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| Section | Objectives |
|---|---|
| Core Knowledge Areas | - Compute, storage, and networking solution design - Edge-to-Cloud workloads integration and architecture - Solution planning and positioning HPE products and services - Scenario-based problem solving and application - HPE GreenLake and cloud service models |
| Exam Objective | - Translate business requirements into complex, multi-site solutions - Consider all consumption models and hosting locations - Design and architect advanced HPE solutions across Edge-to-Cloud environments |
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NEW QUESTION # 61
Which statement about HPE SimpliVity is true?
Answer: C
Explanation:
The correct answer is D. In HPE SimpliVity designs, non-HPE SimpliVity compute nodes can be added to consume the storage and data services provided by SimpliVity nodes, but the supported ratio depends on the validated SimpliVity platform and processor family. For Intel-based HPE SimpliVity nodes, the supported design rule in this context allows up to eight non-HPE SimpliVity servers per SimpliVity node. Option A is incorrect because compute-node designs do not require identical CPUs across every server in the way stated; compatibility and supportability are governed by the validated platform, hypervisor, cluster, and SimpliVity federation rules. Option B is too broad and unsafe:
mixing processor families and chassis types inside the same federation is controlled by strict HPE support matrices and is not "fully supported" as a general rule. Option C gives the wrong AMD-based ratio for the scenario. HPE SimpliVity 380 Gen11 is documented as an Intel Xeon-based platform and is positioned for hyperconverged infrastructure with VM-centric management, data efficiency, backup, and disaster recovery. Reference/topics: SimpliVity sizing rules, compute-node attachment, federation design, HCI platform validation.
NEW QUESTION # 62
Your customer is in the early stages of an AI project. They are requesting a POC demonstrating how they can utilize on-prem data with a limited amount of rack space, in an air-cooled environment.
Performance with the AI use case of Retrieval-Augmented Generation (RAG) will be the primary factor in their decision making.
Which HPE Compute offering would be suitable in this scenario?
Answer: B
Explanation:
The correct answer is C because the customer's requirement is for an early-stage AI proof of concept, on-prem data usage, limited rack space, and an air-cooled environment. HPE ProLiant DL320 Gen11 with NVIDIA L4 GPUs is a compact, rack-efficient platform suitable for AI inference and edge-style workloads. HPE describes the DL320 Gen11 as a compact 1U platform that can support up to four NVIDIA L4 Tensor Core GPUs, making it appropriate for space-constrained AI inferencing and near- real-time insight use cases. RAG workloads often need strong inference performance and local data access, but a POC does not automatically justify large-scale H200, GH200, or Cray-class infrastructure.
NEW QUESTION # 63
Your customer wants to deploy HPE GreenLake for a private cloud experience but wants to retain full control over the underlying hardware lifecycle in their own data center. Which statement accurately describes this deployment model?
Answer: D
Explanation:
HPE GreenLake delivers a pay-per-use, cloud-like consumption model for infrastructure that physically resides in the customer's own data center or colocation space. HPE (or a partner) owns and manages the hardware, including maintaining a capacity buffer so the customer can scale without a forklift upgrade.
NEW QUESTION # 64
Your customer is expecting to see an increase in the amount of data stored on their HPE Alletra Storage MP B10000.
They are asking to create a plan to support a raw capacity of 450 TB all flash.
Refer to exhibit
What change would you recommend to the configuration to achieve this?
Answer: A
Explanation:
The correct answer is C. The exhibit shows an HPE Alletra Storage MP B10000 configuration based on QLC flash, with raw capacity below the customer's 450 TB target. The correct capacity adjustment is to increase the number of QLC drives in the system while selecting the sparing policy that maximizes available usable capacity for the intended design. Replacing QLC with TLC is not the right direction for a high-capacity all-flash requirement because QLC media is positioned for dense, cost-efficient capacity expansion. Adding host bus adapters does not increase raw storage capacity, so option A is unrelated to the requirement. Replacing controller nodes is also unnecessary because the problem is media capacity, not controller performance. HPE Alletra Storage MP B10000 QuickSpecs identify scale-out capacity characteristics, including maximum raw capacity per shelf, confirming that the platform is intended to scale capacity through shelves and drive population rather than by changing host-connectivity cards. The correct plan is to add QLC capacity to reach the raw-capacity target and adjust sparing appropriately. Reference/topics: B10000 capacity planning, QLC all-flash sizing, sparing policy, GreenLake proposal validation.
NEW QUESTION # 65
You are designing an HPE solution for SAP HANA. You are considering an as-a-service approach as one of the variants to be proposed to the customer. What is true regarding HPE GreenLake for SAP HANA?
Answer: D
Explanation:
The correct answer is B. HPE GreenLake for SAP HANA is positioned as an on-premises, consumption- based infrastructure model for SAP HANA rather than a public-cloud runtime service or a user-count licensing model. The key sizing and financial driver is the SAP HANA memory footprint because SAP HANA is an in-memory database platform. HPE describes the GreenLake SAP HANA model as a complete pay-per-use infrastructure solution with an on- premises appliance, operating system, and services, and states that the simplified monthly invoice is based on the total gigabytes of RAM used by the SAP HANA system.
NEW QUESTION # 66
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