HPE1-H04 Advanced HPE Edge-to-Cloud Solutions Practical Exam Practice Questions
Prepare for HPE1-H04 with more than an answer.
- 1
An enterprise architect is evaluating consumption models for different workloads within a multinational conglomerate. Which TWO workload scenarios correctly align with the recommended HPE procurement models? (Select TWO)
Show answer details
Correct answer: A, D
A static, non-growing workload with guaranteed funding through an allocated CapEx budget and fixed long-term depreciation is ideally suited for a Traditional HPE purchase model. Variable, unpredictable containerized microservices benefit significantly from GreenLake pay-per-use with elastic buffer capacity. Standard standardized modular workloads are served by GreenLake Core/standard SKUs rather than requiring complex GLCS custom engineering.
GreenLake pay-per-use is designed for dynamic, growing workloads where on-premises latency/compliance is required, eliminating overprovisioning through committed reserve plus variable buffer capacity.
- 2
In an HPE GreenLake Flex contract, which mechanism protects the customer from service disruption during unexpected demand surges while guaranteeing a predictable financial baseline for HPE?
Show answer details
Correct answer: B
HPE GreenLake Flex structures billing around a minimum contracted commitment termed 'reserve capacity', which establishes the baseline predictable monthly payment. Deployed alongside this is an active 'variable buffer capacity' installed on-premises ahead of demand. When demand surges, workloads burst into the buffer seamlessly and are billed based on metered unit usage, avoiding procurement delays and downtime. Hard usage caps cause throttling, and credit card pay-as-you-go is a hyperscaler model rather than enterprise GreenLake Flex.
- 3
A government agency requires an on-premises private AI infrastructure to fine-tune open-weight Large Language Models (LLMs) and host real-time inferencing applications using high-security classified intelligence data. The infrastructure must support NVIDIA Blackwell architectures, scale horizontally using a federated private AI architecture, and run entirely within a secure air-gapped facility. Which HPE solution directly addresses this requirement?
Show answer details
Correct answer: D
HPE Private Cloud AI (PC AI) is a turnkey, co-engineered private AI solution featuring an integrated AI software stack, federated architecture capabilities, full air-gapped operational options for classified environments, and support for next-generation accelerators including NVIDIA Blackwell architectures. Standard PCE PC7000 is a general-purpose private cloud (VMs, containers, bare metal) not turnkey-optimized for AI workflows. Ezmeral Runtime Enterprise sizing tools are legacy/decommissioned. PC3000 is targeted at general virtualization and dHCI, not specialized federated AI training.
- 4
A lead infrastructure architect is designing a high-availability multi-site storage architecture for an enterprise financial transaction database using HPE Alletra Storage MP B10000 systems. The design requires zero Recovery Point Objective (RPO) and automatic, transparent failover across two metro data centers separated by 15 kilometers, with no human intervention needed during an array outage. Which configuration must the architect specify in the design?
Show answer details
Correct answer: B
Active Peer Persistence over HPE Alletra MP B10000 Remote Copy provides synchronous replication with automated, transparent failover across metro distances (RPO=0, RTO=0). It requires an external Quorum Witness to avoid split-brain scenarios and uses host proximity definitions within replication policies to ensure optimized I/O paths for multi-site stretch clusters. Periodic asynchronous replication cannot achieve RPO=0. Active-active multi-controller clustering without Remote Copy cannot span metro data centers 15 km apart.
- 5
A manufacturing enterprise is updating its core computing platform. The enterprise operates a multi-terabyte in-memory SAP HANA database requiring certified scale-up shared-memory capacity, while simultaneously deploying a dedicated deep learning model training cluster requiring high-density accelerator nodes with liquid cooling. Which compute architecture pairing should the architect propose to satisfy these distinct technical requirements?
flowchart TD Workloads[Enterprise Workload Demands] --> SAP[In-Memory SAP HANA Scale-Up] Workloads --> AI[High-Density AI Model Training] SAP --> Comp1[HPE Compute Scale-up Server 3200] AI --> Comp2[HPE Compute XD690 GPU Clusters]Show answer details
Correct answer: A
The HPE Compute Scale-up Server 3200 is engineered specifically for mission-critical, large-scale symmetric multiprocessing (SMP) workloads such as in-memory SAP HANA environments requiring massive coherent memory footprints. Conversely, the HPE Compute XD690 is designed for high-density, high-performance AI model training, accommodating advanced GPU accelerators and high-efficiency liquid or specialized air cooling. The ProLiant DL380 Gen11 does not provide the multi-socket SMP scale-up coherent memory needed for very large monolithic SAP HANA instances, and the Alletra Storage Server 4210 is an ultra-dense storage server rather than an AI training compute platform.
- 6
An international retail corporation requires continuous data protection against sophisticated ransomware threats, combined with centralized monitoring of power consumption, ambient thermal levels, and carbon footprint across its distributed data centers. Which combination of HPE solutions directly addresses both the business continuity and environmental tracking requirements?
Show answer details
Correct answer: D
HPE Zerto with Cyber Resilience Vault (CRV) delivers software-defined continuous data protection, non-disruptive disaster recovery testing, and an isolated, air-gapped immutable recovery environment designed to neutralize ransomware attacks. To address environmental factors, the HPE Sustainability Insight Center aggregates energy usage, carbon emissions, and thermal telemetry across HPE infrastructure on the GreenLake platform. Fabric Composer manages networking fabrics, while Compute Ops Management focuses on server firmware and lifecycle rather than dedicated carbon footprint and sustainability governance.
