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  • Exam Name: Using HPE AI and Machine Learning
  • Last Update: 21-Feb-2024
  • Questions and Answers: 40
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HPE2-N69 Questions and Answers

Question # 1

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

A.

Hospitality

B.

K-12education

C.

Retail

D.

Manufacturing

Question # 2

You are meeting with a customer how has several DL models deployed. Out wants to expand the projects.

The ML/DL team is growing from 5 members to 7 members. To support the growing team, the customer has assigned 2 dedicated IT start. The customer is trying to put together an on-prem GPU cluster with at least 14 CPUs.

What should you determine about this customer?

A.

The customer is not ready for an HPE Machine Learning Development solution, but you could recommend open-source Determined Al.

B.

The customer is not ready for an HPE Machine Learning Development solution. Out you could recommend an educational HPE Pointnext ASPS workshop.

C.

The customer is a key target for HPE Machine Learning Development Environment, but not HPE Machine Learning Development System.

D.

The customer is a key target for an HPE Machine Learning Development solution, and you should continue the discussion.

Question # 3

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?

A.

HP-UX v11i

B.

Windows Server 2016 or above

C.

Windows 10 or above

D.

Red Hat 7-based Linux

Question # 4

What type of interconnect does HPE Machine learning Development System use for high-speed, agent-to-agent communications?

A.

Remote Direct Memory Access (RDMA) overconverged Ethernet (RoCE)

B.

Slingshot

C.

InfiniBand

D.

Data Center Bridging (OCB)-enabled Ethernet

Question # 5

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

A.

HPO is a joint ml and IT Ops effort, and engineers lack deep enough integration with the IT team.

B.

They cannot implement HPO on TensorFlow models, so they must move their models to a new framework.

C.

Implementing HPO manually can be time-consuming and demand a great deal of expertise.

D.

ML teams struggle to find large enough data sets to make HPO feasible and worthwhile.

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