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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Integration with Third-Party Platforms | 15% | - VMware integration - Red Hat OpenShift and Kubernetes integration |
| Topic 2: Solution Optimization and Security | 5% | - Efficiency and resilience - Security best practices |
| Topic 3: High Performance Computing (HPC) Design and Management | 10% | - Management and monitoring - Architecture and scaling |
| Topic 4: HPE ProLiant Gen12 for AI | 20% | - GPU integration and configuration - AI-optimized compute design - Server architecture and components |
| Topic 5: Troubleshooting HPE Private Cloud AI | 10% | - Issue diagnosis and resolution - Performance tuning and optimization |
| Topic 6: HPE AI Essentials: Data Analytics and Data Science | 15% | - Analytics and machine learning tools - Data pipelines and processing |
| Topic 7: NVIDIA AI Enterprise and Model Serving | 10% | - Software deployment and management - Model serving architecture and optimization |
| Topic 8: AI Workshops and HPE AI Essentials Platform | 15% | - Customer engagement and assessment - Platform capabilities and deployment |
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NEW QUESTION # 32
A hospital wants to fine-tune an LLM on their own dataset. However, the data needs to have sensitive information redacted. Duplicate records also need to be cleaned up. What can help the customer use to achieve this goal?
Answer: C
Explanation:
NeMo Curator is used to prepare datasets for LLM training and fine-tuning, including data cleaning, filtering, sensitive data redaction, and deduplication. MLflow tracks experiments, NeMo Customizer fine-tunes models, and Katib handles hyperparameter tuning.
NEW QUESTION # 33
Which correctly describes an aspect of the AI software stack for HPE Cray XD systems?
Answer: D
Explanation:
D is correct. HPE Cray XD systems use a conventional layered management model: platform firmware is managed through standards-based interfaces such as Redfish, while the host operating environment is Linux- based. This separation is appropriate for HPC because firmware lifecycle, hardware telemetry, node provisioning, and operating-system management are distinct administrative functions even when they are coordinated by cluster-management tooling.
Option A overstates the role of HPE GreenLake. HPE Cray XD environments are not fundamentally managed as a multi-system HPC control plane through GreenLake. Option B misnames and mischaracterizes HPE Performance Cluster Manager (HPCM); HPCM is cluster provisioning and management software, not the AI development environment itself. Option C is false because HPE does not provide a single combined firmware- and-OS image that replaces the normal platform firmware and Linux software stack.
An integrator should understand both layers: Redfish/BMC interfaces expose node hardware management and telemetry, while Linux and HPC software provide the runtime environment for schedulers, MPI, accelerators, storage clients, and applications.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 9 "HPE Cray management" and
"HPE Cray firmware and software"; HPE Cray XD management documentation.
NEW QUESTION # 34
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: C
Explanation:
HPE Compute Ops Management is the cloud-based, GreenLake-delivered management platform for ProLiant/Synergy/Apollo systems, providing lifecycle management, monitoring, and remote console access without requiring a local management appliance. HPE OneView is typically deployed on-premises (or as a VM), Insight Remote Support is focused on support-case automation, and SIM is a legacy product.
NEW QUESTION # 35
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 # 36
What is one role played by the retriever reranking NIM in the NVIDIA NeMo Retriever pipeline?
Answer: C
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 # 37
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