NVIDIA-Certified Associate -AI Infrastructure and Operations (NCA-AIIO) Cheat Sheet: Key Concepts, Acronyms, and Commands

The NVIDIA Certified Associate - AI Infrastructure and Operations (NCA-AIIO) exam validates your ability to manage AI workloads using NVIDIA's cutting-edge technologies. This cheat sheet is designed to help you quickly revise core concepts, acronyms, and commands essential for the exam, ensuring you're well-prepared for test day.

Core Concepts You Must Know

Understanding these core concepts is crucial for the NCA-AIIO exam:

• AI, ML, and DL Basics – Understand the differences between Artificial Intelligence, Machine Learning, and Deep Learning.

• Common AI Workloads – Recognize typical workloads like computer vision, NLP, recommendation systems, and large language models (LLMs).

• GPU vs. CPU Architecture – Know how GPUs are optimized for parallel computing and AI workloads.

• NVIDIA GPU Architecture – Familiarity with cores (CUDA, Tensor), memory hierarchy, and multi-GPU interconnects (NVLink).

• Compute Acceleration – Understand how GPUs accelerate training and inference across different AI frameworks.

• Containerization – Basics of Docker and Kubernetes for running AI/ML workloads in containers.

• Virtualization and Bare Metal – Know the difference between running on VMs, containers, and physical servers.

• Model Lifecycle – Stages of training, validation, inference, and monitoring for AI models.

• Inference vs. Training – Distinguish between training a model and deploying it for inference.

• Multi-GPU and Multi-Node Scaling – Concepts like data parallelism and model parallelism for large-scale training.

• Data Center Infrastructure – Basics of networking, storage, and cooling relevant to GPU clusters.

• Monitoring and Telemetry – Importance of tracking GPU utilization, temperature, power, and failures.

• Security and Isolation – Role of DPUs, secure boot, and multi-tenant isolation in AI infrastructure.

• NVIDIA’s AI Stack – Understand how tools like NGC, Triton, RAPIDS, and DOCA fit together.

• Edge vs. Cloud vs. On-Prem – Deployment options for AI workloads and their tradeoffs.

Acronyms and What They Mean

Acronyms are often used in the NCA-AIIO exam. Here's a quick guide:

• AI – Artificial Intelligence

• ML – Machine Learning

• DL – Deep Learning

• GPU – Graphics Processing Unit

• CPU – Central Processing Unit

• DPU – Data Processing Unit

• MIG – Multi-Instance GPU

• NGC – NVIDIA GPU Cloud

• DCGM – Data Center GPU Manager

• CLI – Command Line Interface

• SDK – Software Development Kit

• API – Application Programming Interface

• VM – Virtual Machine

• K8s – Kubernetes

• DOCA – Data Center Infrastructure SDK

• TF – TensorFlow

• PT – PyTorch

• ONNX – Open Neural Network Exchange

• HPC – High-Performance Computing

• IO – Input/Output

• NVLink – NVIDIA High-Speed GPU Interconnect

• SLI – Scalable Link Interface

• DGX – NVIDIA’s AI Supercomputing System

• RMM – Remote Monitoring and Management

• FP16/FP32 – Floating Point Precision Formats (Half/Single Precision)

Key NVIDIA Software Tools

Familiarize yourself with these essential NVIDIA software tools:

• NVIDIA GPU Operator – Automates GPU driver and software stack deployment in Kubernetes clusters.

• NVIDIA Container Toolkit (nvidia-docker) – Enables GPU access within containerized workloads.

• NVIDIA NGC (NVIDIA GPU Cloud) – Hosts pre-trained models, containers, SDKs, and Helm charts.

• NVIDIA Triton Inference Server – Serves AI models at scale using multiple frameworks and protocols.

• NVIDIA DeepStream SDK – Powers real-time video analytics on the edge or in the data center.

• NVIDIA Clara – AI and HPC toolkit for healthcare applications like imaging and genomics.

• NVIDIA DOCA – SDK for programming BlueField DPUs to offload networking and security tasks.

• NVIDIA Magnum IO – High-speed IO stack for multi-GPU, multi-node data movement.

• NVIDIA RAPIDS – Accelerates data science workflows with GPU-optimized Python libraries.

• NVIDIA DCGM (Data Center GPU Manager) – Monitors and manages GPU health and diagnostics.

• NVIDIA Nsight Systems & Nsight Compute – Developer tools for performance profiling and analysis.

• NVIDIA Base Command Platform – End-to-end platform for training and managing AI workloads on DGX.

• NVIDIA cuDNN / cuBLAS / cuDF / cuGraph / cuML – GPU-accelerated libraries for DL, ML, and data processing.

• NVIDIA Fabric Manager – Manages NVLink and NVSwitch topologies in multi-GPU systems.

• NVIDIA AI Enterprise – Licensed suite for enterprise-grade AI deployment and support.

Basic Linux & CLI Commands

Command-line proficiency is vital. Here are some basic commands to know:

• ls – List directory contents.

• cd – Change the current directory.

• pwd – Print the current working directory.

• mkdir – Create a new directory.

• rm – Remove files or directories.

• cp – Copy files or directories.

• mv – Move or rename files or directories.

• touch – Create an empty file or update file timestamps.

• cat – View the contents of a file.

• less – View large files one screen at a time.

• grep – Search text using patterns.

• top – Monitor running processes and system resource usage.

• ps – View active processes.

• kill – Terminate a process by its PID.

• df -h – Display available disk space in a human-readable format.

• free -m – Show memory usage in megabytes.

• nvidia-smi – Show GPU status, utilization, memory, temperature, and running processes.

• docker ps – List running Docker containers.

• docker run – Start a new Docker container.

• kubectl get pods – List Kubernetes pods in the current namespace.

• kubectl describe pod [name] – Get detailed information about a specific pod.

• chmod – Change file or directory permissions.

• chown – Change file or directory ownership.

• sudo – Run a command with superuser privileges.

Metrics & Monitoring

Monitoring system performance is crucial for AI operations:

• GPU Utilization (%) – Measures how much of the GPU's compute capacity is being used.

• Memory Utilization (%) – Shows how much GPU memory is actively being used by workloads.

• GPU Temperature (°C) – Indicates thermal status; excessive heat can trigger throttling or shutdown.

• Power Consumption (Watts) – Displays real-time energy use by the GPU.

• Fan Speed (%) – Shows how fast the GPU fan is running; relates to cooling efficiency.

• ECC Error Counts – Reports memory integrity errors detected and corrected on the GPU.

• GPU Clock and Memory Clock – Monitor the operating frequencies of GPU cores and memory.

• Process List – Displays which processes are currently using the GPU (via nvidia-smi).

• Driver Version – Ensures compatibility with GPU and software stack.

• GPU Health Status – Summary of hardware diagnostics and operational flags.

• DCGM Metrics – NVIDIA Data Center GPU Manager provides metrics like GPU errors, power, utilization, and thermals over time.

• Node Resource Usage – CPU, RAM, and disk metrics for the node hosting the GPU.

• Kubernetes Pod GPU Usage – Resource consumption by pods using GPUs.

• Prometheus/Grafana Dashboards – Visualization of real-time and historical GPU performance metrics.

• Alerts and Thresholds – Monitoring systems trigger alerts when metrics exceed acceptable ranges.

Bonus Tips for Test Day

Here are some last-minute tips to ensure success on test day:

• Review Key Concepts: Focus on understanding rather than memorization.

• Practice with Mock Tests: Simulate the exam environment to build confidence.

Pro Tip: Pay special attention to NVIDIA's software tools and their applications in real-world AI scenarios.

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