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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Workshops and HPE AI Essentials Platform | 15% | - Customer engagement and assessment - Platform capabilities and deployment |
| Topic 2: HPE ProLiant Gen12 for AI | 20% | - Server architecture and components - AI-optimized compute design - GPU integration and configuration |
| Topic 3: HPE AI Essentials: Data Analytics and Data Science | 15% | - Data pipelines and processing - Analytics and machine learning tools |
| Topic 4: Integration with Third-Party Platforms | 15% | - Red Hat OpenShift and Kubernetes integration - VMware integration |
| Topic 5: NVIDIA AI Enterprise and Model Serving | 10% | - Software deployment and management - Model serving architecture and optimization |
| Topic 6: Solution Optimization and Security | 5% | - Efficiency and resilience - Security best practices |
| Topic 7: Troubleshooting HPE Private Cloud AI | 10% | - Issue diagnosis and resolution - Performance tuning and optimization |
| Topic 8: High Performance Computing (HPC) Design and Management | 10% | - Management and monitoring - Architecture and scaling |
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NEW QUESTION # 49
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: B
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 # 50
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 # 51
You are proposing HPE Private Cloud AI to a financial institution and want to demo the solution running a complex AI-enabled security application.
What can help you to accelerate the development of the demo?
Answer: A
Explanation:
B is correct. NVIDIA AI Blueprints are packaged reference workflows that combine models, NIM microservices, retrieval components, guardrails, and application logic for specific generative-AI use cases.
Selecting a suitable blueprint from the NVIDIA catalog can shorten a proof-of-concept cycle because the team starts from a validated architecture and deployable workflow instead of assembling every component from scratch. That is particularly useful when demonstrating a complex security-oriented AI application to a regulated customer.
Sample Kubeflow pipelines can accelerate individual machine-learning workflow steps, but they do not provide the same end-to-end application blueprint. NVIDIA Base Command Manager is an infrastructure and cluster-management product; it is not the development accelerator being asked for. HPE AI Essentials tutorials are useful instructional resources, but a blueprint is explicitly intended to speed implementation of a working use case.
Current Rev. 26.21 training has a dedicated module on using NVIDIA Blueprints and updates the recommended installation process for validated blueprints, reinforcing their role in demonstrations and PoCs.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 6 "Using an NVIDIA Blueprint to demo an AI Chatbot"; NVIDIA AI Blueprints catalog and deployment documentation.
NEW QUESTION # 52
What option does HPE Private Cloud AI provide for organizations who want to retain Prometheus metrics over the long term?
Answer: D
Explanation:
HPE Private Cloud AI can export observability data to an external OpenTelemetry endpoint, allowing organizations to retain Prometheus metrics long term outside the default in-cluster retention period.
NEW QUESTION # 53
What is one action that an HPE Private Cloud AI Administrator can perform from the Alerts view in HPE AI Essentials?
Answer: C
Explanation:
C is correct. The Alerts view in HPE AI Essentials is designed to move an administrator from a detected condition to the evidence needed for diagnosis. From an alert, the administrator can open the logs for the pod associated with that alert. That is materially different from taking a remediation action such as restarting a pod: the Alerts interface provides observability and troubleshooting context, while restart or recovery actions are performed through the appropriate Kubernetes or platform-management workflow. Alert forwarding is separately configured in the platform's observability settings rather than as a per-alert action, and HPE GreenLake does not provide an alert-correlation function from this local Alerts view.
This is an important operational distinction for the exam. HPE Private Cloud AI troubleshooting follows an evidence-first workflow: identify the alert, inspect the affected workload and its logs, and then determine the corrective action. Pod logs can expose image-pull failures, scheduling problems, resource exhaustion, application exceptions, or service communication errors without immediately changing system state.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 7 "Troubleshooting HPE Private Cloud AI," especially alerts and solution-provided tools; HPE AI Essentials Software documentation,
"Viewing Alerts and Notifications."
NEW QUESTION # 54
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