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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Workshops and HPE AI Essentials Platform | 15% | - Platform capabilities and deployment - Customer engagement and assessment |
| Topic 2: Troubleshooting HPE Private Cloud AI | 10% | - Issue diagnosis and resolution - Performance tuning and optimization |
| Topic 3: NVIDIA AI Enterprise and Model Serving | 10% | - Software deployment and management - Model serving architecture and optimization |
| Topic 4: HPE ProLiant Gen12 for AI | 20% | - GPU integration and configuration - AI-optimized compute design - Server architecture and components |
| Topic 5: Solution Optimization and Security | 5% | - Security best practices - Efficiency and resilience |
| Topic 6: High Performance Computing (HPC) Design and Management | 10% | - Management and monitoring - Architecture and scaling |
| Topic 7: Integration with Third-Party Platforms | 15% | - Red Hat OpenShift and Kubernetes integration - VMware integration |
| Topic 8: HPE AI Essentials: Data Analytics and Data Science | 15% | - Data pipelines and processing - Analytics and machine learning tools |
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NEW QUESTION # 48
What is one difference between a Class 1 and a Class 3 HPE Slingshot topology?
Answer: B
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 # 49
Which HPE tool would an integrator use to perform a pre-deployment health and compliance scan across a large fleet of ProLiant servers, checking firmware levels against best-practice baselines?
Answer: D
Explanation:
HPE iLO Amplifier Pack discovers, inventories, and scans large numbers of ProLiant servers for firmware/driver compliance and security vulnerabilities at scale. SUM performs the actual firmware update process on individual or grouped servers, Insight Diagnostics runs local hardware tests, and AHS Viewer analyzes historical telemetry logs rather than performing fleet- wide compliance scans.
NEW QUESTION # 50
Which benefit does HPE Rear Door Heat Exchanger (RDHX) provide?
Answer: C
Explanation:
HPE RDHX attaches to the rear of the rack and uses a liquid-to-air heat exchanger to remove server exhaust heat efficiently, reducing the cooling burden and energy costs.
NEW QUESTION # 51
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 # 52
You are proposing HPE Private Cloud AI to a financial institution and want to demo the solution running a complex AI-enabled security application. What can help you to accelerate the development of the demo?
Answer: B
Explanation:
NVIDIA Blueprints provide sample/reference AI applications that accelerate development of complex demos and enterprise AI workflows; they are available through NVIDIA AI Enterprise/NGC resources.
NEW QUESTION # 53
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