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| Section | Objectives |
|---|---|
| Troubleshooting and Support | - Hardware and firmware troubleshooting - Performance monitoring and diagnostics |
| Server Deployment and Configuration | - Firmware and system configuration tools (iLO, SPP) - Initial setup and provisioning |
| Storage and Networking Integration | - Networking configuration and connectivity options - SAN and direct-attached storage integration |
| HPE Compute Architecture and Solutions | - HPE ProLiant server families overview - Compute solution positioning and use cases |
| Solution Design and Implementation | - High availability and redundancy design - Workload sizing and architecture planning |
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NEW QUESTION # 63
When designing high availability for an HPE OneView managed environment, what is the purpose of deploying OneView in a multi-appliance (HA) configuration?
Answer: A
Explanation:
HPE OneView HA (clustered appliances) protects the management plane itself, ensuring continued management, monitoring, and orchestration capability if one appliance instance becomes unavailable. It does not affect VLAN counts, compute capacity, or replace Synergy Composer, which is the management appliance specific to Synergy frames.
NEW QUESTION # 64
Which HPE ProLiant Gen11 storage technology allows administrators to non-disruptively expand a RAID array's capacity by adding drives without taking the array offline?
Answer: C
Explanation:
HPE Smart Array controllers support online capacity expansion, allowing drives to be added to an existing array and the array/logical volume to grow without downtime. Offline RAID migration would require downtime by definition, Secure Erase is for data sanitization, and cold data tiering relates to moving infrequently accessed data rather than array expansion.
NEW QUESTION # 65
An integrator is proposing a two-node HPE SimpliVity hyperconverged solution for a remote office. Which of the following is a core benefit of HPE SimpliVity's architecture that should be highlighted?
Answer: C
Explanation:
HPE SimpliVity performs global inline deduplication and compression at the source (via the OmniStack accelerator) and includes native, VM-centric backup, restore, and DR capabilities without a separate backup appliance. It integrates directly with VMware vCenter for management (Hyper-V support is more limited/legacy), so C and D are incorrect, and A contradicts its built-in data protection design.
NEW QUESTION # 66
What option does HPE Private Cloud AI provide for organizations who want to retain Prometheus metrics over the long term?
Answer: D
Explanation:
HPE Private Cloud AI can export observability data to an external OpenTelemetry endpoint, allowing organizations to retain Prometheus metrics long term outside the default in-cluster retention period.
NEW QUESTION # 67
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: B
Explanation:
D is correct after correcting the YAML location to spec.predictor.model.storageUri. The ServingRuntime describes the runtime container capable of serving the model; the InferenceService describes the particular model instance to deploy. Because the CodeLlama model files already reside on a persistent volume claim named demo-model, the InferenceService must tell KServe where those model artifacts are stored. A PVC URI is therefore supplied through the model's storageUri field, for example pvc://demo-model.
Option A is structurally wrong because modelFormat.name identifies the model format understood by the ServingRuntime; it is not a storage location. Option B incorrectly assigns model storage to the ServingRuntime specification. Option C changes the NIM cache directory inside the container but does not tell KServe to mount and initialize the requested model from the PVC. KServe's v1beta1 schema places storageUri alongside modelFormat and runtime under the predictor's model block.
This distinction is fundamental to reusable KServe design: a runtime can serve multiple compatible models, while each InferenceService supplies the model-specific location and deployment parameters.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 5 "Other options for serving models on HPE Private Cloud AI"; HPE AI Essentials KServe documentation; KServe InferenceService v1beta1 schema.
NEW QUESTION # 68
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