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| Section | Weight | Objectives |
|---|---|---|
| Integration with Third-Party Platforms | 15% | - Red Hat OpenShift and Kubernetes integration - VMware integration |
| AI Workshops and HPE AI Essentials Platform | 15% | - Customer engagement and assessment - Platform capabilities and deployment |
| Solution Optimization and Security | 5% | - Security best practices - Efficiency and resilience |
| HPE ProLiant Gen12 for AI | 20% | - GPU integration and configuration - Server architecture and components - AI-optimized compute design |
| HPE AI Essentials: Data Analytics and Data Science | 15% | - Analytics and machine learning tools - Data pipelines and processing |
| NVIDIA AI Enterprise and Model Serving | 10% | - Model serving architecture and optimization - Software deployment and management |
| Troubleshooting HPE Private Cloud AI | 10% | - Issue diagnosis and resolution - Performance tuning and optimization |
| High Performance Computing (HPC) Design and Management | 10% | - Management and monitoring - Architecture and scaling |
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NEW QUESTION # 35
An HPE Private Cloud AI customer wants to implement access controls on Airflow DAGs such that only HPE Private Cloud AI Administrators can trigger the DAGs.
What should you recommend?
Answer: A
Explanation:
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 4 "Airflow"; June 15, 2026 HPE7- S02 release notes noting the updated Airflow RBAC method; HPE AI Essentials 1.11 "Defining RBACs on DAGs."
NEW QUESTION # 36
A customer wants to run GPU-accelerated AI training workloads and needs a rack-scale, high- density HPE compute platform optimized for this. Which product line should the integrator recommend?
Answer: A
Explanation:
HPE Apollo 6500 is purpose-built for dense GPU compute, supporting high numbers of GPUs per chassis for AI/ML and HPC training workloads. ProLiant ML350 is a tower server for general- purpose workloads, Edgeline targets ruggedized edge deployments, and MicroServer Gen10 Plus is an entry-level small-business server.
NEW QUESTION # 37
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 the customer use to achieve this goal?
Answer: C
Explanation:
D is correct. NVIDIA NeMo Curator is the data-curation component intended to prepare large datasets for generative-AI training and fine-tuning. Its pipeline capabilities include filtering, quality processing, deduplication, and privacy-oriented data preparation. That directly matches both requirements in the scenario:
duplicate records must be removed and sensitive information must be redacted before the hospital's dataset is used to adapt the LLM. Performing these controls before fine-tuning reduces data leakage risk and prevents repeated records from biasing training.
The distractors address different stages of the AI lifecycle. MLflow is principally used for experiment tracking, model lifecycle management, and related MLOps functions; it is not the primary tool for cleansing the raw training corpus. NeMo Customizer performs model customization or fine-tuning after an appropriate dataset has been prepared. Katib is a Kubernetes-native hyperparameter-optimization component and does not provide the required privacy redaction and deduplication workflow.
The sequencing matters: curate the data first, then customize and evaluate the model. That separation is part of the software-stack knowledge expected for HPE Private Cloud AI with NVIDIA.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 5 "Using NVIDIA AI Enterprise and Serving Models," NeMo deep dive; NVIDIA NeMo Curator documentation.
NEW QUESTION # 38
What option does HPE Private Cloud AI provide for organizations who want to retain Prometheus metrics over the long term?
Answer: C
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 # 39
Which correctly describes an aspect of the AI software stack for HPE Cray XD systems?
Answer: D
Explanation:
HPE Cray XD systems use standard server management based on Redfish and run a Linux- based HPC/AI software stack. HPE Performance Cluster Manager is for cluster provisioning/management, not the AI development environment.
NEW QUESTION # 40
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