This six-week HPE7-S01 study plan is designed to guide learners from foundational understanding to full professional competency in HPE AI and HPC solutions. The plan follows a progressive structure that begins with core architectural knowledge, advances into solution design, implementation, and operational practices, and culminates in end-to-end AI solution demonstration and exam-level mastery. Each week focuses on a critical domain of expertise while integrating the Pomodoro method and spaced-repetition principles to maximize retention and learning efficiency.
Throughout the program, you will engage in structured study sessions, hands-on tasks, system diagrams, scenario analyses, architecture design exercises, and regular consolidation cycles. By the end of the plan, you will have developed both the technical depth and the practical reasoning skills necessary to understand, design, deploy, and articulate complete HPE AI/HPC environments. This study plan not only prepares you for the HPE7-S01 certification but also equips you with real-world skills applicable to modern high-performance and AI-driven computing environments.
WEEK 1 — HPE AI & HPC ARCHITECTURE FOUNDATIONS
Focus of Week 1: Build a solid and structured understanding of the entire HPE AI/HPC architecture stack, including compute, storage, interconnect, and software management.
This week sets the foundation for later design and implementation learning.
Weekly Learning Outcomes
By the end of Week 1, you should be able to:
Explain the complete HPE AI/HPC portfolio.
Describe Cray EX/XD, Apollo, ProLiant architectures in detail.
Understand the internal mechanics of ClusterStor and Lustre.
Explain Slingshot, InfiniBand, and Ethernet interconnect technologies.
Describe the full HPC/AI software stack from OS to scheduler to frameworks.
Draw an end-to-end AI/HPC logical architecture based on what you learned.
Learning Method
Daily workload: 5 to 8 Pomodoros (1 Pomodoro = 25 minutes study + 5 minutes rest)
After 4 Pomodoros: take a 20-minute long break.
Every day includes:
Study content
Specific detailed tasks
A short revision session following the forgetting curve
DAY 1 — HPE AI/HPC Portfolio Overview
Daily Goal: Understand the full landscape of HPE AI/HPC solutions and identify how the major components fit together.
Study Content
HPE AI & HPC portfolio overview
Cray EX and Cray XD
Apollo GPU-dense systems
ProLiant systems
GreenLake for HPC and AI
Basic cluster concepts: scale-out, shared storage, fabric networks, management frameworks
Tasks
Task 1: Create a top-level architecture map
Describe and diagram the overall AI/HPC architecture including compute, storage, interconnect, and software layers.
Include all major HPE product families.
The diagram should show: compute nodes, storage layers, fabric interconnect, and management stack.
Task 2: Write a detailed explanation (minimum 300 words)
Explain the meaning of an AI/HPC architecture.
Describe why organizations need HPC and AI systems.
Describe the role of HPE in this ecosystem.
Use your own wording.
Task 3: Create a component categorization table
Create a table listing at least 15 components across compute, storage, interconnect, and software.
Each row must include: component name, category, purpose, best workload type, key strengths.
Revision
Review the architecture diagram and table created today.
DAY 2 — Compute Architectures (Cray, Apollo, ProLiant, GPUs)
Daily Goal: Develop a deep understanding of compute architecture and the role of GPUs.
Study Content
Cray EX architecture (liquid cooling, blade layout)
Cray XD architecture (air cooled, data center friendly)
Apollo GPU nodes
ProLiant servers with GPU options
GPU types: A100, H100, L40S
GPU memory, tensor cores, and interconnects (NVLink, NVSwitch)
Tasks
Task 1: Create a compute-family comparison document
Compare Cray EX, Cray XD, Apollo, and ProLiant across at least eight dimensions: cooling, GPU density, scalability, power requirements, management tools, workload types, node density, and expandability.
Write at least one to two pages.
Task 2: Draw three internal node diagrams
Cray EX compute blade
Apollo GPU node (showing GPU positions and airflow)
NVLink or NVSwitch GPU topology diagram
These must be clear and technically structured.
Task 3: Write a 200-word explanation of GPU importance
Explain why GPUs are essential for AI and HPC.
Discuss memory bandwidth, tensor operations, and parallelism.
Task 4: Mini case study
Scenario:
A research team trains vision models using 4 GPUs per job, potentially scaling to 16 GPUs later.
Choose an HPE compute platform and justify your choice in 150 words.
Revision
Review Day 1 content following Day+1 rule.
DAY 3 — Storage Architectures (Parallel FS, Enterprise Storage, Object Storage)
Daily Goal: Understand high-performance storage and why it is critical for AI/HPC.
Study Content
Lustre architecture: metadata servers, object storage servers, striping
ClusterStor integrated architecture
Enterprise storage (Alletra, Nimble, Primera)
Object storage for data lakes and large dataset ingestion
Tasks
Task 1: Draw a complete parallel file system diagram
Include: clients, MDS, OSS, OSTs, metadata flow, data flow.
Explain the interplay between MDS and OSS.
Task 2: Write a 300–400 word explanation of data flow
Explain how a read or write operation works.
Explain why striping is important.
Explain the performance benefits for deep learning and HPC.
Task 3: Create a storage tiering table
Compare hot (parallel FS), warm ( enterprise arrays ), and cold storage (object storage).
Include capacity, performance, ideal use cases, and limitations.
Task 4: Mini storage scenario
Given: 80 TB active dataset, 2 PB archive, and 10 TB scratch needed per job.
Design a hot/warm/cold tier structure with justification.
Revision
Review Day 2 content.
DAY 4 — Interconnect and Networking (Slingshot, InfiniBand, Ethernet)
Daily Goal: Understand why network interconnects determine HPC and AI scaling performance.
Study Content
Slingshot features and design goals
Dragonfly topology
InfiniBand HDR/NDR
Ethernet in HPC and AI
RDMA basics
Tasks
Task 1: Draw a simplified Dragonfly topology
Include groups, routers, local links, and global links.
Task 2: Create a fabric comparison table
Compare Slingshot, InfiniBand, and Ethernet across latency, bandwidth, routing behavior, congestion control, and typical workloads.
Task 3: Write a 250-word explanation of distributed training bottlenecks
Explain why network performance impacts multi-node training and MPI operations.
Task 4: Case study
If running both large-scale MPI workloads and distributed deep learning, choose a fabric and justify your decision in 150 words.
Revision
Review Day 1 and Day 3 content (Day+3 review cycle).
DAY 5 — Software Stack (HPCM, Slurm, AI Frameworks, MPI, Containers)
Daily Goal: Build a full understanding of the software layers that make AI/HPC clusters usable.
Study Content
HPCM and Cray System Management
Provisioning images, managing nodes
Slurm architecture and core components
AI frameworks such as PyTorch, TensorFlow, JAX
NCCL and MPI
Containers and environment modules
Tasks
Task 1: Draw a layered software stack diagram
Layers: Physical hardware, OS, drivers, scheduler, frameworks, user layer.
Task 2: Write a 250-word Slurm explanation
Explain the role of slurmctld, slurmd, slurmdbd, partitions, and accounting.
Task 3: Framework comparison table
Compare PyTorch, TensorFlow, and JAX in terms of distributed training support and typical use cases.
Task 4: Example module-load workflow
Describe how a user loads modules and prepares an environment for training.
Include an example job script.
Revision
Review Day 2 content again (Day+3).
DAYS 6 AND 7 — Week 1 Consolidation
Daily Goal: Transform the entire week's knowledge into structured long-term understanding.
Tasks
Task 1: Create a Week 1 Summary Document
At least five pages including all diagrams, notes, and structured explanations.
Task 2: Build a full mind map
Include compute, storage, interconnect, software, and management.
Task 3: Write and answer 15 self-test questions
Cover all major concepts.
Examples:
How does Slingshot differ from InfiniBand?
Describe the flow of a Lustre read operation.
Task 4: Teach-back exercise
Choose one topic and explain it verbally for 10 minutes as if teaching a novice.
Revision
Review Day 3 content and Day 1 content again (Day+7 review cycle).
WEEK 2 — HPE AI/HPC SOLUTION DESIGN
Focus of Week 2: Learn how to translate workloads into concrete architecture decisions, including compute sizing, storage sizing, network topology design, and scheduler configuration.
Weekly Learning Outcomes
By the end of Week 2, you should be able to:
Characterize HPC, AI, and analytics workloads accurately.
Size compute nodes and determine GPU/CPU counts for specific workloads.
Design multi-tier storage strategies based on dataset characteristics.
Select appropriate interconnect fabrics and topologies.
Define scheduler partitions, quotas, and policies for different user groups.
Produce a complete solution design diagram with justifications.
WEEK 2 STRUCTURE
Daily workload: 5 to 8 Pomodoros (25 minutes each).
Includes study, tasks, and revision following the forgetting curve.
DAY 1 — Workload Characterization
Daily Goal: Understand workload behavior deeply enough to drive sizing and architecture decisions.
Study Content
HPC workloads: MPI patterns, strong scaling, weak scaling, floating-point intensity.
AI workloads: training versus inference, memory requirements, data-parallel versus model-parallel.
Analytics workloads: mixed I/O patterns, distributed processing tools such as Spark or Dask.
Identifying workload KPIs such as time-to-solution, throughput, accuracy targets.
Tasks
Task 1: Create a workload classification checklist
At least 15 items covering data size, compute intensity, memory footprint, latency sensitivity, scaling behavior, GPU needs, and I/O profile.
Task 2: Construct three workload profiles
One HPC workload, one AI training workload, and one analytics workload.
Each profile must include: dataset size, compute requirement, memory usage, I/O characteristics, scalability, and performance goals.
Task 3: Write an explanation (minimum 250 words)
Describe why workload characterization is essential for designing an HPE AI/HPC solution.
Task 4: Mini scenario
A customer runs CFD simulations, image classification training, and SQL analytics.
Identify which workloads fit HPC, AI, and analytics categories and justify in a 150-word explanation.
Revision
Review Week 1 Day 1 content.
DAY 2 — Compute Sizing
Daily Goal: Learn to estimate CPU, GPU, memory, and node counts based on defined workload characteristics.
Study Content
Node configuration design: CPU cores, memory per core, GPU count per node.
GPU sizing using samples-per-second benchmarks.
Memory sizing for HPC solvers and AI models.
Scaling strategies for training large models.
Headroom planning and growth considerations.
Tasks
Task 1: Build a compute sizing worksheet
Worksheet columns should include: workload type, GPU requirement, CPU requirement, memory requirement, expected throughput, scaling efficiency, estimated node count.
Task 2: Perform a sizing calculation for a training task
Select a standard model (for example, ResNet or BERT).
Estimate GPUs needed to reach a defined target training time.
Write your assumptions and calculations clearly.
Task 3: Compare three node configurations
Choose three hypothetical configurations (for example: 4 GPU nodes, 8 GPU nodes, CPU-only nodes).
Write a one-page comparison analyzing their impact on the workload.
Task 4: Mini scenario
A team needs to run 100 inference requests per second with low latency.
Decide whether GPU or CPU nodes are better and justify your choice.
Revision
Review Week 1 Day 2 content.
DAY 3 — Storage Sizing and Architecture
Daily Goal: Learn how data size, I/O patterns, and performance requirements drive storage architecture.
Study Content
Storage tiering: hot, warm, and cold tiers.
How striping influences throughput.
Metadata load considerations for AI workloads with many small files.
Sizing for datasets, checkpoints, logs, and archival.
Storage network considerations.
Tasks
Task 1: Create a three-tier storage design template
Include fields for: capacity, performance, reliability, use cases, and placement (parallel FS, enterprise block/file, object storage).
Task 2: Build a storage sizing example
Given a dataset of 150 TB, daily data growth of 1 TB, and frequent checkpointing, design hot, warm, and cold tier capacities.
Task 3: Write a 300-word explanation of metadata performance
Explain why metadata operations matter, especially for AI workloads with many small files, and how to design around it.
Task 4: Mini scenario
A customer trains on 200 TB of images stored as individual files.
Propose an appropriate storage configuration and justify it.
Revision
Review Week 1 Day 3 content.
DAY 4 — Network and Topology Design
Daily Goal: Understand interconnect selection and topology design from a solution architect’s perspective.
Study Content
Selecting fabrics: Slingshot, InfiniBand, Ethernet.
Designing topologies: Dragonfly, Fat Tree, Clos, HyperX.
Oversubscription and impact on performance.
Bisection bandwidth and its importance in distributed training and MPI.
Tasks
Task 1: Create a topology selection matrix
Compare Dragonfly, Clos, and Fat Tree in terms of latency, scalability, cabling complexity, cost, and typical use cases.
Task 2: Oversubscription analysis
Explain what happens when a network is oversubscribed.
Provide numerical examples demonstrating potential bottlenecks.
Task 3: Draw two topology diagrams
One for a small cluster (for example, 4 racks).
One for a medium cluster (for example, 16 racks).
Show the fabric layout clearly.
Task 4: Mini scenario
A distributed training job has degraded performance when scaling beyond 8 nodes.
Write a 200-word explanation of potential network-related causes.
Revision
Review Week 1 Day 4 content and Week 1 Day 1 content (Day+7).
DAY 5 — Scheduler and AI Stack Design
Daily Goal: Learn how to design Slurm partitions, resource limits, quotas, and AI framework stacks.
Study Content
Slurm partition types and queue configurations.
Fair-share, preemption, and priority rules.
GPU partition design.
AI framework standardization.
Module management for multiple versions.
Containerization strategy.
Tasks
Task 1: Create a Slurm partition design document
Include CPU partition, GPU partition, debug partition, and high-priority partition.
Specify limits, timeouts, and user policies.
Task 2: Define AI framework standards
Choose standard versions of PyTorch, TensorFlow, and JAX.
Explain why version control matters.
Show how containers or environment modules maintain consistency.
Task 3: Write a 250-word explanation
Explain how fair-share scheduling works and why it matters in multi-tenant HPC/AI environments.
Task 4: Mini scenario
The cluster has 10 GPU nodes.
Design a policy to ensure no single user monopolizes the GPU resources.
Revision
Review Week 1 Day 2 and Day 3 content (Day+3 review).
DAYS 6 AND 7 — Week 2 Consolidation
Daily Goal: Integrate all solution design knowledge into a coherent model.
Tasks
Task 1: Create a complete AI/HPC solution design document
At least six pages.
Must include compute, storage, network, scheduler, and AI stack design.
Task 2: Draw a complete end-to-end architecture diagram
Include compute node types, storage tiers, fabric topology, scheduler layer, and AI framework usage.
Task 3: Write and answer 15 self-test questions
Cover solution design principles, sizing logic, and architectural decisions.
Task 4: Teach-back activity
Choose one solution design topic from this week and explain it for 10 minutes as if teaching someone else.
Revision
Review Day 1 and Day 2 content again, following Day+7 cycle.
WEEK 3 — IMPLEMENTATION AND STARTUP OF HPE AI/HPC SOLUTIONS
Focus of Week 3: Learn the full lifecycle of building and launching an HPE AI/HPC cluster, including site preparation, racking, cabling, BIOS configuration, provisioning, scheduler setup, monitoring, and validation.
Weekly Learning Outcomes
By the end of Week 3, you should be able to:
Describe every implementation phase (Plan, Build, Integrate, Validate, Go-Live, Operate).
Understand all site readiness requirements (power, cooling, space, network).
Explain physical installation tasks including racking, cabling, labeling, and power distribution.
Configure firmware, BIOS, OS provisioning, and storage.
Deploy scheduler components, AI frameworks, and monitoring tools.
Perform validation and benchmarking processes.
Produce a complete implementation workflow document.
WEEK 3 STRUCTURE
Daily workload: 5 to 8 Pomodoros per day
Each day includes:
Focused study
Detailed tasks with expected outputs
Forgetting-curve-based revision
DAY 1 — Implementation Planning
Daily Goal: Understand the full lifecycle of deploying an HPE AI/HPC solution.
Study Content
Phases: Plan → Build → Integrate → Validate → Go-Live → Operate
Responsibilities: customer vs. HPE vs. third parties
Acceptance criteria for a complete deployment
Risk identification and mitigation practices
Tasks
Task 1: Write a full implementation lifecycle description
Explain every stage in your own words.
Your explanation should be at least one full page.
Task 2: Create a responsibility matrix
Columns: phase, customer role, HPE role, third-party role.
Must include at least 20 detailed items.
Task 3: Define acceptance criteria
Create a list of at least 12 acceptance criteria such as network latency targets, storage throughput levels, job scheduler functionality tests, GPU health checks, and user access verification.
Task 4: Mini scenario
A customer wants the cluster ready before a major research deadline.
Explain how you would plan the phases to minimize risk and delays.
Write at least 150 words.
Revision
Review Week 2 Day 1 content following the Day+1 rule.
DAY 2 — Site Readiness (Power, Cooling, Space, Network)
Daily Goal: Learn the physical infrastructure requirements of an AI/HPC cluster.
Study Content
Power capacity per rack and A/B power feeds
Cooling: air-cooled vs. liquid-cooled requirements
Floor space, rack placement, and aisle layouts
Network readiness: management network, storage network, fabric network
WAN or cloud connectivity for GreenLake monitoring
Tasks
Task 1: Create a site readiness checklist
At least 25 checklist items covering power, cooling, cabling pathways, rack constraints, safety requirements, network addressing, and WAN prerequisites.
Task 2: Draw a rack placement plan
Include cold aisle, hot aisle, airflow direction, PDU placement, and future expansion racks.
Task 3: Write a 300-word explanation on cooling requirements
Explain the difference between air and liquid cooling, why liquid cooling is essential for dense systems like Cray EX, and how temperature affects performance.
Task 4: Mini scenario
The data center has limited cooling capacity.
Explain how you redesign the deployment or scaling strategy to fit the environment.
Revision
Review Week 2 Day 2 content.
DAY 3 — Physical Installation (Racking, Cabling, Power Integration)
Daily Goal: Learn how the physical infrastructure is built.
Study Content
Rack installation and stabilization
Cable types: fabric cables, management cables, storage network cables
Labeling and documentation standards
Power distribution units (PDUs) and load balancing
Liquid cooling connections for Cray EX
Tasks
Task 1: Draw a complete cabling diagram
Showing management network, compute fabric, and storage network cabling.
Include port labels and cable identifiers.
Task 2: Write a racking procedure document
At least 20 steps describing: unboxing, rail installation, server mounting, cabling order, safety checks, and alignment.
Task 3: Create a power distribution plan
Specify PDU connections, balancing across phases, estimating power draw per rack, and risk mitigation.
Task 4: Mini scenario
You discover cabling mistakes after the cluster is partially installed.
Write how you would diagnose, document, and correct the issue.
Revision
Review Week 2 Day 3 content.
DAY 4 — System Configuration and Provisioning
Daily Goal: Learn BIOS configuration, OS provisioning, and storage configuration.
Study Content
Firmware and BIOS updates
NUMA settings, memory interleaving, PCIe configuration, CPU power modes
OS image creation and provisioning through HPCM or Cray System Management
Network configuration and IP addressing
Parallel file system creation and enterprise storage provisioning
Tasks
Task 1: Create a BIOS tuning guide
Include optimal BIOS settings for HPC and AI workloads, such as disabling deep C-states, adjusting NUMA, enabling high-performance power mode, and PCIe tuning.
Task 2: Write an OS provisioning workflow
From creating an OS image to applying it across hundreds of nodes.
Your workflow should include at least 15 steps.
Task 3: Draw a storage configuration flow diagram
Showing MDS, OSS, OSTs, mount points, LUN allocation, and access permissions.
Task 4: Mini scenario
A set of nodes fails provisioning.
Describe possible causes and steps to diagnose and fix the issue.
Revision
Review Week 2 Day 4 content.
DAY 5 — Software Deployment (Scheduler, AI Stack, Monitoring)
Daily Goal: Understand the logical layer that makes the cluster operational.
Study Content
Slurm controller, compute daemons, accounting database
Queue definitions and resource quotas
AI frameworks installation (CUDA, PyTorch, TensorFlow, NCCL)
Module system or container integration
Monitoring tools for health, utilization, and logs
Tasks
Task 1: Write a Slurm installation and configuration document
Cover controller installation, daemon configuration, accounting setup, and partition creation.
Task 2: Create a GPU-enabled Slurm job example
Write a job script using multiple GPUs, including appropriate Slurm directives.
Task 3: Build an AI framework installation checklist
List dependencies, driver version requirements, CUDA versions, NCCL versions, and test commands.
Task 4: Mini scenario
Users report that distributed training jobs hang.
Explain possible causes from scheduler, network, and driver layers.
Revision
Review Week 2 Day 5 content.
DAYS 6 AND 7 — Week 3 Consolidation
Daily Goal: Transform implementation knowledge into a step-by-step deployment capability.
Tasks
Task 1: Produce a complete implementation workflow
At least six pages covering:
Planning, Site readiness, Racking, Provisioning, Scheduler setup, AI stack deployment, Monitoring, Validation, Go-live procedures.
Task 2: Draw a full implementation flow diagram
Showing the structural order from hardware delivery to production readiness.
Task 3: Write and answer 15 self-test questions
Focus on:
BIOS tuning
Cabling
Provisioning issues
Scheduler setup
Storage configuration
Validation steps
Task 4: Teach-back activity
Explain the entire implementation lifecycle verbally for at least 10 minutes.
Revision
Review Day 1, Day 2, and Day 3 content following the Day+7 rule.
WEEK 4 — DEMONSTRATING AI SOLUTIONS
Focus of Week 4: Learn to build complete AI demonstration scenarios, moving from business requirements to data pipelines, training workflows, deployment, monitoring, and value articulation.
Weekly Learning Outcomes
By the end of Week 4, you should be able to:
Translate business goals and constraints into AI technical requirements.
Build demonstration architectures using HPE reference designs.
Construct end-to-end AI pipelines covering data ingestion, preprocessing, training, and inference.
Integrate lifecycle management and MLOps practices into demonstrations.
Present value and ROI using technical-to-business mapping.
Produce a complete demonstration scenario document.
WEEK 4 STRUCTURE
Daily workload: 5 to 8 Pomodoros
Each day includes study content, detailed tasks, and scheduled revision following the Ebbinghaus forgetting curve.
DAY 1 — From Business Problem to Technical AI Requirements
Daily Goal: Learn how to interpret business needs and convert them into technical goals.
Study Content
Business KPIs such as throughput, latency, accuracy, cost reduction, and time-to-insight.
Constraints such as regulations, privacy, and data locality.
Mapping KPIs to AI use cases (for example: predictive maintenance, anomaly detection, recommendation systems, forecasting).
Identifying hardware and software technical requirements derived from business objectives.
Tasks
Task 1: Write a business-to-technical translation guide
A two-page document describing how to interpret business KPIs and convert them into training/inference requirements, performance metrics, and resource needs.
Task 2: Build three business problem examples
For each example, include:
Business description
Business KPIs
Data constraints
Technical AI requirements
Initial architectural implications
Task 3: Create a requirement mapping table
Columns: business KPI, data requirement, technical AI need, hardware implication, software implication.
Fill at least 12 complete rows.
Task 4: Mini scenario
The company wants to reduce defect detection time in manufacturing from hours to minutes.
Write a 200-word explanation of how you would translate this into technical requirements.
Revision
Review Week 3 Day 1 content.
DAY 2 — Building Demonstration Architectures
Daily Goal: Learn how to design small-scale but realistic demonstration environments using HPE reference designs.
Study Content
HPE Reference Architectures for AI on Cray EX/XD and Apollo GPU nodes.
GreenLake hybrid architectures combining on-prem compute with service-based operations.
Building small demonstration clusters (one to four GPU nodes).
Key design principles for demo systems: representativeness, scalability, cost efficiency, and clarity.
Tasks
Task 1: Create a demonstration architecture diagram
Include compute nodes, storage, network, AI framework layers, and data flows.
The diagram should reflect real-world HPE practices but at small scale.
Task 2: Write a demonstration architecture explanation
Explain each component of your architecture and why it fits the demonstration goals.
Minimum 300 words.
Task 3: Build a reference design comparison table
Compare Cray-based vs Apollo-based demonstration architectures across at least eight criteria: performance, scalability, cooling, cost, support tools, and suitability for various AI workloads.
Task 4: Mini scenario
A customer wants to experiment with hybrid training across on-prem and cloud-like environments.
Design a GreenLake-based demonstration architecture and justify your design in 150 to 200 words.
Revision
Review Week 3 Day 2 content.
DAY 3 — End-to-End AI Pipeline Design for Demonstrations
Daily Goal: Learn to demonstrate a full AI pipeline from data ingestion to model deployment.
Study Content
Data ingestion patterns (batch, streaming, file-based, object storage).
Data preprocessing and feature engineering workflows on HPC/AI systems.
Distributed training: single GPU, multi-GPU, and multi-node considerations.
Inference patterns: batch inference, microservice inference, and edge inference.
Tasks
Task 1: Draw a complete AI pipeline diagram
Include: data source, ingestion, preprocessing, storage, training, validation, deployment, monitoring.
Task 2: Write a data pipeline explanation (300 to 400 words)
Describe how data moves from source to training-ready format.
Include discussions on parallel preprocessing and use of high-performance storage.
Task 3: Build a multi-stage training workflow
Document how training scales from single GPU to multiple GPUs and then to multi-node.
Include expected bottlenecks and required configuration adjustments.
Task 4: Mini scenario
You need to demonstrate image classification training and inference.
Describe your training pipeline and inference pipeline design in 150 to 200 words.
Revision
Review Week 3 Day 3 content and Week 3 Day 1 content (Day+7 cycle).
DAY 4 — MLOps and Lifecycle Management
Daily Goal: Learn to demonstrate operational maturity, model lifecycle, and governance.
Study Content
Dataset versioning and metadata management.
Model versioning and experiment tracking tools.
Automated retraining triggers and CI/CD pipelines for AI.
Monitoring model performance and detecting drift.
Access control, audit logs, and multi-tenancy considerations.
Tasks
Task 1: Create a model lifecycle diagram
Include dataset creation, training, validation, versioning, deployment, monitoring, retraining triggers.
Task 2: Write a 300-word document on AI governance
Explain why governance, versioning, access control, and audit logging are essential for enterprise AI.
Task 3: Build an MLOps tool comparison
Compare three MLOps tools or processes: experiment tracking, dataset versioning tools, and deployment workflows.
Task 4: Mini scenario
A model’s accuracy drops two months after deployment.
Explain the potential causes and how to respond using MLOps mechanisms.
Revision
Review Week 3 Day 4 content.
DAY 5 — Demonstrating Business Value and ROI
Daily Goal: Learn how to articulate technical improvements in business terms.
Study Content
Mapping technical performance gains to business outcomes.
TCO calculations and cost comparisons.
Before-and-after scenario modeling.
Scalability and future-proofing arguments.
Tasks
Task 1: Write a technical-to-business translation document
Explain how to convert performance gains such as faster training or higher throughput into measurable business value.
Minimum 300 words.
Task 2: Create a TCO comparison example
Compare a legacy system versus a modern HPE solution.
Include power, cooling, maintenance, performance, and usage efficiency.
Task 3: Write three before-and-after scenarios
Include:
Time-to-insight
Productivity improvements
Cost improvements
Task 4: Mini scenario
A customer is unsure whether AI investment is financially justified.
Write a 200-word explanation showing ROI based on technical evidence.
Revision
Review Week 3 Day 5 content.
DAYS 6 AND 7 — Week 4 Consolidation
Daily Goal: Combine all demonstration-related knowledge into a coherent, professional-level narrative.
Tasks
Task 1: Create a complete demonstration scenario document
At least six pages including business requirements, architecture, pipeline, lifecycle, and value demonstration.
Task 2: Build an end-to-end demonstration diagram
From business problem to final inference and monitoring.
Task 3: Write and answer 15 self-test questions
Cover all topics from Week 4: business translation, pipeline design, MLOps, and value articulation.
Task 4: Teach-back activity
Explain your entire demonstration scenario verbally for 10 minutes.
Focus on clarity and business alignment.
Revision
Review Day 1 and Day 2 content again following the Day+7 rule.
WEEK 5 — INTEGRATED CONSOLIDATION AND SYSTEM THINKING
Focus of Week 5: Strengthen deep understanding and integrate all knowledge areas into a coherent, system-level perspective.
This week transitions from learning individual components to mastering full-stack reasoning, cross-domain relationships, and architectural design logic.
Weekly Learning Outcomes
By the end of Week 5, you should be able to:
Connect compute, storage, network, scheduler, and AI pipeline knowledge into unified mental models.
Explain how design decisions propagate across the system.
Identify bottlenecks and limitations based on architectural understanding.
Communicate architecture reasoning clearly and accurately.
Demonstrate full knowledge retention through structured revision using the forgetting curve.
Produce a complete learning summary covering all four major knowledge points.
WEEK 5 STRUCTURE
Daily workload: 6 to 10 Pomodoros (review-heavy week).
Daily schedule includes:
System-level study
Cross-topic tasks
Integration exercises
Revision cycles using the Ebbinghaus forgetting curve
Self-testing and scenario reasoning
DAY 1 — System-Level Architecture Integration
Daily Goal: Combine all four Week-1 architectural domains (compute, storage, interconnect, software stack).
Study Content
Review of compute families (Cray EX/XD, Apollo, ProLiant).
Review of storage architectures (Parallel FS, enterprise storage, object storage).
Review of Slingshot, InfiniBand, Ethernet interconnects.
Review of software stack: OS, drivers, Slurm, AI frameworks.
Tasks
Task 1: Create an integrated system map
Combine compute, storage, fabric, and software stack into a single architecture diagram.
The diagram must reflect node types, storage tiers, network fabric hierarchy, and software layers.
Task 2: Write a two-page explanation
Describe the entire architecture from bottom to top, clearly explaining interdependencies between components.
Task 3: Build a cross-domain dependency table
Columns: Component, Dependent Component, Nature of Dependency, Impact if Misconfigured.
Fill at least 15 rows.
Task 4: Mini scenario
A customer chooses weaker PCIe-based GPU topology instead of NVLink/NVSwitch.
Write 200 words explaining the system-wide consequences.
Revision
Review Week 4 Day 1 and Week 3 Day 1 content.
DAY 2 — Solution Design Integration
Daily Goal: Strengthen logical reasoning for designing complete HPE AI/HPC solutions.
Study Content
Review workload characterization.
Review compute sizing.
Review storage tiering and metadata considerations.
Review network topology selection.
Review scheduler partition design.
Tasks
Task 1: Create a complete solution design template
Sections must include:
Workload characterization
Compute design
Storage design
Network design
Scheduler design
AI stack strategy
Task 2: Design two different architectures
Design one for an HPC-dominant workload.
Design one for an AI-training-dominant workload.
Each should be at least two pages.
Task 3: Write a 300-word analysis
Compare the two architectures and explain how workload characteristics influenced every major decision.
Task 4: Mini scenario
You must design a solution supporting both multi-node AI training and heavy metadata workloads.
Explain the storage design in 200 words.
Revision
Review Week 4 Day 2 and Week 3 Day 2 content.
DAY 3 — Implementation Process Integration
Daily Goal: Understand the entire lifecycle from physical deployment to operational readiness.
Study Content
Review implementation phases.
Review site readiness.
Review racking, cabling, and power integration.
Review BIOS and OS provisioning.
Review scheduler and AI frameworks deployment.
Tasks
Task 1: Build a full deployment playbook
At least six pages.
Include planning, readiness checks, racking, provisioning, scheduler setup, monitoring, and validation steps.
Task 2: Create a failure-mode analysis table
Columns: Implementation Phase, Possible Failure, Root Cause, Detection Method, Resolution Steps.
Include 20 failure scenarios.
Task 3: Write a provisioning troubleshooting guide
Explain firmware issues, network boot issues, SSH connectivity failures, image mismatch issues, and storage mount failures.
Task 4: Mini scenario
During validation, storage performance is 50 percent below expected.
Describe possible root causes and diagnostic steps.
Revision
Review Week 4 Day 3 and Week 3 Day 3 content.
DAY 4 — AI Demonstration and MLOps Integration
Daily Goal: Understand end-to-end AI pipeline demonstration and integrate MLOps concepts.
Study Content
Business-to-technical translation.
Demonstration architecture design.
AI pipeline design.
MLOps lifecycle and governance.
ROI and value articulation.
Tasks
Task 1: Create a complete demonstration pipeline blueprint
Include: business inputs, technical requirements, architecture, data pipeline, training workflow, deployment workflow, monitoring, and governance.
Task 2: Write a 300-word document
Explain how MLOps contributes to long-term reliability of AI systems and why it matters in enterprise environments.
Task 3: Build a demonstration architecture evaluation table
Compare two demonstration architectures in terms of clarity, maintainability, scalability, performance, and training-to-inference consistency.
Task 4: Mini scenario
A customer wants an AI demonstration that must include model retraining triggered by new data.
Propose a design and justify it.
Revision
Review Week 4 Day 4 and Week 3 Day 4 content.
DAY 5 — Exam-Oriented Consolidated Practice
Daily Goal: Reinforce cross-domain reasoning and prepare for exam-level thinking.
Study Content
Mixed architecture review.
Mixed solution design reasoning.
Mixed implementation process reasoning.
Mixed demonstration pipeline reasoning.
Tasks
Task 1: Write 20 exam-style questions and answer them
Questions should combine multiple topics, such as:
Compute plus fabric impact
Storage plus workload performance
Scheduler plus GPU utilization
MLOps plus business goals
Each answer must be at least 80 words.
Task 2: Create a five-page consolidated study summary
Include the most important concepts from all four major knowledge points.
Task 3: Perform a system bottleneck analysis
Choose any architecture you created earlier and identify bottlenecks in compute, storage, fabric, or scheduling.
Explain each bottleneck and propose fixes.
Task 4: Mini scenario
A cluster performs well in HPC workloads but poorly in AI training.
Explain 5 possible architectural causes.
Revision
Review Week 4 Day 5 and Week 3 Day 5 content.
DAYS 6 AND 7 — Week 5 Deep Integration and Mastery
Daily Goal: Convert knowledge from fragmented topics into complete understanding.
Tasks
Task 1: Create a final Week 5 integrated architecture diagram
This diagram must capture compute, storage, fabric, scheduler, pipeline, and governance in one unified view.
Task 2: Write an eight-page complete integration document
Explain system relationships, design decisions, implementation flow, and demonstration logic in a unified narrative.
Task 3: Conduct a full self-assessment
Ask yourself 30 comprehension questions across compute, storage, network, scheduler, implementation, and AI demonstration.
Answer all questions in writing.
Task 4: Teach-back session
Teach the entire HPE AI/HPC solution life cycle aloud for 15 minutes.
This is a key memory reinforcement step.
Revision
Review Week 2 and Week 3 summaries following the long-term forgetting curve.
WEEK 6 — FINAL EXAM PREPARATION AND CAPSTONE PRACTICE
Focus of Week 6: Strengthen long-term retention, reinforce cross-domain reasoning, practice exam-level thinking, and validate readiness through comprehensive synthesis tasks.
Weekly Learning Outcomes
By the end of Week 6, you should be able to:
Demonstrate mastery of all four major knowledge domains (architecture, design, implementation, demonstration).
Answer exam-style scenario questions with full reasoning.
Explain system-level interactions across compute, storage, network, and software stack.
Articulate AI pipeline design, lifecycle processes, and business value clearly.
Produce an end-to-end AI/HPC solution description independently.
Confirm readiness for the real HPE7-S01 exam.
WEEK 6 STRUCTURE
Daily workload: 6 to 10 Pomodoros
Each day includes focused integration tasks, scenario practice, and targeted revision following the long-term forgetting curve.
DAY 1 — Comprehensive Knowledge Review
Daily Goal: Revisit and reinforce all four major domains using structured review.
Study Content
Architecture review: compute, storage, interconnect, software.
Solution design review: sizing and topology selection.
Implementation review: provisioning and cluster preparation.
AI demonstration review: pipeline and MLOps.
Tasks
Task 1: Create a complete topic outline
Write a detailed outline covering every major concept learned across Weeks 1–5.
Aim for three to four pages.
Task 2: Build a high-level architectural summary
Describe compute, storage, interconnect, and software layers in one unified technical narrative of at least 600 words.
Task 3: Create a consolidated comparison table
Rows should include at least 20 components or concepts.
Columns should include: function, category, strengths, limitations, and scenario fit.
Task 4: Mini scenario
You must review an existing AI/HPC cluster’s design.
Write 200 words describing the questions you would ask to evaluate architecture fitness.
Revision
Review Week 5 Day 1 and Week 4 Day 1 content.
DAY 2 — Cross-Domain Examination Scenarios
Daily Goal: Practice scenario-based reasoning that mirrors the exam format.
Study Content
Multidimensional reasoning: compute plus network plus storage.
Architectural trade-off analysis.
Performance bottleneck identification.
Practical constraints: power, cooling, data locality.
Tasks
Task 1: Solve 10 cross-domain architectural scenarios
Each scenario should involve decisions across at least two domains.
Write a minimum of 120 words per scenario.
Task 2: Create a performance bottleneck catalog
List at least 15 bottlenecks related to compute, network, storage, or scheduler.
Describe root causes and mitigation strategies.
Task 3: Write a 300-word analysis
Explain how poor network design affects both HPC and AI workloads differently.
Task 4: Mini scenario
A customer wants to scale their training from 8 GPUs to 64 GPUs.
Describe what architectural areas must be re-evaluated.
Write at least 200 words.
Revision
Review Week 5 Day 2 and Week 4 Day 2 content.
DAY 3 — Full Solution Design Synthesis
Daily Goal: Practice creating complete, end-to-end solution designs under exam-like conditions.
Study Content
Workload-driven compute selection.
Storage tiering and performance requirements.
Interconnect fabric selection and topology design.
Scheduler partition and policy definitions.
AI framework, container, and module strategy.
Tasks
Task 1: Build a complete solution design for an AI-heavy environment
Must include workload analysis, compute sizing, storage architecture, network design, scheduler plan, and AI stack strategy.
Your design should be at least four pages.
Task 2: Build a complete solution design for an HPC-heavy environment
Include the same sections as above but tailored to HPC workloads.
Also at least four pages.
Task 3: Write a comparison of the two designs
Explain how workload characteristics changed your decisions.
At least 300 words.
Task 4: Mini scenario
A research institution has 10 different departments and needs multi-tenancy.
Design a scheduler and storage access policy.
Write at least 200 words.
Revision
Review Week 5 Day 3 and Week 4 Day 3 content.
DAY 4 — Implementation and Startup Synthesis
Daily Goal: Demonstrate full understanding of the deployment and operationalization process.
Study Content
Implementation phases from planning to go-live.
Failure modes and diagnostic strategies.
BIOS tuning and OS provisioning.
Scheduler deployment and monitoring stack integration.
Tasks
Task 1: Write a complete implementation plan
Include planning, site readiness, racking, cabling, provisioning, scheduler setup, GPU driver installation, monitoring, and validation.
At least five to six pages.
Task 2: Build a troubleshooting matrix
Include at least 20 failure situations across compute, network, storage, provisioning, and scheduler.
Write root causes and recommended actions.
Task 3: Create a validation and benchmarking checklist
Include storage throughput tests, fabric latency testing, Slurm job submission tests, and GPU functionality tests.
Task 4: Mini scenario
During acceptance tests, multi-node training fails unpredictably.
Write 200 words explaining your diagnostic approach.
Revision
Review Week 5 Day 4 and Week 4 Day 4 content.
DAY 5 — AI Demonstration and Business Value Synthesis
Daily Goal: Practice full-cycle AI demonstration reasoning including business justification.
Study Content
Translating business KPIs to technical requirements.
Designing demonstration architectures and pipelines.
Building MLOps workflows for retraining and governance.
Creating ROI and before-after models.
Tasks
Task 1: Create a full AI demonstration document
Must include business requirements, architecture, data pipeline, training flow, inference deployment, monitoring, governance, and value proposition.
Minimum five pages.
Task 2: Build a business value mapping table
Columns: technical improvement, business KPI affected, measurable business impact.
Task 3: Write three ROI justification examples
Each example must show a clear connection from technical metrics to business improvements.
Task 4: Mini scenario
A customer wants to justify AI investment to executives.
Write a 250-word narrative showing business value and ROI.
Revision
Review Week 5 Day 5 and Week 4 Day 5 content.
DAYS 6 AND 7 — Final Mastery and Readiness Evaluation
Daily Goal: Validate your complete knowledge and determine whether you are ready for the HPE7-S01 exam.
Tasks
Task 1: Create a comprehensive eight-page exam study guide
Cover compute, storage, network, scheduler, implementation, AI pipeline, MLOps, and demonstration strategies.
Task 2: Perform a self-administered mock exam
Write 30 exam-style questions that require:
Architectural reasoning
Design decisions
Implementation and troubleshooting
AI pipeline and business translation
Answer all 30 questions in writing.
Task 3: Build a final architecture diagram
Include all layers (compute, storage, network, scheduler, AI stack, pipeline, MLOps).
This is your final integrated mental model.
Task 4: Final teach-back
Explain the entire HPE AI/HPC solution lifecycle for 20 minutes.
Focus on clarity, accuracy, and system-level explanation.
Final Revision
Review Weeks 1–5 summaries using the long-term forgetting curve.