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| Section | Objectives |
|---|---|
| Topic 1: Solution Design and Implementation | - Workload sizing and architecture planning - High availability and redundancy design |
| Topic 2: Storage and Networking Integration | - Networking configuration and connectivity options - SAN and direct-attached storage integration |
| Topic 3: HPE Compute Architecture and Solutions | - Compute solution positioning and use cases - HPE ProLiant server families overview |
| Topic 4: Server Deployment and Configuration | - Initial setup and provisioning - Firmware and system configuration tools (iLO, SPP) |
| Topic 5: Troubleshooting and Support | - Performance monitoring and diagnostics - Hardware and firmware troubleshooting |
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NEW QUESTION # 56
What is one benefit of HPE Slingshot for HPC workloads?
Answer: D
Explanation:
HPE Slingshot improves bandwidth utilization with fine-grained adaptive routing; routing decisions can be packet-by-packet for unordered traffic.
NEW QUESTION # 57
An organization running a mixed fleet of HPE ProLiant, Synergy, and Alletra storage systems wants predictive analytics and AI-driven recommendations to prevent infrastructure issues before they occur. Which HPE offering should be proposed?
Answer: C
Explanation:
HPE InfoSight provides AI-driven predictive analytics across compute and storage (extended from Nimble/Alletra into ProLiant and other infrastructure) to identify and often auto-resolve problems before they impact operations. OneView Global Dashboard aggregates OneView instances, Insight Remote Support automates support-case creation, and iLO Amplifier Pack is a firmware/health scanning tool for large ProLiant fleets.
NEW QUESTION # 58
A customer has HPE ProLiant DL Gen12 servers managed by HPE Compute Ops Management and acting as VMware vSphere ESXi hosts. How can you handle updating the servers' firmware and OS together?
Answer: D
NEW QUESTION # 59
What is one difference between a Class 1 and a Class 3 HPE Slingshot topology?
Answer: A
Explanation:
A is correct. HPE Slingshot topology classes trade edge-facing capacity against the inter-switch links needed to scale the fabric. A Class 1 topology uses a smaller topology structure and can devote more switch ports to L0 connections toward compute-node NICs. A Class 3 topology uses additional switch groups and therefore consumes more switch ports for L1/L2 fabric connectivity, leaving fewer host-facing ports per switch even though the overall fabric can scale to a much larger endpoint count.
Option B reverses the scaling relationship: Class 3 is the topology intended for larger fabrics, not Class 1.
Option C also reverses the port-ratio concept; the larger class requires a greater proportion of inter-switch connectivity. Option D is incorrect because adaptive-routing capabilities are properties of the Slingshot fabric and switch architecture, not a special routing technology unlocked only by choosing Class 1.
This is a topology-design question rather than a raw bandwidth question. The designer balances the number of endpoint ports per switch, the number of switch groups, and the inter-switch links required to maintain the target bisection and path diversity.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 10; HPE Slingshot "Switch Topology Classes" documentation.
NEW QUESTION # 60
An HPE Private Cloud AI User wants to use KServe to run a NIM that is available in NGC.
What is one prerequisite?
Answer: D
Explanation:
D is correct. A NIM image hosted in NVIDIA NGC is protected by NVIDIA registry authentication. To allow KServe in a user namespace to pull that image, the user needs a valid NGC API key and a Kubernetes secret containing the corresponding registry credentials in the namespace where the InferenceService will run. The workload can then reference that secret for the image pull or other required NGC access.
Option A describes registering NGC with HPE Machine Learning Inference Software. That is relevant when MLIS is the serving mechanism, but the scenario explicitly uses KServe. Option B is not how NVIDIA AI Enterprise is made available to HPE AI Essentials; it is not imported as a user Helm chart. Option C is also invalid because KServe does not require special "decorators" in application code to launch a NIM.
Namespace placement matters because Kubernetes secrets are namespace-scoped. A secret created elsewhere is not automatically available to the user's KServe deployment.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 5 "Other options for serving models on HPE Private Cloud AI" and NVIDIA AI Enterprise; HPE AI Essentials KServe guidance; NVIDIA NGC authentication documentation.
NEW QUESTION # 61
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