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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Demonstrate AI Solutions | 10% | - AI solution demonstration
|
| Topic 2: Design HPE Solutions for AI and HPC Workloads | 50% | - Solution architecture and design
|
| Topic 3: Explain HPE Compute AI and HPC Solution Components and Architecture | 38% | - Management and automation
|
| Topic 4: Manage Implementation and Startup of HPE AI and HPC Solutions | 2% | - Deployment and validation
|
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NEW QUESTION # 38
You are recommending HPE Slingshot as the interconnect for an HPC cluster that also performs AI training.
Which statement accurately describes this solution's benefits for this use case?
Answer: D
Explanation:
HPE Slingshot is a high-performance interconnect engineered for tightly coupled HPC and AI workloads that require predictable communication latency, high bandwidth, and efficient congestion management.
Distributed AI training frequently involves collective communication operations in which GPUs exchange gradients or model-related data. Certain synchronization operations cannot complete until all participating accelerators have finished their required communications.
Consequently, the slowest network flow can influence the completion time of an entire training iteration. This makes tail latency particularly important.
HPE Slingshot addresses this challenge through fine-grained adaptive routing and hardware-based congestion management.
Adaptive routing continuously evaluates network conditions and redirects traffic across available paths to balance utilization. HPE documents packet-by-packet routing for unordered traffic and flow-based routing for protocols requiring ordered delivery.
Its congestion-management mechanisms identify sources of excessive traffic and regulate transmission rates to reduce congestion and execution-time variability.
Option A incorrectly treats aggregate bandwidth as the sole performance determinant. Option B misrepresents Slingshot as a combination of separate InfiniBand and Ethernet technologies. Option C incorrectly emphasizes average latency and conventional ECMP routing.
Option D correctly identifies the importance of low tail latency and the technologies HPE Slingshot uses to achieve it.
Official HPE Reference: HPE Slingshot QuickSpecs - Automatic Congestion Management, Fine-Grained Adaptive Routing, and High Bandwidth and Low Latency.
NEW QUESTION # 39
An automotive engineering team is running inference workloads for traffic simulation and computer vision models using KServe on HPE Private Cloud AI.
The team also uses GPU-enabled notebooks running on HPE Private Cloud AI to experiment with new models. They want to automatically adjust GPU usage for the different models based on current demand, as well as ensure that the notebooks never prevent the inferencing workloads from receiving the GPUs they need.
Which two features support this functionality? (Choose two.)
Answer: B,C
Explanation:
KServe autoscaling adjusts inference deployment capacity based on demand. HPE AI Essentials scheduler priorities ensure higher-priority inference workloads can get GPUs ahead of lower- priority notebook workloads.
Reference: https://kserve.github.io/website/docs/model-serving/predictive- inference/autoscaling/hpa-autoscaler
https://support.hpe.com/hpesc/public/docDisplay?docId=a00aie17hen_us&docLocale=en_US&pa ge=pcai%2FGenAI%2Fdeploying-knowledge-base.html
NEW QUESTION # 40
You are helping a customer plan to use their HPE Private Cloud AI solution securely.
What is one recommendation to make?
Answer: B
Explanation:
HPE AI Essentials includes built-in self-signed certificates, and HPE documents replacing them with custom/CA-signed certificates for secure production use. The other options are not standard recommended security steps for HPE Private Cloud AI.
Reference:
https://support.hpe.com/hpesc/public/docDisplay?docId=a00aie18hen_us&docLocale=en_US&pa ge=Security%2FCA-SSL-Cert%2Fupdate-cert.html
https://support.hpe.com/hpesc/public/docDisplay?docId=a00aie19hen_us&docLocale=en_US&pa ge=Security%2FCA-SSL-Cert%2Fusing-cert-manager.html
NEW QUESTION # 41
A customer runs a transactional database with a write-intensive I/O profile on twelve SAS SSDs attached to an HPE MR controller and requires fault tolerance with minimal write penalty.
Which RAID level should you recommend?
Answer: B
Explanation:
RAID 10 mirrors and stripes, incurring a write penalty of 2 compared with 4 for RAID 5 and 6 for RAID 6. For write-heavy transactional workloads where usable capacity is secondary to write performance, RAID 10 is the appropriate choice.
NEW QUESTION # 42
You are discussing HPE Private Cloud AI with a customer. The customer says they need a simpler way to visualize analyses of data spanning multiple database types, such as SQL Server and MongoDB.
What should you explain?
Answer: B
Explanation:
HPE AI Essentials integrates Apache Superset and EzPresto to provide federated data analysis and visualization across multiple enterprise data sources.
EzPresto serves as an accelerated SQL query engine that enables organizations to query data residing in different databases and storage systems through a unified analytical interface.
Rather than requiring every dataset to be migrated into a single database, the architecture allows supported data sources to be queried through configured connectors.
Apache Superset provides the visualization layer. Users can create analytical datasets, execute queries, generate charts, and combine visualizations into interactive dashboards.
For the customer scenario, SQL Server and MongoDB connections can be incorporated through compatible data-source integrations, subject to the available connectors and database configuration. EzPresto processes supported federated queries, while Superset presents the analytical results visually.
Option B is incorrect because Knowledge Bases primarily support retrieval-augmented generation rather than general-purpose business intelligence dashboards.
Option C describes notebook-based data exploration but does not provide the integrated analytics and visualization workflow being requested.
Option D incorrectly identifies HPE GreenLake for File Storage as the primary database federation and reporting service.
The combined Superset and EzPresto architecture is therefore the most appropriate recommendation for simplifying analytics across heterogeneous enterprise data sources.
Official HPE Reference: HPE AI Essentials Software 1.11.x - Superset; BI Reporting (Superset) Basics; Installing Included Frameworks.
HPE AI Essentials Software 1.11.x Documentation
NEW QUESTION # 43
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