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| Section | Weight | Objectives |
|---|---|---|
| High Performance Computing (HPC) Design and Management | 10% | - Architecture and scaling - Management and monitoring |
| Solution Optimization and Security | 5% | - Security best practices - Efficiency and resilience |
| AI Workshops and HPE AI Essentials Platform | 15% | - Platform capabilities and deployment - Customer engagement and assessment |
| HPE AI Essentials: Data Analytics and Data Science | 15% | - Data pipelines and processing - Analytics and machine learning tools |
| Integration with Third-Party Platforms | 15% | - VMware integration - Red Hat OpenShift and Kubernetes integration |
| NVIDIA AI Enterprise and Model Serving | 10% | - Software deployment and management - Model serving architecture and optimization |
| HPE ProLiant Gen12 for AI | 20% | - GPU integration and configuration - Server architecture and components - AI-optimized compute design |
| Troubleshooting HPE Private Cloud AI | 10% | - Performance tuning and optimization - Issue diagnosis and resolution |
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NEW QUESTION # 27
What is one benefit of HPE Slingshot for HPC workloads?
Answer: D
Explanation:
HPE Slingshot improves bandwidth utilization with fine-grained adaptive routing; routing decisions can be packet-by-packet for unordered traffic.
NEW QUESTION # 28
What is one way that HPE minimizes costs for the HPE Private Cloud AI Developer System?
Answer: B
Explanation:
A is correct. The HPE Private Cloud AI Developer System is positioned as a smaller, cost-conscious configuration for development and proof-of-concept work. Integrating file and object storage into the AI- optimized node reduces the need to purchase, cable, power, and administer a separate external storage tier for the entry configuration. That consolidation lowers infrastructure cost while preserving the data-access patterns required by notebooks, model assets, and AI development workflows.
The remaining choices contradict the design intent of HPE Private Cloud AI. An AI-optimized node is not made cost-effective by eliminating GPU acceleration and using CPU-only compute; doing so would undermine the target AI workload. HPE also does not reduce value by stripping out the integrated software toolchain. The platform's advantage is precisely the curated, validated software environment. NVIDIA AI Enterprise support is likewise a core differentiator of HPE Private Cloud AI with NVIDIA rather than an optional capability removed to save money.
For exam purposes, distinguish architectural consolidation from functional reduction. HPE minimizes entry- system cost by integrating necessary infrastructure components, not by removing the accelerated-compute or enterprise-AI capabilities that define the solution.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 1 "HPE ProLiant Gen12 for AI" and HPE solutions for AI; HPE Private Cloud AI engineered-system and Developer System materials.
NEW QUESTION # 29
A customer wants to manage the full lifecycle of their HPE ProLiant Gen11 servers - including firmware updates, monitoring, and remote console access - from a single cloud-based console without deploying an on-premises management appliance. Which HPE solution should the integrator recommend?
Answer: D
NEW QUESTION # 30
Which statement about HPE Alletra storage integration with HPE compute solutions is most accurate?
Answer: B
Explanation:
HPE Alletra is HPE's unified data storage brand spanning multiple platforms for block, file, and hybrid workloads, and it integrates with HPE GreenLake and InfoSight for AI-driven, cloud- operated management alongside compute resources, giving integrators a consistent management experience. It is not limited to block-only, edge-only, or fully siloed from compute management.
NEW QUESTION # 31
An HPE Private Cloud AI customer has a model deployed with HPE Machine Learning Inference Software (MLIS). You have helped the customer further fine-tune that model. Now the customer would like to test the new model by directing 10% of traffic to it.
What should you do?
Answer: B
Explanation:
A is correct. HPE Machine Learning Inference Software supports canary rollout behavior for a deployment when a new model version is introduced. A canary allows the current production version and the candidate version to run concurrently while a defined percentage of inference traffic is directed to each. Setting the new version to 10% gives the customer a controlled production test while retaining 90% of traffic on the established version.
Option B invents a "Canal" routing service and unnecessarily creates a separate deployment. Option C confuses autoscaling with traffic splitting; autoscaling changes replica capacity, not the percentage of requests routed to different model versions. Option D also misstates the model: traffic distribution is part of the versioned canary rollout rather than an arbitrary routing rule between two independently configured model versions in the existing deployment.
The canary mechanism reduces change risk because model quality, latency, error rate, and resource consumption can be compared before full promotion. If the candidate is satisfactory, its traffic share can be increased; if not, the rollout can be stopped or rolled back.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 5 "Serving models with HPE Machine Learning Inference Software"; HPE AI Essentials MLIS documentation, "Initiate Canary Rollout."
NEW QUESTION # 32
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