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| Section | Weight | Objectives |
|---|---|---|
| Troubleshooting HPE Private Cloud AI | 10% | - Performance tuning and optimization - Issue diagnosis and resolution |
| HPE ProLiant Gen12 for AI | 20% | - Server architecture and components - AI-optimized compute design - GPU integration and configuration |
| NVIDIA AI Enterprise and Model Serving | 10% | - Software deployment and management - Model serving architecture and optimization |
| High Performance Computing (HPC) Design and Management | 10% | - Management and monitoring - Architecture and scaling |
| Integration with Third-Party Platforms | 15% | - VMware integration - Red Hat OpenShift and Kubernetes integration |
| HPE AI Essentials: Data Analytics and Data Science | 15% | - Data pipelines and processing - Analytics and machine learning tools |
| 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 |
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NEW QUESTION # 59
Which statement about HPE Alletra storage integration with HPE compute solutions is most accurate?
Answer: C
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 # 60
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: A
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 # 61
A customer needs to deploy a NIM LLM with HPE Machine Learning Inference Software running on HPE Private Cloud AI (AI Essentials 1.6.1).
What is one option for the image for serving the model?
Answer: C
Explanation:
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 5 "Serving NIMs with HPE Machine Learning Inference Software"; HPE AI Essentials/MLIS registry documentation and release-history notes.
NEW QUESTION # 62
A customer has fine-tuned a model and wants to find out whether the model is producing any toxic content.
Which NVIDIA microservice best meets this need?
Answer: C
Explanation:
D is correct. NVIDIA NeMo Evaluator is designed to measure model and application quality against defined evaluation criteria, including safety-related dimensions. Current NeMo evaluation tooling includes safety evaluations for harmful-content generation, toxicity, bias, hate speech, and related robustness concerns. A customer who has fine-tuned a model and wants to determine whether it produces toxic output needs an evaluation service rather than a data-preparation or runtime-policy component.
NeMo Customizer is used to adapt or fine-tune models. NeMo Curator prepares training data through curation, filtering, and deduplication. NeMo Guardrails is used to apply runtime controls to conversational AI applications, such as restricting unsafe topics or validating inputs and outputs. Guardrails can help prevent unacceptable responses in production, but the question asks which microservice is used to evaluate whether the fine-tuned model is producing toxic content.
A sound AI lifecycle therefore separates customization, evaluation, and enforcement: customize the model, evaluate its behavior, and then apply appropriate guardrails and deployment controls.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 5 "NVIDIA NeMo deep dive"; NVIDIA NeMo Evaluator documentation, safety and security evaluation capabilities.
NEW QUESTION # 63
HPE Private Cloud AI Users want to run interactive Spark applications from notebook servers running on HPE AI Essentials. What should you do?
Answer: D
Explanation:
Interactive Spark sessions from notebooks use Livy; HPE AI Essentials Spark magic commands connect to the Livy server to start and manage Spark sessions. Spark Operator is for Spark Applications, not interactive notebook sessions.
NEW QUESTION # 64
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