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| Section | Objectives |
|---|---|
| HPE Compute Architecture and Solutions | - HPE ProLiant server families overview - Compute solution positioning and use cases |
| Server Deployment and Configuration | - Initial setup and provisioning - Firmware and system configuration tools (iLO, SPP) |
| Troubleshooting and Support | - Performance monitoring and diagnostics - Hardware and firmware troubleshooting |
| Solution Design and Implementation | - Workload sizing and architecture planning - High availability and redundancy design |
| Storage and Networking Integration | - Networking configuration and connectivity options - SAN and direct-attached storage integration |
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NEW QUESTION # 26
What is one difference between a Class 1 and a Class 3 HPE Slingshot topology?
Answer: D
Explanation:
A is correct. HPE Slingshot topology classes trade edge-facing capacity against the inter-switch links needed to scale the fabric. A Class 1 topology uses a smaller topology structure and can devote more switch ports to L0 connections toward compute-node NICs. A Class 3 topology uses additional switch groups and therefore consumes more switch ports for L1/L2 fabric connectivity, leaving fewer host-facing ports per switch even though the overall fabric can scale to a much larger endpoint count.
Option B reverses the scaling relationship: Class 3 is the topology intended for larger fabrics, not Class 1.
Option C also reverses the port-ratio concept; the larger class requires a greater proportion of inter-switch connectivity. Option D is incorrect because adaptive-routing capabilities are properties of the Slingshot fabric and switch architecture, not a special routing technology unlocked only by choosing Class 1.
This is a topology-design question rather than a raw bandwidth question. The designer balances the number of endpoint ports per switch, the number of switch groups, and the inter-switch links required to maintain the target bisection and path diversity.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 10; HPE Slingshot "Switch Topology Classes" documentation.
NEW QUESTION # 27
Which statement correctly describes the role of HPE GreenLake in an integrator's proposal for a customer wanting consumption-based IT?
Answer: B
Explanation:
HPE GreenLake is HPE's edge-to-cloud platform providing a cloud operating model -- consumption-based pricing, unified management, and workload-optimized solutions -- for infrastructure that remains on-premises or at the edge. It spans compute, storage, networking, and software services, and does not require migration to public cloud or elimination of on-prem hardware.
NEW QUESTION # 28
An HPE Private Cloud AI customer wants to prevent Airflow from consuming too many resources on the cluster.
What is one approach you can take to fulfill this request?
Answer: C
Explanation:
D is correct. Airflow pools provide a direct mechanism for controlling task concurrency. Each pool has a defined number of slots, and tasks assigned to that pool consume slots while they execute. Reducing the number of slots in the default pool therefore limits the number of tasks that can run simultaneously and places an upper bound on how aggressively Airflow can consume cluster resources across concurrent DAG activity.
Option A is not the supported control in this scenario; the Airflow UI does not serve as the general editor for executor-pod CPU and memory sizing. Option B is the opposite of the stated objective because enabling autoscaling can increase executor capacity in response to demand. Option C suggests changing framework values for executor resources, but HPE's Airflow integration historically constrains those settings and the exam-tested method for limiting aggregate workload pressure is pool concurrency.
This is a scheduling-level safeguard rather than a Kubernetes quota. In a production environment, pool slots can be combined with platform quotas and workload resource requests to create layered control over resource consumption.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Module 4 "Airflow" and Module 7 resource troubleshooting; HPE AI Essentials Airflow documentation, pool slots and maximum simultaneous jobs.
NEW QUESTION # 29
Which statement about HPE Alletra storage integration with HPE compute solutions is most accurate?
Answer: B
Explanation:
HPE Alletra is HPE's unified data storage brand spanning multiple platforms for block, file, and hybrid workloads, and it integrates with HPE GreenLake and InfoSight for AI-driven, cloud- operated management alongside compute resources, giving integrators a consistent management experience. It is not limited to block-only, edge-only, or fully siloed from compute management.
NEW QUESTION # 30
HPE Private Cloud AI Users want to run interactive Spark applications from notebook servers running on HPE AI Essentials. What should you do?
Answer: A
Explanation:
Interactive Spark sessions from notebooks use Livy; HPE AI Essentials Spark magic commands connect to the Livy server to start and manage Spark sessions. Spark Operator is for Spark Applications, not interactive notebook sessions.
NEW QUESTION # 31
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