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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: NVIDIA AI Enterprise and Model Serving | 10% | - Model serving architecture and optimization - Software deployment and management |
| Topic 2: Troubleshooting HPE Private Cloud AI | 10% | - Performance tuning and optimization - Issue diagnosis and resolution |
| Topic 3: HPE ProLiant Gen12 for AI | 20% | - GPU integration and configuration - AI-optimized compute design - Server architecture and components |
| Topic 4: AI Workshops and HPE AI Essentials Platform | 15% | - Platform capabilities and deployment - Customer engagement and assessment |
| Topic 5: High Performance Computing (HPC) Design and Management | 10% | - Architecture and scaling - Management and monitoring |
| 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: Solution Optimization and Security | 5% | - Security best practices - Efficiency and resilience |
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NEW QUESTION # 28
An HPE Private Cloud AI customer wants to import a custom framework. The customer wants to ensure that only authorized users access that framework's endpoint URL. You have created an authorization policy for this purpose.
In which namespace should you add the policy?
Answer: D
Explanation:
A is correct. For an imported framework exposed through HPE AI Essentials, the authorization policy that controls access to the framework endpoint is placed in the istio-system namespace. The reason is architectural: access to the application is enforced at the Istio service-mesh and ingress layer, so the AuthorizationPolicy must be associated with the namespace where the relevant Istio gateway and policy enforcement context operate. The policy then limits which authenticated identities can reach the imported framework.
A user namespace is where many user-scoped workloads and resources run, but it is not the correct location for the ingress authorization policy in this workflow. The ezapp-system namespace contains platform application components and is not the documented target for this policy. Likewise, placing the policy only in the framework's application namespace would not implement the HPE AI Essentials ingress authorization pattern described for imported frameworks.
Current Rev. 26.21 training also introduces AutoHelm and updated framework-import behavior, but it continues to treat access control as an Istio authorization-policy function rather than a GreenLake user-setting shortcut.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 3 "Understanding and managing communications" and governance policies; HPE AI Essentials Software documentation, SSO/authorization for imported frameworks.
NEW QUESTION # 29
An organization has an HPE Cray Storage Systems C500 solution. IT is interested in scripting management of these systems. Which API can they use?
Answer: C
Explanation:
HPE Cray Storage Systems support the Redfish/Swordfish REST API for scripted storage management. HPE GreenLake and HPE OneView are broader management platforms, while Slurm is a workload scheduler, not a storage management API.
NEW QUESTION # 30
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 # 31
An HPE Private Cloud AI User wants to use KServe to run a NIM that is available in NGC.
What is one prerequisite?
Answer: B
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 # 32
What is one way that NVIDIA Spectrum-X meets the needs for AI networks?
Answer: B
Explanation:
D is correct. NVIDIA Spectrum-X is engineered for Ethernet-based AI fabrics where synchronized GPU traffic can create short, extremely large bursts. Its congestion-management architecture uses high-frequency telemetry and flow metering to identify the sources contributing to congestion and react with much finer control than conventional static Ethernet behavior. This is especially valuable for RDMA/RoCE AI traffic, where packet loss, tail latency, and uneven use of parallel paths can materially reduce GPU utilization.
Option A reverses the relationship between ECN and PFC. Spectrum-X does not simply replace ECN with Priority Flow Control; modern AI Ethernet designs combine congestion signaling and control mechanisms to avoid relying on broad pause behavior. Option B is also inaccurate because static ECMP hashing is not the defining fine-grained balancing mechanism; Spectrum-X uses adaptive routing and telemetry-aware decisions. Option C describes dedicated per-port buffering as the basis for losslessness, which is not the principal Spectrum-X innovation.
The exam objective is to understand why an AI network differs from ordinary data-center Ethernet: the network must react rapidly to elephant flows and collective-communication bursts while keeping accelerated nodes productive.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, AI/HPC networking objectives; NVIDIA Spectrum-X Ethernet networking documentation, congestion control and telemetry.
NEW QUESTION # 33
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