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| Section | Objectives |
|---|---|
| HPE Compute Architecture and Solutions | - HPE ProLiant server families overview - Compute solution positioning and use cases |
| Solution Design and Implementation | - Workload sizing and architecture planning - High availability and redundancy design |
| 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 | - SAN and direct-attached storage integration - Networking configuration and connectivity options |
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NEW QUESTION # 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?
Answer: B
Explanation:
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.
NEW QUESTION # 19
An HPE Private Cloud AI customer wants to implement access controls on Airflow DAGs such that only HPE Private Cloud AI Administrators can trigger the DAGs.
What should you recommend?
Answer: D
Explanation:
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 4 "Airflow"; June 15, 2026 HPE7- S02 release notes noting the updated Airflow RBAC method; HPE AI Essentials 1.11 "Defining RBACs on DAGs."
NEW QUESTION # 20
What is one action that an HPE Private Cloud AI Administrator can perform from the Alerts view in HPE AI Essentials?
Answer: B
Explanation:
In the HPE AI Essentials Alerts view, an administrator can select View Logs to inspect pod logs related to the alert. This helps troubleshoot the workload or platform component that generated the alert.
NEW QUESTION # 21
Which HPE Smart Array controller feature allows administrators to configure RAID and volumes before the operating system is installed, using a UEFI-based utility?
Answer: C
Explanation:
HPE Smart Storage Administrator, accessible from the UEFI System Utilities (F9/F10 during boot), allows pre-OS RAID and logical drive configuration. The legacy ACU CLI is an offline/scripting tool, Insight Diagnostics is for hardware testing, and Virtual Media is used to mount ISO images remotely, not to configure storage.
NEW QUESTION # 22
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 # 23
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