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HP HPE7-S01 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Explain HPE Compute AI and HPC Solution Components and Architecture38%- Management and automation
  • 1. Lifecycle management
  • 2. HPE OneView
  • 3. HPE GreenLake
- AI and HPC architectures
  • 1. High-performance workload platforms
  • 2. GPU-accelerated computing
  • 3. NVIDIA integration
- HPE Compute portfolio
  • 1. HPE Synergy
  • 2. HPE Cray systems
  • 3. HPE ProLiant servers
Topic 2: Demonstrate AI Solutions10%- AI solution demonstration
  • 1. Customer use cases
  • 2. Solution positioning
  • 3. Business value presentation
Topic 3: Design HPE Solutions for AI and HPC Workloads50%- Solution architecture and design
  • 1. Performance optimization
  • 2. Capacity planning
  • 3. Workload analysis
- Infrastructure integration
  • 1. Compute, storage, and networking integration
  • 2. Hybrid cloud architectures
  • 3. Scalability and resiliency design
Topic 4: Manage Implementation and Startup of HPE AI and HPC Solutions2%- Deployment and validation
  • 1. Configuration verification
  • 2. Implementation planning
  • 3. Operational readiness

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HP Advanced HPE Compute Architect Solutions Written Exam Sample Questions (Q84-Q89):

NEW QUESTION # 84
A government customer requires servers whose components are traceable and assembled under controlled conditions to reduce supply chain risk.
What should you recommend?

Answer: B

Explanation:
HPE Trusted Supply Chain servers are built in controlled facilities by vetted personnel, ship with security settings enabled and verified firmware, and provide component traceability. Factory Express provides custom integration but does not address supply chain provenance.


NEW QUESTION # 85
A customer asks about the options HPE Private Cloud AI includes out of the box for hyperparameter optimization (HPO) during fine-tuning.
What is one thing that you should explain?

Answer: C

Explanation:
HPE AI Essentials includes Kubeflow, and Kubeflow includes Katib for hyperparameter tuning experiments. The other options incorrectly map HPO to MLflow, Ray Tune exposure, or Spark/Livy.
Reference:
https://support.hpe.com/hpesc/public/docDisplay?docId=a00ecp55hen_us&docLocale=en_US&p age=reference%2Fkubernetes%2Fusing-kubernetes%2Fgeneral-functionality%2Ftutorials%2Fkubeflow%2FTutorial_Katib_Hyperparameter_Tuning.html
https://www.hpe.com/us/en/collaterals/collateral.a50014263enw.html


NEW QUESTION # 86
A customer wants you to design a solution for effectively near 100% liquid cooling for their HPE Cray XD systems.
Which solution should you propose?

Answer: B

Explanation:
HPE Cray XD DLC removes the main component heat, while HPE RDHX captures the remaining hot exhaust air and transfers it to facility water, enabling effectively near-100% heat removal to liquid cooling.
Reference: https://www.hpe.com/us/en/collaterals/collateral.a50004292enw.html


NEW QUESTION # 87
A customer has policies requiring the removal of sensitive information from AI fine-tuning data.
Which solution will help them address this need?

Answer: D

Explanation:
NVIDIA NeMo Curator is used for data curation and preprocessing, including PII/sensitive-data identification and removal before data is used for training or fine-tuning. Katib and Ray Tune are for HPO, while NeMo Evaluator evaluates model quality.
Reference: https://docs.nvidia.com/nemo-framework/user-
guide/25.04/datacuration/personalidentifiableinformationidentificationandremoval.html


NEW QUESTION # 88
A customer has an HPE Private Cloud AI solution, running HPE AI Essentials 1.11. The customer has identified a NIM for an LLM that they want to use. The customer has imported NeMo microservices on the AI Essentials platform. The customer plans to use NeMo Customizer's LoRA to fine-tune the LLM for their purposes.
What should you recommend that the customer do to deploy the fine-tuned model on HPE Private Cloud AI with HPE Machine Learning Inference Software?

Answer: D

Explanation:
LoRA fine-tuning creates adapter weights, not a new NIM image. NVIDIA NIM supports serving LoRA adapters by setting environment variables such as NIM_PEFT_SOURCE to the NeMo Entity Store/Data Store, allowing the NIM to pull the fine-tuned adapters for inference.


NEW QUESTION # 89
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