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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: High Performance Computing (HPC) Design and Management | 10% | - Architecture and scaling - Management and monitoring |
| Topic 2: Solution Optimization and Security | 5% | - Efficiency and resilience - Security best practices |
| Topic 3: Troubleshooting HPE Private Cloud AI | 10% | - Performance tuning and optimization - Issue diagnosis and resolution |
| Topic 4: HPE ProLiant Gen12 for AI | 20% | - Server architecture and components - AI-optimized compute design - GPU integration and configuration |
| Topic 5: AI Workshops and HPE AI Essentials Platform | 15% | - Platform capabilities and deployment - Customer engagement and assessment |
| Topic 6: HPE AI Essentials: Data Analytics and Data Science | 15% | - Analytics and machine learning tools - Data pipelines and processing |
| Topic 7: Integration with Third-Party Platforms | 15% | - Red Hat OpenShift and Kubernetes integration - VMware integration |
| Topic 8: NVIDIA AI Enterprise and Model Serving | 10% | - Software deployment and management - Model serving architecture and optimization |
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NEW QUESTION # 35
A customer uses HPE Synergy servers as VMware ESXi hosts, assigned to clusters. What is one action that is enabled by your importing the ESXi clusters in HPE OneView for vCenter?
Answer: D
Explanation:
This matches VMware Proactive HA with HPE OneView for VMware vCenter. HPE describes this feature as preventing VM downtime by automatically migrating affected workloads before possible hardware degradation disrupts service. HPE's own OV4VC training outline also lists enabling VMware Proactive HA as a management function and separately lists cluster import/grow/shrink as provisioning functions.
NEW QUESTION # 36
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 # 37
A customer requires accessing the HPE AI Essentials KubeRay cluster interactively (with ray.init()) from an external client.
What should you explain?
Answer: A
Explanation:
C is correct. The built-in KubeRay head service is normally exposed only inside the Kubernetes cluster, so an external client cannot simply call ray.init() against it by default. One supported way to provide interactive external access is to change kuberay-head-svc from ClusterIP to NodePort. After the service is exposed, the administrator identifies the assigned node port for the Ray client endpoint and connects to the cluster using a ray:// address.
Option A incorrectly states that external access is enabled by default. Port 10001 is the Ray client service port inside the service definition, but an external client still requires a reachable service exposure mechanism.
Option D confuses the Ray dashboard port, commonly 8265, with the Ray client endpoint. Option B is too restrictive; a separate Ray cluster is not required merely to permit external interactive access.
Because NodePort expands network exposure, the change should be combined with appropriate firewalling, authentication, and access controls rather than treated as a convenience-only setting.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 4 "Using Ray"; HPE AI Essentials Software documentation, "Connecting to Ray Cluster."
NEW QUESTION # 38
A customer's compliance team requires that all server firmware and drivers be baselined and rolled back if drift is detected. Which HPE OneView feature addresses this?
Answer: D
Explanation:
HPE OneView lets administrators assign a Service Pack for ProLiant (SPP) baseline to server profiles; OneView monitors for drift from that baseline and can remediate it. Server Profile Templates standardize configuration but the compliance/drift function specifically is the firmware baseline feature. Scopes/Permissions control access, and the Activity Feed is an audit log.
NEW QUESTION # 39
A customer needs to deploy a NIM LLM with HPE Machine Learning Inference Software running on HPE Private Cloud AI (AI Essentials 1.6.1). What is one option for the image for serving the model?
Answer: D
Explanation:
For AI Essentials 1.6.1, NIM LLM serving images are handled as prepackaged/curated images in the HPE Private Cloud AI registry. Direct dynamic NGC delivery is associated with later AI Essentials releases, not 1.6.1.
NEW QUESTION # 40
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