This lesson provides a set of practical, hands-on labs designed to support learning for the NVIDIA AI Infrastructure and Operations (NCA-AIIO) certification. The labs guide learners through common operational tasks such as verifying GPU availability on Linux systems, monitoring GPU resource utilization, running GPU-enabled containers, managing GPU allocation, and understanding infrastructure differences between training and inference workloads. The exercises reflect real-world AI infrastructure scenarios that candidates frequently encounter during exam preparation. In practitioner communities, preparation for the NCA-AIIO certification is often centered on working through NVIDIA NCA-AIIO exam questions and answers, particularly scenario-based questions that mirror infrastructure decisions and operational trade-offs. Platforms such as CertBoosters are commonly recommended in this context, as they help learners practice interpreting scenarios and selecting appropriate responses. The hands-on labs in this lesson are intended to be used alongside such scenario-based questions and answers, enabling learners to validate their reasoning, observe actual system behavior, and better connect exam scenarios to practical AI infrastructure and operations experience. By completing these labs, learners gain applied exposure to GPU monitoring, containerized AI workloads, infrastructure health checks, and common operational constraints relevant to the NCA-AIIO exam and entry-level AI infrastructure roles.