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| Section | Weight | Objectives |
|---|
| Topic 1: Integration with Data Center Infrastructure | 20% | - Security, compliance and data protection - HPE OneView management and automation - Networking and storage integration
|
| Topic 2: HPE Compute Architecture and Technologies | 25% | - Virtualization and container integration - Compute component selection: processors, memory, storage, accelerators - HPE ProLiant, Apollo and Superdome Flex platforms
|
| Topic 3: Implementation and Lifecycle Management | 15% | - Upgrade, maintenance and monitoring - Deployment planning and validation
|
| Topic 4: Solution Design for Enterprise Workloads | 30% | - Scalability, availability and performance optimization - Requirements analysis and environment assessment - Design for AI, HPC and mission-critical workloads
|
| Topic 5: Customer Success and Solution Optimization | 10% | - Troubleshooting and continuous improvement - Solution demonstration and value justification
|
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HP Advanced HPE Compute Architect Solutions Written Exam Sample Questions (Q71-Q76):
NEW QUESTION # 71
How does NVIDIA Spectrum-X address congestion?
- A. It allocates a dedicated buffer to each port to prevent traffic loss during congestion.
- B. It exclusively uses open standard technologies such as PFC and IP ECN to ensure compatibility for heterogeneous networks.
- C. It prevents congestion proactively by using ECMP to distribute traffic across multiple links.
- D. It uses flow metering based on information gathered from control packets to target congestion at the source.
Answer: D
Explanation:
NVIDIA Spectrum-X uses telemetry/control information with flow metering so senders can adjust traffic at the source before congestion causes packet loss. ECMP, PFC, and ECN alone do not describe Spectrum-X's differentiated congestion-control mechanism.
Reference: https://developer.nvidia.com/blog/optimize-large-scale-ai-workloads-with-nvidia- spectrum-x/
NEW QUESTION # 72
You want to demonstrate HPE Private Cloud AI running a complex AI application, such as a video search app, to a customer.
What is one good resource for putting together this demonstration more quickly?
- A. HPE GitHub repo for Airflow demos
- B. The tutorials folder in the HPE AI Essentials shared directory
- C. NVIDIA CUDA catalog
- D. NVIDIA AI blueprints
Answer: D
NEW QUESTION # 73
An HPE Private Cloud AI customer is interested in fine-tuning with Ray and asks how to submit jobs to the Ray cluster running on HPE AI Essentials.
What should you explain?
- A. They can run a notebook server on HPE AI Essentials and use its Ray kernel.
- B. They can install the Ray Tune framework from the administration settings.
- C. They can use the HPE AI Essentials API Ray endpoint.
- D. They can submit jobs directly from the Ray dashboard, which opens from the HPE AI Essentials Tools & Frameworks page.
Answer: C
Explanation:
Do not submit jobs directly from the Ray dashboard. HPE documentation says to connect to the Ray cluster, and HPE recommends JobSubmissionClient for Ray job submission; the Ray dashboard is mainly for monitoring/debugging.
Reference:
https://support.hpe.com/hpesc/public/docDisplay?docId=a00aie17hen_us&docLocale=en_US&pa ge=Ray%2Fray.html
https://support.hpe.com/hpesc/public/docDisplay?docId=a00aie19hen_us&docLocale=en_US&pa ge=Ray%2Fconnecting-to-ray-cluster.html
NEW QUESTION # 74
An organization is planning to run AI inferencing for customer service. The organization has strict environmental efficiency standards. They have facility water access at the rack.
Which server configuration should you recommend?
- A. HPE ProLiant DL380a Gen12 with 70% DLC
- B. HPE ProLiant DL380 Gen12 with high-performance heat sinks
- C. HPE ProLiant DL360 Gen12 with closed-loop liquid cooling
- D. HPE ProLiant DL580 Gen 12 with liquid-to-air rear-door cooling
Answer: A
Explanation:
The DL380a Gen12 is accelerator-optimized for AI inferencing, and 70% direct liquid cooling is the best fit when the customer has facility water at the rack and strict efficiency requirements. Air cooling, rear-door cooling, or closed-loop cooling would be less efficient for this scenario.
Reference: https://www.hpe.com/emea_europe/en/compute/hpe-proliant-compute/dl380a- gen12.html
NEW QUESTION # 75
A customer has policies requiring the removal of sensitive information from AI fine-tuning data.
Which solution will help them address this need?
- A. Kubeflow Katib
- B. NVIDIA NeMo Curator
- C. NVIDIA NeMo Evaluator
- D. Ray Tune
Answer: B
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 # 76
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