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| Section | Objectives |
|---|---|
| Topic 1: HPE Compute Architecture Design | - Workload analysis and requirements mapping - Compute infrastructure planning and sizing |
| Topic 2: Operations and Troubleshooting | - Incident resolution and capacity planning - Monitoring and performance optimization |
| Topic 3: Security and Lifecycle Management | - Firmware, patching, and lifecycle management - Secure compute architecture principles |
| Topic 4: HPE Compute Solutions Portfolio | - HPE ProLiant server solutions - HPE GreenLake compute offerings - HPE Synergy and composable infrastructure |
| Topic 5: Solution Design and Integration | - Storage and networking integration considerations - High availability and scalability design |
1. You are demonstrating HPE Private Cloud AI. The customer asks how the system handles multiple users running concurrent workloads with different priorities.
Which feature would you highlight to explain how this is managed?
A) The built-in StorageClass and CSI driver
B) The solution's Kubeflow pipeline architecture
C) The HPE AI Essentials scheduler plug-in
D) The MLflow dashboard built into HPE AI Essentials
2. An HPE Synergy customer is expanding from one frame to seven frames and wants to minimize the number of interconnect modules and uplink ports required.
Which design should you recommend?
A) Pass-through modules in all frames connected to top-of-rack switches
B) Direct-attach SAS interconnects in each frame with shared uplinks
C) A pair of Virtual Connect modules in every frame, each with its own uplinks
D) Master Virtual Connect modules in two frames with satellite modules in the remaining frames
3. An automotive engineering team is running inference workloads for traffic simulation and computer vision models using KServe on HPE Private Cloud AI.
The team also uses GPU-enabled notebooks running on HPE Private Cloud AI to experiment with new models. They want to automatically adjust GPU usage for the different models based on current demand, as well as ensure that the notebooks never prevent the inferencing workloads from receiving the GPUs they need.
Which two features support this functionality? (Choose two.)
A) Autoscaling for KServe
B) Canary roll outs for KServe
C) Kyverno mutating admission policies
D) HPE AI Essentials scheduler priorities
4. A financial services customer runs a market data application that is extremely sensitive to jitter and response time, not throughput.
Which UEFI workload profile should you recommend?
A) General Peak Frequency Compute
B) Low Latency
C) Virtualization - Max Performance
D) Graphic Processing
5. A customer has policies requiring the removal of sensitive information from AI fine-tuning data.
Which solution will help them address this need?
A) Kubeflow Katib
B) NVIDIA NeMo Evaluator
C) NVIDIA NeMo Curator
D) Ray Tune
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: D | Question # 3 Answer: A,D | Question # 4 Answer: B | Question # 5 Answer: C |
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