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| Section | Weight | Objectives |
|---|---|---|
| Troubleshooting HPE Private Cloud AI | 10% | - Performance tuning and optimization - Issue diagnosis and resolution |
| Integration with Third-Party Platforms | 15% | - VMware integration - Red Hat OpenShift and Kubernetes integration |
| AI Workshops and HPE AI Essentials Platform | 15% | - Platform capabilities and deployment - Customer engagement and assessment |
| Solution Optimization and Security | 5% | - Security best practices - Efficiency and resilience |
| HPE AI Essentials: Data Analytics and Data Science | 15% | - Data pipelines and processing - Analytics and machine learning tools |
| HPE ProLiant Gen12 for AI | 20% | - Server architecture and components - AI-optimized compute design - GPU integration and configuration |
| High Performance Computing (HPC) Design and Management | 10% | - Architecture and scaling - Management and monitoring |
| NVIDIA AI Enterprise and Model Serving | 10% | - Software deployment and management - Model serving architecture and optimization |
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NEW QUESTION # 53
An HPE Private Cloud AI customer wants to eliminate the certificate errors that show when users access HPE AI Essentials. The customer has created a certificate signed by their CA to replace the current certificate. The customer has given you the certificate file, private key file, and CA certificate file. How should you install this certificate?
Answer: A
Explanation:
For a customer-provided certificate, key, and CA certificate, you replace the existing Istio ingress gateway certificate material by recreating the ingress-cert secret in the istio-system namespace.
Cert-manager/ClusterIssuer is used when you want cert-manager to issue or renew the certificate automatically.
NEW QUESTION # 54
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: D
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 # 55
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: D
Explanation:
B is correct. NVIDIA AI Blueprints are packaged reference workflows that combine models, NIM microservices, retrieval components, guardrails, and application logic for specific generative-AI use cases.
Selecting a suitable blueprint from the NVIDIA catalog can shorten a proof-of-concept cycle because the team starts from a validated architecture and deployable workflow instead of assembling every component from scratch. That is particularly useful when demonstrating a complex security-oriented AI application to a regulated customer.
Sample Kubeflow pipelines can accelerate individual machine-learning workflow steps, but they do not provide the same end-to-end application blueprint. NVIDIA Base Command Manager is an infrastructure and cluster-management product; it is not the development accelerator being asked for. HPE AI Essentials tutorials are useful instructional resources, but a blueprint is explicitly intended to speed implementation of a working use case.
Current Rev. 26.21 training has a dedicated module on using NVIDIA Blueprints and updates the recommended installation process for validated blueprints, reinforcing their role in demonstrations and PoCs.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 6 "Using an NVIDIA Blueprint to demo an AI Chatbot"; NVIDIA AI Blueprints catalog and deployment documentation.
NEW QUESTION # 56
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: B
Explanation:
B is correct. HPE Cray Supercomputing Storage Systems C500 exposes management through the Redfish
/Swordfish REST API model. Redfish provides a standardized, HTTP-based management interface, and Swordfish extends the model for storage resources. This makes it suitable for scripted inventory, configuration, health, and lifecycle operations without requiring an administrator to automate an interactive management console.
HPE GreenLake is not the device-level scripting API for the C500. HPE OneView is a composable- infrastructure and server-management platform and is not the native C500 storage-management API. Slurm is a workload manager and scheduler for HPC jobs; it allocates compute resources and schedules work but does not replace the storage system's management API.
The spelling is also significant: the standard is "Redfish," not "RedFish." In an HPC environment, administrators commonly combine specialized tools rather than expecting one product to perform every management function. HPE Performance Cluster Manager or Slurm can participate in cluster operations, while the storage platform remains manageable through its supported Redfish/Swordfish interface.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Modules 9 and 10, HPE Cray management and HPC storage; HPE Cray Supercomputing Storage Systems C500 Redfish/Swordfish REST API documentation.
NEW QUESTION # 57
What option does HPE Private Cloud AI provide for organizations who want to retain Prometheus metrics over the long term?
Answer: A
Explanation:
D is correct. HPE Private Cloud AI can export observability data through an OpenTelemetry (OTel) endpoint.
HPE AI Essentials retains Prometheus metrics for a defined local retention period, but organizations that require longer retention can register an OTel endpoint and send the telemetry to an external observability or storage platform with the retention policy they require. This decouples operational history from the local Prometheus retention window.
Option A does not describe the supported long-term metrics export mechanism. Option B would merely add another visualization component and would not solve durable metric retention; Grafana reads metrics but is not itself the long-term time-series store. Option C concerns alert output, which is a subset of operational events and cannot substitute for retained Prometheus time-series data.
This design is operationally important for capacity analysis, incident review, compliance evidence, and trend detection. The platform remains responsible for producing metrics, while the external OTel destination provides enterprise-scale storage and analytics.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Modules 3 and 7, monitoring and troubleshooting; HPE AI Essentials Software documentation, "Configuring Endpoints," which describes OTel export for long-term metric storage.
NEW QUESTION # 58
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