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| Section | Weight | Objectives |
|---|---|---|
| AI Workshops and HPE AI Essentials Platform | 15% | - Customer engagement and assessment - Platform capabilities and deployment |
| High Performance Computing (HPC) Design and Management | 10% | - Architecture and scaling - Management and monitoring |
| Integration with Third-Party Platforms | 15% | - Red Hat OpenShift and Kubernetes integration - VMware integration |
| NVIDIA AI Enterprise and Model Serving | 10% | - Model serving architecture and optimization - Software deployment and management |
| HPE AI Essentials: Data Analytics and Data Science | 15% | - Data pipelines and processing - Analytics and machine learning tools |
| Troubleshooting HPE Private Cloud AI | 10% | - Issue diagnosis and resolution - Performance tuning and optimization |
| Solution Optimization and Security | 5% | - Security best practices - Efficiency and resilience |
| HPE ProLiant Gen12 for AI | 20% | - Server architecture and components - AI-optimized compute design - GPU integration and configuration |
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질문 # 15
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?
정답：B
설명：
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.
질문 # 16
A customer wants server management traffic (iLO) to be logically isolated from production data traffic for security reasons. What is the standard HPE best practice to achieve this?
정답：D
설명：
Best practice is to use iLO's dedicated network port connected to an isolated out-of-band management network/VLAN, separating management traffic from production data traffic for security and reduced attack surface. Mixing iLO traffic with production VLANs, disabling iLO, or defaulting to shared NIC mode regardless of requirements all reduce security or manageability.
질문 # 17
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?
정답：A
설명：
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.
질문 # 18
A customer has fine-tuned a model and wants to find out whether the model is producing any toxic content.
Which NVIDIA microservice best meets this need?
정답：D
설명：
D is correct. NVIDIA NeMo Evaluator is designed to measure model and application quality against defined evaluation criteria, including safety-related dimensions. Current NeMo evaluation tooling includes safety evaluations for harmful-content generation, toxicity, bias, hate speech, and related robustness concerns. A customer who has fine-tuned a model and wants to determine whether it produces toxic output needs an evaluation service rather than a data-preparation or runtime-policy component.
NeMo Customizer is used to adapt or fine-tune models. NeMo Curator prepares training data through curation, filtering, and deduplication. NeMo Guardrails is used to apply runtime controls to conversational AI applications, such as restricting unsafe topics or validating inputs and outputs. Guardrails can help prevent unacceptable responses in production, but the question asks which microservice is used to evaluate whether the fine-tuned model is producing toxic content.
A sound AI lifecycle therefore separates customization, evaluation, and enforcement: customize the model, evaluate its behavior, and then apply appropriate guardrails and deployment controls.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 5 "NVIDIA NeMo deep dive"; NVIDIA NeMo Evaluator documentation, safety and security evaluation capabilities.
질문 # 19
How do workloads running within HPE AI Essentials authenticate each other?
정답：D
설명：
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.
질문 # 20
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