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HP HPE2-N69 Prüfungsplan:

Thema Einzelheiten
Thema 1
  • 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)
Thema 2
  • 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
Thema 3
  • Size HPE Machine Learning Development Environment and System solutions
  • Understand machine learning (ML) and deep learning (DL) fundamentals
Thema 4
  • Qualify customers for HPE Machine Learning Development Environment and System
  • Articulate the business case for HPE Machine Learning Development solutions
Thema 5
  • Explain how HPE Machine Learning Development Environment helps customers surmount their challenges
  • Run a proof of concept (PoC)

>> HPE2-N69 Prüfungsfragen <<

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HP Using HPE Cray AI Development Environment HPE2-N69 Prüfungsfragen mit Lösungen (Q20-Q25):

20. Frage
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 a key target for HPE Machine Learning Development Environment, but not HPE Machine Learning Development System.
  • B. The customer is not ready for an HPE Machine Learning Development solution, but you could recommend open-source Determined Al.
  • C. The customer is a key target for an HPE Machine Learning Development solution, and you should continue the discussion.
  • D. The customer is not ready for an HPE Machine Learning Development solution. Out you could recommend an educational HPE Pointnext ASPS workshop.

Antwort: C Begründung:
The customer is a key target for an HPE Machine Learning Development solution, and you should continue the discussion. With the customer's dedicated IT staff, the customer is ready to deploy an on-premise GPU cluster with at least 14 CPUs. The HPE Machine Learning Development Environment is a comprehensive solution that provides the tools and technologies required to develop, manage, and deploy ML models. It includes a distributed training framework, an orchestration layer, a powerful development environment, and an integrated MLOps platform. With this solution, the customer can expand their ML/DL projects and scale up their team.
21. Frage
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)

Antwort: D Begründung:
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.
22. Frage
An HPE Machine Learning Development Environment resource pool uses priority scheduling with preemption disabled. Currently Experiment 1 Trial I is using 32 of the pool's 40 total slots; it has priority 42. Users then run two more experiments:
* Experiment 2:1 trial (Trial 2) that needs 24 slots; priority 50
* Experiment 3; l trial (Trial 3) that needs 24 slots; priority I
What happens?

  • A. Trial 1 is allowed to finish. Then Trial 2 is scheduled.
  • B. Trial I is allowed to finish. Then Trial 3 is scheduled.
  • C. Trial 2 is scheduled on 8 of the slots. Then, alter Trial 1 has finished, it receives 16 more slots.
  • D. Trial 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots.

Antwort: D Begründung:
Trial 3 is scheduled on 8 of the slots. Then, after Trial 1 has finished, it receives 16 more slots. This is because priority scheduling is used in the HPE Machine Learning Development Environment resource pool, which means higher priority tasks will be given priority over lower priority tasks. As such, Trial 3 with priority 1 will be given priority over Trial 2 with priority 50.
23. Frage
What is a benefit of HPE Machine Learning Development Environment, beyond open source Determined AI?

  • A. Distributed training
  • B. Automated user provisioning
  • C. Pipeline-based data management
  • D. Automated hyperparameter optimization (HPO)

Antwort: D Begründung:
One of the main benefits of HPE Machine Learning Development Environment is its ability to automate the process of hyperparameter optimization (HPO). HPO is a process of automatically tuning the hyperparameters of a model during training, which can greatly improve a model's performance. HPE ML DE provides automated HPO, making the process of tuning and optimizing the model much easier and more efficient.
24. Frage
An HPE Machine Learning Development Environment cluster has this resource pool:
Name: pool 1
Location: On-prem
Agents: 2
Aux containers per agent: 100
Total slots: 0
Which type of workload can run In pool I?

  • A. GPU Jupyter Notebook
  • B. Training
  • C. Validation
  • D. CPU-only Jupyter Notebook

Antwort: D
25. Frage
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