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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Solution Optimization and Security | 5% | - Security best practices - Efficiency and resilience |
| Topic 2: High Performance Computing (HPC) Design and Management | 10% | - Architecture and scaling - Management and monitoring |
| Topic 3: NVIDIA AI Enterprise and Model Serving | 10% | - Model serving architecture and optimization - Software deployment and management |
| Topic 4: Integration with Third-Party Platforms | 15% | - Red Hat OpenShift and Kubernetes integration - VMware integration |
| Topic 5: HPE AI Essentials: Data Analytics and Data Science | 15% | - Analytics and machine learning tools - Data pipelines and processing |
| Topic 6: Troubleshooting HPE Private Cloud AI | 10% | - Issue diagnosis and resolution - Performance tuning and optimization |
| Topic 7: AI Workshops and HPE AI Essentials Platform | 15% | - Customer engagement and assessment - Platform capabilities and deployment |
| Topic 8: HPE ProLiant Gen12 for AI | 20% | - AI-optimized compute design - Server architecture and components - GPU integration and configuration |
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NEW QUESTION # 22
How do workloads running within HPE AI Essentials authenticate each other?
Answer: B
NEW QUESTION # 23
Which correctly describes an aspect of the AI software stack for HPE Cray XD systems?
Answer: A
Explanation:
D is correct. HPE Cray XD systems use a conventional layered management model: platform firmware is managed through standards-based interfaces such as Redfish, while the host operating environment is Linux- based. This separation is appropriate for HPC because firmware lifecycle, hardware telemetry, node provisioning, and operating-system management are distinct administrative functions even when they are coordinated by cluster-management tooling.
Option A overstates the role of HPE GreenLake. HPE Cray XD environments are not fundamentally managed as a multi-system HPC control plane through GreenLake. Option B misnames and mischaracterizes HPE Performance Cluster Manager (HPCM); HPCM is cluster provisioning and management software, not the AI development environment itself. Option C is false because HPE does not provide a single combined firmware- and-OS image that replaces the normal platform firmware and Linux software stack.
An integrator should understand both layers: Redfish/BMC interfaces expose node hardware management and telemetry, while Linux and HPC software provide the runtime environment for schedulers, MPI, accelerators, storage clients, and applications.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 9 "HPE Cray management" and
"HPE Cray firmware and software"; HPE Cray XD management documentation.
NEW QUESTION # 24
In an HPE Synergy environment, what is the role of the Synergy Composer?
Answer: A
Explanation:
Synergy Composer is the embedded management appliance running HPE OneView that provides the software-defined control plane for composing and managing Synergy resources into logical servers, storage volumes, and fabric connectivity. It is not a physical cooling component, a licensing key, or a switch module (that role belongs to Virtual Connect/Synergy interconnect modules).
NEW QUESTION # 25
You need to connect several existing HPE Cray XD servers to a new HPE Slingshot network featuring HPE Slingshot ToR switches. The servers have existing 200 Gbps Ethernet NICs. What is required?
Answer: D
Explanation:
HPE Slingshot can work with select qualified 100/200 GbE Ethernet adapters, not only HPE Slingshot NICs. Therefore, the existing 200 GbE NICs do not automatically need replacement, but they must be verified as qualified for the Slingshot ToR switch.
NEW QUESTION # 26
What is one role played by the retriever reranking NIM in the NVIDIA NeMo Retriever pipeline?
Answer: A
Explanation:
C is correct. In a retrieval-augmented generation pipeline, initial retrieval can return a set of candidate passages or vectors that are approximately relevant to the user's query. The retriever reranking NIM evaluates those candidates more precisely and reorders them so that the items with the strongest semantic relevance are placed first. The highest-quality context can then be supplied to the generative model, improving answer relevance and reducing the amount of weak context passed into the prompt.
Option A describes ingestion and preprocessing, which occurs before the content is indexed in a RAG datastore. Option B describes retrieval or database-query activity, not reranking itself. Option D is the role of the LLM or generation NIM that produces the final natural-language response. Reranking sits between retrieval and generation.
The distinction is important because RAG quality is not determined only by the embedding search. A reranker can use a stronger relevance model on a limited candidate set, trading a small amount of additional computation for substantially better context selection.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 5, NVIDIA NeMo deep dive; NVIDIA NeMo Retriever documentation, retrieval and reranking services.
NEW QUESTION # 27
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