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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Solution Optimization and Security | 5% | - Security best practices - Efficiency and resilience |
| Topic 2: HPE AI Essentials: Data Analytics and Data Science | 15% | - Analytics and machine learning tools - Data pipelines and processing |
| Topic 3: Troubleshooting HPE Private Cloud AI | 10% | - Performance tuning and optimization - Issue diagnosis and resolution |
| Topic 4: NVIDIA AI Enterprise and Model Serving | 10% | - Model serving architecture and optimization - Software deployment and management |
| Topic 5: High Performance Computing (HPC) Design and Management | 10% | - Architecture and scaling - Management and monitoring |
| Topic 6: HPE ProLiant Gen12 for AI | 20% | - GPU integration and configuration - AI-optimized compute design - Server architecture and components |
| Topic 7: Integration with Third-Party Platforms | 15% | - VMware integration - Red Hat OpenShift and Kubernetes integration |
| Topic 8: AI Workshops and HPE AI Essentials Platform | 15% | - Platform capabilities and deployment - Customer engagement and assessment |
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NEW QUESTION # 29
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 # 30
What is one way that HPE minimizes costs for the HPE Private Cloud AI Developer System?
Answer: D
Explanation:
A is correct. The HPE Private Cloud AI Developer System is positioned as a smaller, cost-conscious configuration for development and proof-of-concept work. Integrating file and object storage into the AI- optimized node reduces the need to purchase, cable, power, and administer a separate external storage tier for the entry configuration. That consolidation lowers infrastructure cost while preserving the data-access patterns required by notebooks, model assets, and AI development workflows.
The remaining choices contradict the design intent of HPE Private Cloud AI. An AI-optimized node is not made cost-effective by eliminating GPU acceleration and using CPU-only compute; doing so would undermine the target AI workload. HPE also does not reduce value by stripping out the integrated software toolchain. The platform's advantage is precisely the curated, validated software environment. NVIDIA AI Enterprise support is likewise a core differentiator of HPE Private Cloud AI with NVIDIA rather than an optional capability removed to save money.
For exam purposes, distinguish architectural consolidation from functional reduction. HPE minimizes entry- system cost by integrating necessary infrastructure components, not by removing the accelerated-compute or enterprise-AI capabilities that define the solution.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 1 "HPE ProLiant Gen12 for AI" and HPE solutions for AI; HPE Private Cloud AI engineered-system and Developer System materials.
NEW QUESTION # 31
When designing high availability for an HPE OneView managed environment, what is the purpose of deploying OneView in a multi-appliance (HA) configuration?
Answer: C
Explanation:
HPE OneView HA (clustered appliances) protects the management plane itself, ensuring continued management, monitoring, and orchestration capability if one appliance instance becomes unavailable. It does not affect VLAN counts, compute capacity, or replace Synergy Composer, which is the management appliance specific to Synergy frames.
NEW QUESTION # 32
At the request of an HPE Private Cloud AI customer, you have added an AWS S3 object store to HPE AI Essentials. Several HPE Private Cloud AI Users should be able to access that source when running notebooks and Spark applications. Other users should have no access.
What should you do?
Answer: D
Explanation:
A is correct. HPE AI Essentials provides platform-level identity and access management for connected data sources. After the S3 object store is configured, an administrator can use the Identity and Access Management interface to grant the required users privileges to that object-store data source. This gives the selected users access from supported tools such as notebooks and Spark applications while leaving other users without permission.
The HPE GreenLake cloud user-management page controls broader workspace or service access, but it is not the place to assign the fine-grained HPE AI Essentials data-source privilege described in this scenario.
Embedding user lists directly in the object-store connection settings is not the documented access-control workflow. Creating credential secrets manually in every user namespace is also the wrong pattern: it duplicates sensitive credentials, increases rotation overhead, and bypasses the platform's centralized authorization model.
The operational principle is separation of connection configuration from authorization. The platform stores the data-source integration, while Identity and Access Management determines who may use it. That model is more auditable and maintainable than distributing access keys.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 4 "Connecting to data"; HPE AI Essentials Software documentation, Identity and Access Management and user access to data.
NEW QUESTION # 33
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: B
Explanation:
Airflow DAG-level RBAC is defined in the DAG source using access_control. To allow only admins to trigger the DAG, grant the Admin role can_edit access; HPE AI Essentials admins have full DAG access through these controls.
NEW QUESTION # 34
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