Amazon EC2 G5g Instances Test

This skill focuses on deploying workloads such as 3D rendering, video processing, game streaming, and remote desktop environments on EC2 G5g instances. It assesses knowledge of choosing appropriate AMIs, configuring graphics libraries, installing drivers, and managing real-time performance. Given G5g’s design for graphical applications, candidates must show ability to deliver low-latency, high-quality visual experiences across cloud-hosted environments. This skill is essential for industries like gaming, design, media production, and CAD.

This skill examines how well candidates can monitor and improve the runtime performance of G5g instances. It includes identifying CPU/GPU bottlenecks, tuning storage/network throughput, adjusting visualization pipelines, and benchmarking workloads against instance types. Performance optimization is crucial for maximizing the value of G5g’s Arm-GPU hybrid architecture and ensuring that both graphics and inference tasks perform within SLA boundaries while remaining cost-effective.

Security is essential for G5g workloads, especially those involving sensitive graphical or ML content. This skill measures knowledge of hardening EC2 instances, configuring IAM roles for GPU access, managing secure AMIs, and using remote protocols like NICE DCV with encryption. Given the frequent use of G5g in remote visualization and VDI, security best practices protect data in transit and guard against unauthorized access or data leakage in streaming environments.

This skill assesses the use of G5g instances for machine learning inference tasks, such as running pre-trained models using TensorFlow Lite, ONNX, or PyTorch with GPU acceleration. Candidates must know how to deploy optimized Arm-based models, leverage NVIDIA TensorRT, and manage latency versus throughput tradeoffs. With cost and energy efficiency as key differentiators, G5g is ideal for real-time inference scenarios, making this skill vital for ML engineers building production pipelines.

This skill targets operational excellence in using G5g instances. It includes monitoring GPU metrics via CloudWatch, setting up autoscaling groups, using AWS Systems Manager for fleet control, and diagnosing application issues tied to instance health. It ensures that cloud engineers can manage G5g at scale without overprovisioning or incurring runaway costs. Effective instance lifecycle management increases reliability and reduces administrative overhead.

G5g’s success depends on correct GPU setup—especially NVIDIA T4G GPUs on Arm architecture. This skill evaluates the candidate’s ability to install compatible drivers, manage CUDA versions, configure TensorRT for inference, and integrate GPU features into containerized workloads. It ensures readiness for advanced use cases requiring rendering or ML acceleration. Misconfigured drivers can lead to major performance degradation or crashes, making this a critical operational and reliability skill.

G5g instances, while efficient, involve GPU-related costs that need careful management. This skill evaluates understanding of AWS pricing models, including Spot, On-Demand, and Savings Plans, along with usage forecasting, cost-per-inference benchmarking, and choosing correct instance sizes. Right-sizing decisions directly impact budget, especially in high-scale deployments or variable workloads, making this a critical skill for cloud architects and FinOps teams.

This skill assesses the candidate’s ability to compare G5g with other EC2 GPU instances like G4dn, G5, and P-series, and to recommend the right instance based on workload requirements. It includes considerations of processor type (Arm vs x86), GPU power, memory, and use case suitability (e.g., training vs inference, visualization vs encoding). Sound instance family mapping prevents both underutilization and overspending, enabling solution architects to align infrastructure with business goals.