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| Section | Weight | Objectives |
|---|---|---|
| NVIDIA AI Enterprise and Model Serving | 10% | - Software deployment and management - Model serving architecture and optimization |
| HPE AI Essentials: Data Analytics and Data Science | 15% | - Analytics and machine learning tools - Data pipelines and processing |
| AI Workshops and HPE AI Essentials Platform | 15% | - Customer engagement and assessment - Platform capabilities and deployment |
| High Performance Computing (HPC) Design and Management | 10% | - Management and monitoring - Architecture and scaling |
| Troubleshooting HPE Private Cloud AI | 10% | - Performance tuning and optimization - Issue diagnosis and resolution |
| HPE ProLiant Gen12 for AI | 20% | - GPU integration and configuration - Server architecture and components - AI-optimized compute design |
| Solution Optimization and Security | 5% | - Efficiency and resilience - Security best practices |
| Integration with Third-Party Platforms | 15% | - VMware integration - Red Hat OpenShift and Kubernetes integration |
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NEW QUESTION # 47
A customer's compliance team requires that all server firmware and drivers be baselined and rolled back if drift is detected. Which HPE OneView feature addresses this?
Answer: D
Explanation:
HPE OneView lets administrators assign a Service Pack for ProLiant (SPP) baseline to server profiles; OneView monitors for drift from that baseline and can remediate it. Server Profile Templates standardize configuration but the compliance/drift function specifically is the firmware baseline feature. Scopes/Permissions control access, and the Activity Feed is an audit log.
NEW QUESTION # 48
A customer wants server management traffic (iLO) to be logically isolated from production data traffic for security reasons. What is the standard HPE best practice to achieve this?
Answer: C
Explanation:
Best practice is to use iLO's dedicated network port connected to an isolated out-of-band management network/VLAN, separating management traffic from production data traffic for security and reduced attack surface. Mixing iLO traffic with production VLANs, disabling iLO, or defaulting to shared NIC mode regardless of requirements all reduce security or manageability.
NEW QUESTION # 49
What is one role played by the retriever reranking NIM in the NVIDIA NeMo Retriever pipeline?
Answer: B
Explanation:
The reranking NIM re-scores candidate passages retrieved from the vector database and ranks them by relevance to the user query, so the best context is passed to the LLM.
NEW QUESTION # 50
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: C
Explanation:
MLIS supports canary rollout for a new model version by assigning a traffic percentage, such as
10%, to the canary while the existing model continues receiving the remaining traffic.
NEW QUESTION # 51
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: D
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 # 52
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