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| Section | Weight | Objectives |
|---|---|---|
| Solution Optimization and Security | 5% | - Security best practices - Efficiency and resilience |
| AI Workshops and HPE AI Essentials Platform | 15% | - Customer engagement and assessment - Platform capabilities and deployment |
| High Performance Computing (HPC) Design and Management | 10% | - Management and monitoring - Architecture and scaling |
| NVIDIA AI Enterprise and Model Serving | 10% | - Model serving architecture and optimization - Software deployment and management |
| HPE AI Essentials: Data Analytics and Data Science | 15% | - Data pipelines and processing - Analytics and machine learning tools |
| Troubleshooting HPE Private Cloud AI | 10% | - Performance tuning and optimization - Issue diagnosis and resolution |
| Integration with Third-Party Platforms | 15% | - Red Hat OpenShift and Kubernetes integration - VMware integration |
| HPE ProLiant Gen12 for AI | 20% | - GPU integration and configuration - Server architecture and components - AI-optimized compute design |
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NEW QUESTION # 59
A customer has HPE ProLiant DL Gen12 servers managed by HPE Compute Ops Management and acting as VMware vSphere ESXi hosts. How can you handle updating the servers' firmware and OS together?
Answer: A
Explanation:
HPE Compute Ops Management integrates with VMware vCenter/vSphere Lifecycle Manager so vLCM can manage ESXi host lifecycle, including OS, driver, and firmware updates together for supported HPE servers. HPE notes that Gen10 and later servers managed by Compute Ops Management are supported.
NEW QUESTION # 60
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: A
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 # 61
A customer has HPE Cray XD systems, an HPE Slingshot network that includes HPE Slingshot Fabric Manager, HPE Performance Cluster Manager (HPCM), HPE Slingshot AIOps Software, and SchedMD Slurm.
Which tool provides heatmaps and interactive topologies that help you find trouble spots in the network performance?
Answer: B
Explanation:
A is correct. HPE Slingshot AIOps Software is the analytics and visualization layer designed to expose network performance behavior across a Slingshot fabric. It provides topology-oriented views and telemetry heatmaps that allow an administrator to identify regions with abnormal bandwidth use, congestion, or other performance hot spots. Those visualizations are particularly useful when correlating network behavior with application or job activity.
HPE Slingshot Fabric Manager has a different primary role: fabric configuration, management, discovery, and operational control. SchedMD Slurm schedules jobs and allocates compute resources; although job information can be correlated with infrastructure telemetry, Slurm does not provide the Slingshot network heatmap interface described. HPE Performance Cluster Manager manages cluster provisioning and lifecycle operations rather than serving as the specialized Slingshot performance-analytics application.
The tools are complementary. Fabric Manager controls the network, HPCM manages the cluster, Slurm manages workload scheduling, and Slingshot AIOps helps diagnose how traffic is behaving on the fabric.
References/topics: Advanced HPE Compute Solutions, Rev. 26.21, Modules 9 and 10, HPE Cray management and HPC networking; HPE Slingshot AIOps Software User Guide, system-wide and telemetry heatmap views.
NEW QUESTION # 62
An HPE Private Cloud AI customer wants to replace the self-signed certificate that users see when they access HPE AI Essentials. The customer wants to reduce maintenance efforts by having the solution auto-renew the certificate. What is part of the implementation process?
Answer: D
Explanation:
To enable automatic certificate renewal, HPE AI Essentials uses cert-manager. The implementation includes configuring the CA/DNS service and creating a ClusterIssuer so cert- manager can issue and renew the ingress certificate automatically.
NEW QUESTION # 63
A customer requires that decommissioned HPE server drives be cryptographically erased to meet data-sanitization compliance before the drives leave the data center. Which HPE feature satisfies this requirement?
Answer: A
Explanation:
HPE Secure Erase (available via iLO/RESTful interfaces and Smart Storage Administrator) performs NIST-compliant cryptographic or media sanitization erasure of drives. A RAID rebuild does not sanitize data, an iLO factory reset only resets the management processor, and Secure Boot validates boot software rather than erasing data.
NEW QUESTION # 64
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