learning Development Environment software architecture and deployment options/ Have a conversation with customers about machine learning (ML) and deep learning (DL)
- Topic 5: Size HPE Machine Learning Development Environment and System solutions/ Understand machine learning (ML) and deep learning (DL) fundamentals
- Topic 6: Qualify customers for HPE Machine Learning Development Environment and System/ Articulate the business case for HPE Machine Learning Development solutions
- Topic 7: Demonstrate and explain how to use HPE Machine Learning Development Environment/ Describe the architecture for HPE Machine Learning Development solutions
Free HP HPE2-N69 Exam Actual Questions
The questions for HPE2-N69 were last updated On Sep. 15, 2023
A customer mentions that the ML team wants to avoid overfitting models. What does this mean?
Overfitting occurs when a model is trained too closely on the training data, leading to a model that performs very well on the training data but poorly on new data. This is because the model has been trained too closely to the training data, and so cannot generalize the patterns it has learned to new data. To avoid overfitting, the ML team needs to ensure that their models are not overly trained on the training data and that they have enough generalization capacity to be able to perform well on new data.
What common challenge do ML teams lace in implementing hyperparameter optimization (HPO)?
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.
You want to set up a simple demo cluster for HPE Machine Learning Development Environment for the open source Determined all on a local machine. Which OS Is supported?
The OS supported for setting up a simple demo cluster for HPE Machine Learning Development Environment for the open source Determined on a local machine is Red Hat 7-based Linux. Red Hat 7-based Linux is an open source operating system that is used extensively in enterprise applications. It provides a stable and secure platform for running applications and is suitable for use in a demo cluster.
What common challenge do ML teams lace in implementing hyperparameter optimization (HPO)?
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.
You want to set up a simple demo cluster for HPE Machine Learning Development Environment for the open source Determined all on a local machine. Which OS Is supported?
The OS supported for setting up a simple demo cluster for HPE Machine Learning Development Environment for the open source Determined on a local machine is Red Hat 7-based Linux. Red Hat 7-based Linux is an open source operating system that is used extensively in enterprise applications. It provides a stable and secure platform for running applications and is suitable for use in a demo cluster.
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