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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Manage Implementation and Startup of HPE AI and HPC Solutions | 2% | - Deployment and validation
|
| Topic 2: Explain HPE Compute AI and HPC Solution Components and Architecture | 38% | - Management and automation
|
| Topic 3: Demonstrate AI Solutions | 10% | - AI solution demonstration
|
| Topic 4: Design HPE Solutions for AI and HPC Workloads | 50% | - Infrastructure integration
|
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NEW QUESTION # 46
A customer is evaluating whether to run their AI proof of concept (PoC) in the cloud or on-prem.
They store sensitive data and require low-latency performance.
Which factor supports running the PoC in the data center?
Answer: A
Explanation:
Keeping the PoC in the data center supports low-latency access to local sensitive data and can reduce data movement/compliance concerns compared with sending data to the cloud.
NEW QUESTION # 47
A customer needs to upgrade their ESXi hosts, and you are proposing HPE ProLiant DL servers. When you recommend using HPE custom ESXi images with these servers, the customer states that they include their own tools on all images.
What should you explain?
Answer: B
Explanation:
HPE provides customized VMware ESXi installation images that incorporate validated hardware drivers, management agents, and utilities required for supported HPE ProLiant server configurations.
However, customers are not restricted to using an HPE-provided installation image without modification.
They can assemble customized ESXi images containing their organization's additional software components alongside HPE-certified drivers and management packages.
HPE maintains the Software Delivery Repository, commonly known as vibsdepot, which provides downloadable HPE software bundles, VMware Installation Bundles (VIBs), device drivers, and related components.
These packages can be incorporated into custom ESXi images using supported VMware tools, including Image Builder and vSphere Lifecycle Manager. This approach allows the customer to maintain enterprise- specific software while preserving validated HPE hardware support.
The architect must verify component compatibility, ESXi version support, image-profile consistency, and software signing requirements before deploying the resulting image.
Option A is incorrect because HPE does not guarantee that its bundled tools replace every customer-specific utility. Option C incorrectly relies on an OS Build Plan as the mechanism for combining images. Option D overstates the requirement to use an unchanged HPE custom image.
Option B correctly preserves deployment flexibility and hardware compatibility.
Official HPE Reference: HPE Software Delivery Repository - vibsdepot; HPE VMware ESXi Customized Image Release Notes; SPP HPE Custom Image vibsdepot Mapping.
downloads.linux.hpe.com
NEW QUESTION # 48
An HPE Private Cloud AI customer is interested in fine-tuning using Ray and asks how to submit jobs to the Ray cluster running on HPE AI Essentials.
What should you explain?
Answer: D
Explanation:
HPE AI Essentials provides an integrated notebook development environment that supports distributed AI and machine learning workloads using Ray.
The platform includes supported Jupyter notebook images with Ray libraries and an associated Ray kernel.
This enables data scientists to develop Python applications, connect to the Ray cluster, and submit distributed workloads without separately installing an entire Ray environment.
For model fine-tuning, a user can create a notebook server, activate the Ray-specific kernel, configure the required runtime dependencies, and connect to the Ray cluster running within HPE AI Essentials.
HPE's documentation recommends using Ray's JobSubmissionClient for submitting jobs. The client communicates with the Ray head service through its job-submission interface, allowing users to specify entry points, runtime environments, and resource requirements.
The Ray dashboard is primarily documented as an operational interface for inspecting resource consumption, tasks, logs, execution status, and Ray Tune experiments.
Option A does not identify the documented HPE-supported submission procedure clearly enough. Option B incorrectly presents the dashboard as the primary job-submission mechanism. Option C is unnecessary because the platform's supported Ray notebook environment includes the relevant Ray software libraries.
Option D accurately describes the integrated development environment through which users can prepare and submit Ray workloads.
Official HPE Reference: HPE AI Essentials Software 1.11.x - Connecting to Ray Cluster; Using JobSubmissionClient to Submit Ray Jobs; Support Matrix - Notebook Images and Ray Kernel Packages.
HPE AI Essentials Software 1.11.x Documentation
NEW QUESTION # 49
You want to demonstrate HPE Private Cloud AI running a complex AI application, such as a video search app, to a customer.
What is one good resource for putting together this demonstration more quickly?
Answer: C
Explanation:
NVIDIA AI Blueprints are predefined, customizable reference workflows/sample AI applications designed to accelerate building and demonstrating complex AI use cases. HPE AI Essentials specifically describes Blueprints as workflows for creating and deploying generative AI applications.
Reference:
https://support.hpe.com/hpesc/public/docDisplay?docId=sd00007592en_us&docLocale=en_US& page=GUID-7899509F-3811-4AB7-9528-DCD0A948F4FE.html
NEW QUESTION # 50
A customer is interested in HPE Private Cloud AI's Data Lakehouse Gateway and needs help determining whether this gateway will work with their data lakehouse.
What is one requirement to explain?
Answer: D
Explanation:
The Data Lakehouse Gateway is not AWS-only. HPE states it supports data lakehouses on AWS S3, NFSv4, and S3-compatible storage, so the best requirement is S3 or NFSv4 storage.
Reference:
https://support.hpe.com/hpesc/public/docDisplay?docId=a00aie111hen_us&docLocale=en_US&p age=EzPresto%2Fconnecting-data-lakehouses.html
NEW QUESTION # 51
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