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| Section | Weight | Objectives |
|---|---|---|
| NVIDIA AI Enterprise and Model Serving | 10% | - Model serving architecture and optimization - Software deployment and management |
| AI Workshops and HPE AI Essentials Platform | 15% | - Platform capabilities and deployment - Customer engagement and assessment |
| HPE AI Essentials: Data Analytics and Data Science | 15% | - Data pipelines and processing - Analytics and machine learning tools |
| HPE ProLiant Gen12 for AI | 20% | - Server architecture and components - AI-optimized compute design - GPU integration and configuration |
| Troubleshooting HPE Private Cloud AI | 10% | - Issue diagnosis and resolution - Performance tuning and optimization |
| Solution Optimization and Security | 5% | - Security best practices - Efficiency and resilience |
| High Performance Computing (HPC) Design and Management | 10% | - Architecture and scaling - Management and monitoring |
| Integration with Third-Party Platforms | 15% | - Red Hat OpenShift and Kubernetes integration - VMware integration |
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NEW QUESTION # 65
HPE Private Cloud AI Users want to run interactive Spark applications from notebook servers running on HPE AI Essentials.
What should you do?
Answer: D
Explanation:
B is correct. Livy provides a REST-based service for creating and managing interactive Apache Spark sessions. In HPE AI Essentials, notebook users who need interactive Spark execution rely on Livy so that notebook-side Spark magic or client commands can submit code to a managed Spark session without embedding a full Spark cluster process inside the notebook itself. Therefore the administrator should ensure Livy is installed and available through the platform's Tools & Frameworks administration.
Option A is unnecessary because the platform can provide the supported notebook-to-Spark integration once Livy is present; simply building a custom image does not establish the backend session service. Option C assumes that an image name creates Spark connectivity, which it does not. Option D confuses the Spark Operator, used for Kubernetes-managed SparkApplication resources, with the interactive session mechanism requested in the question.
The exam distinction is between batch and interactive Spark. Spark Operator is well suited to declarative Spark applications; Livy enables interactive sessions from notebooks.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 4 "Using Spark" and Kubeflow notebooks; HPE AI Essentials Software documentation for Livy and interactive Spark access.
NEW QUESTION # 66
You are deploying several HPE ProLiant Gen12 servers to act as VMware vSAN nodes.
How should you configure the vSAN drives on the servers' storage controller?
Answer: C
Explanation:
A is correct. VMware vSAN is designed to manage its storage devices directly so that it can create disk groups or storage pools, apply its own data-placement policies, and monitor device health and performance.
On HPE ProLiant servers using a supported storage controller, vSAN data drives should therefore be presented in passthrough/HBA mode where the controller and vSAN design support it. This exposes the individual drives rather than hiding them behind a hardware RAID virtual disk.
RAID 1 and RAID 5 in options D and C would move redundancy and data placement into the controller, preventing vSAN from managing the devices as intended. Option B incorrectly treats erasure coding as a storage-controller mode. vSAN can apply software-defined RAID/erasure-coding policies at the datastore layer, but that is separate from configuring the physical controller.
The exact controller, firmware, driver, and device configuration must match the VMware Compatibility Guide and HPE vSAN design guidance. Passthrough is the architectural principle; supportability still depends on the validated hardware stack.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 8 "VMware vSAN on HPE solutions"; HPE and VMware vSAN configuration guidance.
NEW QUESTION # 67
What is one action that an HPE Private Cloud AI Administrator can perform from the Alerts view in HPE AI Essentials?
Answer: D
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 # 68
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: B
Explanation:
NVIDIA Blueprints provide sample/reference AI applications that accelerate development of complex demos and enterprise AI workflows; they are available through NVIDIA AI Enterprise/NGC resources.
NEW QUESTION # 69
What is one difference between a Class 1 and a Class 3 HPE Slingshot topology?
Answer: C
Explanation:
A is correct. HPE Slingshot topology classes trade edge-facing capacity against the inter-switch links needed to scale the fabric. A Class 1 topology uses a smaller topology structure and can devote more switch ports to L0 connections toward compute-node NICs. A Class 3 topology uses additional switch groups and therefore consumes more switch ports for L1/L2 fabric connectivity, leaving fewer host-facing ports per switch even though the overall fabric can scale to a much larger endpoint count.
Option B reverses the scaling relationship: Class 3 is the topology intended for larger fabrics, not Class 1.
Option C also reverses the port-ratio concept; the larger class requires a greater proportion of inter-switch connectivity. Option D is incorrect because adaptive-routing capabilities are properties of the Slingshot fabric and switch architecture, not a special routing technology unlocked only by choosing Class 1.
This is a topology-design question rather than a raw bandwidth question. The designer balances the number of endpoint ports per switch, the number of switch groups, and the inter-switch links required to maintain the target bisection and path diversity.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 10; HPE Slingshot "Switch Topology Classes" documentation.
NEW QUESTION # 70
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