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| Section | Objectives |
|---|---|
| Topic 1: Deployment and Operations | - Deployment models for AI solutions - Monitoring and optimization - Lifecycle management of AI infrastructure |
| Topic 2: Security in Private Cloud AI | - Workload and data protection - Identity and access management |
| Topic 3: HPE GreenLake for AI Solutions | - Consumption-based IT model for AI - GreenLake architecture and services |
| Topic 4: HPE Private Cloud AI Fundamentals | - Core AI workload characteristics - Overview of private cloud AI concepts |
| Topic 5: Data Management and Governance | - Data governance and compliance - Data lifecycle management |
| Topic 6: AI Infrastructure Design | - Networking for AI workloads - Storage and data pipeline design - Compute and GPU considerations |
1. A data science team is struggling to manage their AI/ML projects. They use a variety of open-source tools for data preparation, training, and MLOps, but integrating them is complex and time-consuming.
They need a unified platform that provides self-service access to a curated and pre-integrated set of these tools.
Which HPE Private Cloud AI software component is specifically designed to solve this problem?
A) NVIDIA NIM
B) HPE Intelligent Configurator
C) HPE GreenLake for File Storage
D) HPE AI Essentials
2. A customer needs a solution for their deployed customer service chatbot. They state: "We don't need to change the model itself, but we need the chatbot to answer questions using our product documentation, which is updated every night. The answers must be fast and based on the latest documents." How would you categorize this workload?
A) A large-scale model training workload.
B) A RAG (Retrieval-Augmented Generation) inferencing workload.
C) A classic AI inferencing workload.
D) A model development and experimentation workload.
3. After using the HPE Intelligent Configurator, an architect reviews the output for a proposed HPE Private Cloud AI solution.
In addition to the BOM, what other critical information does the tool provide that is essential for site readiness planning? (Select all that apply.)
A) A list of recommended AI models for the specified use case.
B) A list of recommended HPE education courses for the IT staff.
C) The estimated power consumption in kW per rack.
D) The total weight of the solution for data center floor loading considerations.
E) A sample Python script for running the first inference job.
4. What is the primary architectural advantage of the NVIDIA Grace Hopper Superchip (e.g., GH200) for large-scale AI workloads?
A) It uses on-chip encryption to create a confidential computing environment for the CPU.
B) It is the first NVIDIA GPU to feature fourth-generation Tensor Cores for enhanced matrix calculations.
C) It combines a CPU and a GPU on a single superchip, connected by a high-speed, low-latency NVLink-C2C interconnect.
D) It replaces the need for server memory (DRAM) by using the GPU's global memory exclusively.
5. A customer is considering the HPE Private Cloud AI solution. They need to run a moderately sized RAG application for 150 users. They do not have any fine-tuning requirements.
Why would an architect recommend a "Medium" configuration over a "Large" configuration for this customer?
A) The Medium configuration has more storage capacity than the Large configuration.
B) The Medium configuration is the only one that includes the HPE AI Essentials software.
C) The Large configuration does not support Retrieval-Augmented Generation (RAG).
D) The L40S GPUs in the Medium configuration are more cost-effective and power-efficient for this specific inference-heavy workload.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: B | Question # 3 Answer: A,C,D | Question # 4 Answer: C | Question # 5 Answer: D |
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