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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: Demonstrate AI Solutions | 10% | - AI solution demonstration
|
| Topic 3: Design HPE Solutions for AI and HPC Workloads | 50% | - Solution architecture and design
|
| Topic 4: Explain HPE Compute AI and HPC Solution Components and Architecture | 38% | - HPE Compute portfolio
|
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NEW QUESTION # 50
A customer wants to use the Spark framework included with HPE Private Cloud AI to ingest and process data stored in an external MinIO object store. The customer wants to avoid distributing the store's access and secret keys to AI Users.
What should you recommend that AI administrators do?
Answer: C
Explanation:
HPE AI Essentials provides centralized data-source integration that enables Spark applications to access external S3-compatible object storage without exposing storage access credentials directly to individual AI users.
The appropriate solution is to configure MinIO as an external object-store data source through the HPE AI Essentials administrative interface. Administrators specify the MinIO endpoint, access key, secret key, and required TLS settings when establishing the connection.
HPE AI Essentials then provides a secured S3 proxy endpoint through which authorized users and applications can access the connected object store. Client applications authenticate using platform-issued tokens, while the underlying object-store credentials remain managed centrally.
Spark applications can use the configured data-source name, endpoint URL, and bucket information to access the required datasets. This architecture separates application-level authorization from the object store's privileged credentials.
Option A is incorrect because configuring Keycloak authentication directly against MinIO is unnecessary for the supported integration. Option B introduces custom Spark images without addressing centralized credential management. Option D requires data replication, increasing storage requirements and operational complexity.
HPE explicitly documents this credential-isolation capability for external MinIO storage and Spark clients.
Official HPE Reference: HPE AI Essentials Software - Connecting to External S3 Object Stores; Accessing Data in External S3 Object Stores.
NEW QUESTION # 51
A customer runs latency-sensitive network functions in virtual machines and reports that the hypervisor virtual switch adds unacceptable overhead.
What should you recommend?
Answer: B
Explanation:
SR-IOV allows a physical adapter to present virtual functions that are passed directly to guest VMs, bypassing the hypervisor networking stack and reducing latency and CPU overhead. Note that SR-IOV can restrict features such as live migration in some configurations.
NEW QUESTION # 52
An HPE Synergy customer wants compute modules to boot a bare-metal OS image with no OS installed on local disks, so that hardware can be repurposed within minutes.
Which component provides this capability?
Answer: A
Explanation:
HPE Synergy Image Streamer provides stateless, image-based OS deployment. Golden images and deployment plans are applied through server profiles, allowing compute modules to boot from the Image Streamer appliance instead of local storage.
NEW QUESTION # 53
You are creating a proposal for HPE ProLiant DL servers to act as VMware ESXi hosts.
How does a customer requirement to support Fault Tolerance (FT) affect your plan?
Answer: B
Explanation:
VMware vSphere Fault Tolerance depends on processor compatibility for FT/vMotion across the hosts running the primary and secondary VMs. Storage controller RAID modes, iSCSI, and RoCEv2 are not the key FT planning requirement.
Reference: https://techdocs.broadcom.com/us/en/vmware-cis/vsphere/vsphere/8-0/vsphere- availability/providing-fault-tolerance-for-virtual-machines/fault-tolerance-requirements-limits-and- licensing.html
NEW QUESTION # 54
A customer is assessing whether to run their AI proof of concept (PoC) in the cloud or on-premises. They store sensitive data and need low-latency performance.
Which factor supports running the PoC in the data center?
Answer: C
Explanation:
Deploying AI workloads close to sensitive enterprise data can improve application performance while reducing unnecessary data movement and strengthening organizational control over information.
When an AI proof of concept processes data stored in an on-premises data center, running the computing infrastructure locally reduces reliance on external network connections and can minimize data-transfer latency.
This is especially important for latency-sensitive inference applications, real-time analytics, and AI systems that repeatedly access large datasets during processing.
Local deployment can also reduce the requirement to transfer confidential information into public cloud environments. This helps organizations maintain control over data residency, access policies, and security configurations.
HPE Private Cloud AI provides an integrated AI infrastructure that can operate within a customer's data center, combining compute, storage, networking, and AI software capabilities.
HPE explicitly positions the solution for organizations requiring data sovereignty, predictable performance, and low-latency processing near enterprise datasets.
Option A does not establish a clear on-premises advantage because container orchestration knowledge may still be necessary. Option C generally favors cloud-based resource provisioning when rapid infrastructure availability is the overriding requirement. Option D concerns commercial flexibility rather than the stated security and performance requirements.
Option B directly addresses the customer's need for local data processing and low-latency AI performance.
Official HPE Reference: HPE Private Cloud AI - Optimized AI Performance and Economics; Overview of the HPE Private Cloud AI Engineered System; Make Your Data AI-Ready.
HPE
NEW QUESTION # 55
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