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| Section | Weight | Objectives |
|---|---|---|
| HPE ProLiant Gen12 for AI | 20% | - GPU integration and configuration - AI-optimized compute design - Server architecture and components |
| Solution Optimization and Security | 5% | - Security best practices - Efficiency and resilience |
| Troubleshooting HPE Private Cloud AI | 10% | - Issue diagnosis and resolution - Performance tuning and optimization |
| HPE AI Essentials: Data Analytics and Data Science | 15% | - Data pipelines and processing - Analytics and machine learning tools |
| Integration with Third-Party Platforms | 15% | - VMware integration - Red Hat OpenShift and Kubernetes integration |
| NVIDIA AI Enterprise and Model Serving | 10% | - Software deployment and management - Model serving architecture and optimization |
| AI Workshops and HPE AI Essentials Platform | 15% | - Platform capabilities and deployment - Customer engagement and assessment |
| High Performance Computing (HPC) Design and Management | 10% | - Architecture and scaling - Management and monitoring |
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NEW QUESTION # 34
You are helping a customer troubleshooting performance issues on an HPE ProLiant DL server acting as a VMware ESXi host.
You have used the esxtop command, and the output is shown below. (In this output, the details for many VMs have been omitted; however, other VMs have similar stats to the ones that are shown.)
Based on this output, what should you explain?
Answer: B
Explanation:
The ESXi memory state is hard, with very little free memory, active ballooning, and swapping.
This indicates host memory pressure, so the fix is to add memory or reduce VM load on the host.
NEW QUESTION # 35
You are planning to demonstrate how you can use KServe to run NIMs on HPE Private Cloud AI.
You have downloaded the CodeLlama-70B-Instruct model to a volume in your namespace named demo-model. The objects that you are planning to deploy are shown below:
What change should you make to the objects?
Answer: D
Explanation:
The model is already downloaded to a PVC, so the InferenceService must reference that PVC with storageUri. KServe then mounts the model storage for the predictor; you do not change the model format or ServingRuntime. More precisely, this belongs under the predictor's model spec as storageUri.
NEW QUESTION # 36
A customer requires that decommissioned HPE server drives be cryptographically erased to meet data-sanitization compliance before the drives leave the data center. Which HPE feature satisfies this requirement?
Answer: A
Explanation:
HPE Secure Erase (available via iLO/RESTful interfaces and Smart Storage Administrator) performs NIST-compliant cryptographic or media sanitization erasure of drives. A RAID rebuild does not sanitize data, an iLO factory reset only resets the management processor, and Secure Boot validates boot software rather than erasing data.
NEW QUESTION # 37
Which HPE Gen11 server component is specifically responsible for enforcing that only cryptographically signed firmware images can be flashed to the system, rejecting unsigned or tampered images?
Answer: B
Explanation:
The firmware verification mechanism within HPE's Silicon Root of Trust design validates the digital signature of firmware before it is allowed to run or be flashed, rejecting compromised images. The Security Override Switch is a physical jumper for recovery scenarios, Server Configuration Lock prevents unauthorized hardware/BIOS changes, and Chassis Intrusion Detection reports physical case-open events.
NEW QUESTION # 38
How do workloads running within HPE AI Essentials authenticate each other?
Answer: B
Explanation:
B is correct. HPE AI Essentials uses service identities based on SPIFFE concepts. Workloads receive SPIFFE Verifiable Identity Documents (SVIDs) from the platform's SPIRE infrastructure, and the Istio service mesh uses those identities to establish authenticated service-to-service communication. This gives workloads cryptographically verifiable identities that are independent of network location and supports mutual authentication between services inside the platform.
Option A incorrectly attributes workload identity to the Istio ingress gateway. The ingress gateway handles north-south access, but it is not the certificate authority that independently issues identities to every internal workload. Option C describes Kubernetes secrets generated by Kyverno, which is not the HPE AI Essentials workload-authentication architecture. Option D is too generic; static self-signed certificates created at installation would not provide the dynamic, workload-specific identity and rotation model that SPIRE supplies.
For integrators, the important design point is that the platform treats workload identity as a first-class security control. Istio provides the service-mesh enforcement and mTLS capabilities, while SPIRE supplies trusted identities used by workloads and proxies.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 3 "Understanding and managing communications"; HPE Private Cloud AI/HPE AI Essentials core-concepts documentation for Istio, SPIRE, and service identity.
NEW QUESTION # 39
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