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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Demonstrate AI Solutions | 10% | - AI solution demonstration
|
| Topic 2: Design HPE Solutions for AI and HPC Workloads | 50% | - Infrastructure integration
|
| Topic 3: Manage Implementation and Startup of HPE AI and HPC Solutions | 2% | - Deployment and validation
|
| Topic 4: Explain HPE Compute AI and HPC Solution Components and Architecture | 38% | - Management and automation
|
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NEW QUESTION # 53
A customer plans to deploy an AI-powered chatbot with HPE Private Cloud AI but requires help assembling all of the components.
What should you recommend that the customer use?
Answer: D
Explanation:
NVIDIA AI Blueprints provide predefined reference architectures and reusable implementation components that simplify the development and deployment of complete generative AI applications.
An enterprise chatbot typically requires several integrated technologies, including a large language model, inference runtime, document processing, embedding generation, vector database, retrieval framework, and application interface.
Assembling these components independently requires significant integration, configuration, and testing.
NVIDIA AI Blueprints reduce this complexity by providing tested architectural patterns, deployment instructions, reference code, and integration workflows for specific AI application scenarios.
HPE Private Cloud AI supports NVIDIA AI Enterprise technologies, including NVIDIA NIM microservices and validated NVIDIA Blueprints. These capabilities enable organizations to deploy supported generative AI workloads while maintaining control over enterprise data and infrastructure.
For a chatbot, a retrieval-augmented generation architecture can combine organizational documents with an LLM to produce contextually relevant responses.
Option B provides model-serving functionality but does not assemble the complete application architecture.
Option C orchestrates workflows and scheduled tasks rather than supplying an end-to-end chatbot implementation. Option D supports machine learning pipeline execution but is not the most direct solution for assembling a conversational AI application.
An NVIDIA AI Blueprint is therefore the appropriate recommendation.
Official HPE Reference: HPE Private Cloud AI - NVIDIA AI Blueprints; HPE Private Cloud AI Developer Portal - Validated NVIDIA Blueprints; HPE Private Cloud AI Solution Accelerators.
NEW QUESTION # 54
A customer's rack has a hard power budget, and the architect must guarantee that a group of servers never exceeds a defined total wattage.
What should you recommend?
Answer: C
Explanation:
Dynamic power capping enforces a defined power ceiling for individual servers or groups, throttling processor performance only when the cap would be exceeded. Static low power mode reduces performance unconditionally and does not guarantee a group-level ceiling.
NEW QUESTION # 55
An HPE Private Cloud AI customer wants to archive Prometheus metrics collected on the solution.
What should you recommend?
Answer: B
Explanation:
HPE AI Essentials includes OpenTelemetry for forwarding metrics and logs to external monitoring systems, which is the appropriate approach for archiving Prometheus metrics outside the platform. Enlarging Prometheus storage or changing PV retention is not the recommended archival method.
Reference:
https://support.hpe.com/hpesc/public/docDisplay?docId=a00aie111dhen_us&docLocale=en_US& page=Observability%2Fotel.html
NEW QUESTION # 56
A customer plans to train large language models on premises and requires eight interconnected GPUs per node with high GPU-to-GPU bandwidth.
Which platform should you recommend?
Answer: A
Explanation:
HPE ProLiant Compute XD systems are purpose-built for large-scale AI training, supporting dense eight-way GPU configurations with high-speed GPU interconnect and high-bandwidth fabric adapters. Standard 1U dual-GPU servers cannot deliver the required GPU density or interconnect bandwidth.
NEW QUESTION # 57
What is one advantage of NVIDIA NIM in the HPE Private Cloud AI architecture?
Answer: B
Explanation:
NVIDIA Inference Microservices (NIM) provide optimized, containerized inference services that simplify the deployment of artificial intelligence models within HPE Private Cloud AI.
NIM packages the components required to serve AI models, including inference engines, runtime dependencies, and standardized APIs, into deployable microservices.
These containers incorporate optimized inference runtimes such as NVIDIA TensorRT and TensorRT-LLM, depending on the model architecture and supported hardware configuration.
This optimization helps improve inference throughput, reduce response latency, and utilize GPU resources efficiently.
Within HPE Private Cloud AI, NIM integrates with NVIDIA AI Enterprise and the HPE AI Essentials platform. Administrators can deploy supported NIM models through HPE Machine Learning Inferencing Software and expose them through managed inference endpoints.
This architecture reduces the operational complexity associated with manually assembling inference containers, configuring model servers, and integrating GPU-specific optimization libraries.
Option B incorrectly identifies NIM as the mechanism responsible for integrating Kubeflow with HPE AI Essentials. Option C concerns GPU resource scheduling and reclamation, which are separate platform responsibilities. Option D describes NVIDIA GPU interconnect technology rather than software-based inference microservices.
The principal benefit of NVIDIA NIM is the availability of optimized, containerized inference services that accelerate production AI deployment.
Official HPE Reference: HPE Private Cloud AI Administration Guide - Benefits of HPE Private Cloud AI; HPE AI Essentials - NVIDIA AI Enterprise; HPE Machine Learning Inferencing Software.
support.hpe.com
NEW QUESTION # 58
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