AI Infrastructure and Operations

 Essential AI Knowledge – Study Guide

Exam Weight: 38%

 

 

1.1 Describe the NVIDIA software stack used in an AI environment

🔹 Key Components:

• CUDA: Parallel computing platform enabling GPU acceleration.
• cuDNN: Deep Neural Network library for optimized primitives (e.g., convolutions).
• TensorRT: High-performance inference optimizer for deep learning.
• NVIDIA Triton Inference Server: Serves AI models for inference with support for multiple frameworks (TensorFlow, PyTorch, ONNX).
• NVIDIA RAPIDS: Suite of data science and analytics libraries using CUDA for acceleration.
• NVIDIA AI Enterprise: End-to-end AI and data analytics software suite certified for VMware, Red Hat, etc.
• NGC Catalog: Registry of pre-trained models, SDKs, containers, and Helm charts.

🛠️ Practical Example:

Train with PyTorch using CUDA backend → Optimize model with TensorRT → Deploy via Triton.

1.2 Compare and contrast training and inference architecture requirements and considerations

 Training:

• Hardware Needs: More compute, memory, multi-GPU support (e.g., NVIDIA A100).
• Precision: Often uses FP32 or mixed precision (FP16+FP32).
• Duration: Time-consuming and resource-intensive.
• Frameworks: TensorFlow, PyTorch, JAX.

 Inference:

• Hardware: Can run on edge devices (Jetson) or optimized servers (T4, L4).
• Precision: INT8 or FP16 for performance.
• Goal: Low latency, high throughput.
• Tools: TensorRT, Triton, DeepStream (video).

1.3 Differentiate the concepts of AI, machine learning, and deep learning

Concept	Description	Example
AI (Artificial Intelligence)	Simulating intelligent behavior in machines	Chatbots, recommendation engines
ML (Machine Learning)	Subset of AI where machines learn from data	Spam filters, fraud detection
DL (Deep Learning)	Subset of ML using neural networks with many layers	Image recognition, speech synthesis

Mnemonic: AI ⊇ ML ⊇ DL

 

1.4 Explain the factors contributing to recent rapid improvements and adoption of AI

Key Drivers:

• Hardware Advances: GPUs (NVIDIA A100, H100), Tensor Cores, NPUs.
• Big Data Availability: Massive labeled/unlabeled datasets.
• Open Source Ecosystem: PyTorch, TensorFlow, Hugging Face, ONNX.
• Cloud AI: On-demand access to powerful infrastructure (AWS, GCP, Azure).
• NVIDIA Ecosystem: End-to-end tools and pretrained models accelerate dev time.

 

1.5 Explain the key AI use cases and industries

Industries:

• Healthcare: Diagnostics, drug discovery (e.g., Clara)
• Finance: Fraud detection, algorithmic trading
• Retail: Personalized ads, inventory optimization
• Manufacturing: Predictive maintenance, defect detection
• Transportation: Autonomous vehicles (NVIDIA Drive)

Use Cases:

• Natural Language Processing (NLP)
• Computer Vision
• Speech Recognition
• Recommender Systems
• Generative AI (e.g., ChatGPT, Stable Diffusion)

1.6 Explain the purpose and use case of various NVIDIA solutions

NVIDIA Solution	Use Case
NVIDIA Jetson	Edge AI, robotics
NVIDIA Drive	Autonomous vehicles
NVIDIA Clara	AI in healthcare/medical imaging
NVIDIA Metropolis	Smart cities, video analytics
NVIDIA Omniverse	3D collaboration and digital twins
NVIDIA DGX Systems	AI supercomputers for training
NVIDIA cuOpt	Route and logistics optimization
Triton Inference Server	Model deployment and serving

1.7 Describe the software components related to the life cycle of AI development and deployment

AI Development Lifecycle:

1. Data Collection & Labeling: Tools: NVIDIA TAO Toolkit, CVAT
2. Model Training: CUDA, cuDNN, PyTorch/TensorFlow, NVIDIA DGX
3. Optimization: TensorRT, Quantization (FP16/INT8)
4. Deployment: Triton Inference Server, Jetson
5. Monitoring & Feedback: NVIDIA AI Enterprise (with MLOps tools)

Tip: AI dev cycle is iterative — improvements are constant via feedback.

1.8 Compare and contrast GPU and CPU architectures

Feature	CPU	GPU
Core Count	Few powerful cores	Thousands of simple cores
Parallelism	Good at sequential tasks	Excellent at parallel tasks
Use Case	General-purpose computing	Matrix operations, ML/DL workloads
Throughput	Low for AI tasks	High throughput for AI/ML
Examples	Intel i9, AMD Ryzen	NVIDIA A100, RTX 4090

📌 Takeaway: CPUs are brains; GPUs are muscles in AI workloads.

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✅ Pro Tips for the Exam

• Memorize key NVIDIA tools and match them to their use cases.
• Understand AI lifecycle stages and what NVIDIA products are used when.
• Be able to explain why GPUs accelerate AI compared to CPUs.
• Practice comparing INT8 vs FP16 vs FP32 and how inference vs training differ.
• Know the NVIDIA NGC Catalog — it’s a hub for models, containers, and more.

 

 AI Infrastructure – Study Guide

Exam Weight: 40%

 

2.1 Extracting Insights from Large Datasets

Techniques to Know:

• Data Mining: Uncover patterns from raw data (e.g., clustering, association rules).
• Data Visualization: Graphically represent trends/patterns (matplotlib, seaborn, Tableau).
• ETL Process: Extract, Transform, Load – essential for data prep.
• Dimensionality Reduction: PCA, t-SNE for high-dimensional datasets.
• Descriptive vs Inferential Analysis:
  • Descriptive: Summarize data (mean, median, mode).
  • Inferential: Draw conclusions beyond the data (e.g., hypothesis testing).

📌 Tool Stack:

• Python (pandas, numpy, matplotlib, scikit-learn)
• SQL
• NVIDIA RAPIDS (GPU-accelerated data science)

2.2 Comparing Models Using Statistical Metrics

Key Metrics:

Metric	Description
Loss Functions	Measure error (e.g., MSE, Cross-Entropy)
Accuracy / Precision / Recall / F1	Classification quality
Explained Variance	How much of the outcome variance is explained by the model
AUC-ROC / PR Curves	Evaluation under imbalance
R² (R-squared)	Goodness of fit for regression

Tip: Select metrics based on your model type (classification vs regression).

2.3 Conducting Data Analysis Under Supervision

Real-World Application:

• Junior analysts often:
  • Clean and prepare data.
  • Create draft reports or dashboards.
  • Follow analysis protocols set by senior staff.
• Key Skills:
  • Ask clarifying questions.
  • Version control with Git.
  • Maintain data lineage (understand source and transformations).
  • Use Jupyter Notebooks for transparency.

2.4 Creating Visualizations Using Specialized Software

Tools to Master:

• Python: matplotlib, seaborn, Plotly
• BI Tools: Power BI, Tableau, Google Data Studio
• GPU Tools: NVIDIA RAPIDS cuDF + cuGraph, Holoviews

Visualization Types:

• Histograms, bar charts, scatter plots
• Line graphs (trend analysis)
• Heatmaps (correlation matrix)
• Box plots (distribution analysis)

📌 Tip: Choose the right chart based on data type (categorical vs continuous).

 

2.5 Identifying Relationships and Trends

Techniques:

• Correlation Analysis (Pearson, Spearman)
• Time Series Analysis (rolling mean, trend decomposition)
• Outlier Detection (z-score, IQR)
• Regression Models (linear, logistic, multiple)
• Clustering (K-means, DBSCAN)

Interpretation Goals:

• Identify variables that predict outcomes.
• Spot anomalies or shifts.
• Explain causality (when possible).

Recommended Units (from Course)

Unit	Topic
Unit 4	Accelerating AI with GPUs
Unit 7.1	Data Center Platforms
Unit 7.4	NVIDIA DPUs & Transformation
Unit 8	Networking for AI
Unit 10 & 11	Energy-Efficient Computing
Unit 12.4	AI in the Cloud Considerations

📌 Key Terms to Know:

• DPU: Data Processing Unit (offloads networking/storage workloads)
• GPU Acceleration: Speeding up data processing & ML
• Cloud vs On-Prem Infrastructure
• Energy Efficiency: Thermal design, compute density, carbon cost

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✅ Pro Tips for the Exam

• Understand how GPU-powered infrastructure supports AI workloads.
• Practice interpreting loss curves, graphs, and model metrics.
• Use real-world data for mini projects (e.g., Kaggle datasets).
• Familiarize with cloud-native tools (e.g., containers, DPU offloading).
• Be ready to compare CPU vs GPU vs DPU roles in AI pipelines.

 

 AI Operations – Study Guide

Exam Weight: 22%

3.1 AI Data Center Management & Monitoring Essentials

Key Concepts:

• Telemetry: Collect real-time metrics (temperature, power usage, memory, compute load).
• Monitoring Tools:
  • NVIDIA DCGM (Data Center GPU Manager): GPU health, diagnostics, telemetry.
  • Prometheus + Grafana: Time-series monitoring dashboards.
  • Nagios / Zabbix: General infrastructure health tracking.

Best Practices:

• Automate alerts for failures (GPU overheat, memory errors).
• Track power consumption and cooling status.
• Segment logging by node or cluster.

3.2 Cluster Orchestration & Job Scheduling

Essential Components:

• Cluster Orchestration:
  • Kubernetes (with GPU support via nvidia-device-plugin)
  • SLURM (Simple Linux Utility for Resource Management)
• Job Scheduling:
  • Assigns resources based on availability and priority.
  • Supports queues, time limits, and preemption.

Concepts to Know:

• Pod vs Job vs Deployment (in Kubernetes)
• Resource quotas, node affinity, GPU allocation policies
• Multi-tenancy and fair usage enforcement

3.3 Monitoring GPU Metrics & Performance

 Key Metrics:

Metric	Description
GPU Utilization	% of time GPU is actively processing
Memory Usage	VRAM consumption
Power Draw	Measured in watts
Thermal Data	Temperature trends
ECC Errors	Memory integrity events

📌 Tools:

• nvidia-smi (CLI)
• DCGM CLI and DCGM Exporter
• Integrated cloud GPU dashboards (e.g., GCP, AWS)

3.4 Virtualizing Accelerated Infrastructure

 Key Concepts:

• GPU Virtualization Types:
  • vGPU (NVIDIA GRID): Multiple VMs share one physical GPU.
  • Passthrough (PCIe): One VM gets exclusive GPU access.
• Use Cases:
  • Enterprise VDI (Virtual Desktop Infrastructure)
  • Remote AI/ML environments
• Requirements:
  • Compatible hypervisor (e.g., VMware ESXi with vGPU manager)
  • NVIDIA vGPU Software License
  • GPU supporting virtualization (e.g., A40, A100)

 

 Recommended Units (Course Reference)

Unit	Topic
Unit 5	AI Software Ecosystem
Unit 8	Networking for AI
Unit 13	AI Data Center Management and Monitoring
Unit 14	Orchestration, MLOps, and Job Scheduling

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✅ Pro Tips for the Exam

• Memorize core tools like nvidia-smi, DCGM, and Kubernetes job lifecycle.
• Understand GPU metric thresholds (normal vs high temp, power).
• Know virtualization trade-offs: performance vs density.
• Link monitoring → orchestration → infrastructure for full-stack AI ops insight.