Online Access Free HPE2-N69 Exam Questions

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.Complex and time-consuming data cleansing process
• B.Complex model deployment processes
• C.Complex and time-consuming hyperparameter optimization (HPO)
• D.Time-consuming data collection

You want to set up a simple demo cluster for HPE Machine Learning Development Environment (or the open source Determined Al) on Amazon Web Services (AWS). You plan to use "det deploy" to set up the cluster. What is one prerequisite?

• A.Recording the name of a valid AWS EC2 keypair
• B.installing the NVIDIA Container Toolkit on your local machine
• C.Manually creating the AWS EC2 instance with a PostgreSQL database
• D.Adding Amazon Elastic Kubernetes Services (EKS) to your AWS account

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

What is one of the responsibilities of the conductor of an HPE Machine Learning Development Environment cluster?

• A.It validates trained models.
• B.it downloads datasets for training.
• C.It ensures experiment metadata is stored.
• D.It uploads model checkpoints.

What distinguishes deep learning (DL) from other forms of machine learning (ML)?

• A.Models defined with Apache Spark rather than MapReduce
• B.Models trained through multiple training processes implemented by different team members
• C.Models that are trained through unsupervised, rather than supervised, training
• D.Models based on neural networks with interconnected layers of nodes, including multiple hidden layers