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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Troubleshooting HPE Private Cloud AI | 10% | - Performance tuning and optimization - Issue diagnosis and resolution |
| Topic 2: High Performance Computing (HPC) Design and Management | 10% | - Management and monitoring - Architecture and scaling |
| Topic 3: Solution Optimization and Security | 5% | - Security best practices - Efficiency and resilience |
| Topic 4: HPE ProLiant Gen12 for AI | 20% | - Server architecture and components - GPU integration and configuration - AI-optimized compute design |
| Topic 5: AI Workshops and HPE AI Essentials Platform | 15% | - Customer engagement and assessment - Platform capabilities and deployment |
| Topic 6: HPE AI Essentials: Data Analytics and Data Science | 15% | - Data pipelines and processing - Analytics and machine learning tools |
| Topic 7: NVIDIA AI Enterprise and Model Serving | 10% | - Software deployment and management - Model serving architecture and optimization |
| Topic 8: Integration with Third-Party Platforms | 15% | - VMware integration - Red Hat OpenShift and Kubernetes integration |
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NEW QUESTION # 58
A customer needs to deploy a NIM LLM with HPE Machine Learning Inference Software running on HPE Private Cloud AI (AI Essentials 1.6.1).
What is one option for the image for serving the model?
Answer: A
Explanation:
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 5 "Serving NIMs with HPE Machine Learning Inference Software"; HPE AI Essentials/MLIS registry documentation and release-history notes.
NEW QUESTION # 59
A customer is running SUSE Rancher on HPE ProLiant DL servers and HPE Alletra arrays.
How should you install the HPE CSI Driver for Kubernetes?
Answer: D
Explanation:
C is correct after updating the terminology to the current SUSE Rancher interface: use Rancher Apps & Marketplace and the official Partner repository/catalog entry to install the HPE CSI Driver for Kubernetes.
HPE publishes the CSI integration so that Rancher administrators can deploy it through the native application- management workflow, which handles the Helm-based installation and ongoing configuration in a form consistent with Rancher operations.
Option A is technically possible in generic Kubernetes environments because HPE provides Helm deployment methods, but the scenario specifically asks how to install the driver in SUSE Rancher. The integrated Rancher catalog workflow is the preferred answer. Option B is incorrect because an Ansible playbook is not the standard driver installation path. Option D references a Data Ops Manager plug-in that is not required to deploy the CSI driver.
The original wording "SUSE Rancher Operators catalog" is outdated/inexact; current Rancher releases expose this through Apps & Marketplace/Partner repositories. The underlying concept remains the same: use the supported Rancher-integrated package rather than building a separate deployment mechanism.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 8 "HPE integrations with third- party container platforms"; HPE Storage Container Orchestrator documentation, SUSE Rancher deployment of the HPE CSI Driver.
NEW QUESTION # 60
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: D
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 # 61
An AI team is using Spark Applications to analyze and format data for AI fine-tuning. The team would like to automate running the applications in a series.
Which component of HPE AI Essentials can help?
Answer: D
Explanation:
B is correct. Airflow is the workflow-orchestration component in HPE AI Essentials. It is designed to define directed acyclic graphs (DAGs) whose tasks execute in a controlled sequence based on dependencies. If a team has several Spark applications that prepare, transform, and validate data before model fine-tuning, Airflow can schedule those applications as stages in a repeatable pipeline and coordinate retries, dependencies, and execution order.
Katib and Ray Tune are focused on hyperparameter optimization rather than general-purpose workflow orchestration. MLflow tracks experiments, runs, artifacts, and model lifecycle information; it is not the scheduler that executes an ordered series of Spark jobs. Airflow complements these components rather than replacing them.
The key exam distinction is between orchestration and execution engines. Spark performs distributed data processing. Airflow controls when the Spark applications run and how one stage depends on another. That separation permits the data-preparation pipeline to be automated without embedding orchestration logic inside each Spark application.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 4 "Using Spark" and "Airflow"; HPE AI Essentials Software Airflow documentation.
NEW QUESTION # 62
Which HPE Gen11 server component is specifically responsible for enforcing that only cryptographically signed firmware images can be flashed to the system, rejecting unsigned or tampered images?
Answer: D
Explanation:
The firmware verification mechanism within HPE's Silicon Root of Trust design validates the digital signature of firmware before it is allowed to run or be flashed, rejecting compromised images. The Security Override Switch is a physical jumper for recovery scenarios, Server Configuration Lock prevents unauthorized hardware/BIOS changes, and Chassis Intrusion Detection reports physical case-open events.
NEW QUESTION # 63
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