NCA-GENL Software Development Practice Question
Which TWO of the following practices are recommended when using NVIDIA Triton Inference Server to maximize throughput for a concurrent multi-model deployment?
⚠ Common exam trap
Test-takers frequently confuse dynamic batching with static batching or mistakenly think that adding more hardware nodes alone replaces the need for optimized instance group configurations on a single server instance.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Configure multiple instance groups per model for concurrency.
Optimizing Triton involves managing hardware resources and execution concurrency. By configuring instance groups, developers can ensure that multiple model instances are pre-loaded to saturate GPU compute. Additionally, using dynamic batching allows the server to aggregate individual requests into larger batches, which is essential for maximizing GPU utilization during high-traffic periods. These strategies are fundamental for scaling LLM services in production environments where resource contention is a primary concern.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Configure multiple instance groups per model for concurrency.
Why this is correct
Creating multiple instance groups allows the server to spawn several concurrent execution units for a single model. This capability is vital for parallelizing requests across multiple GPU streams, effectively hiding latency and increasing total throughput when the hardware has sufficient spare capacity to handle additional workloads.
- ✓
Enable dynamic batching in the model configuration.
Why this is correct
Dynamic batching aggregates individual inference requests arriving within a short window into a single batch. This improves GPU efficiency by processing more data in parallel, which is particularly beneficial for deep learning models that perform better with larger matrix multiplications on highly parallel GPU architectures.
- ✗
Disable all logging to reduce disk I/O latency.
Why it's wrong here
While logging does consume minor resources, disabling it entirely hinders observability and troubleshooting. The performance gain from disabling logs is negligible compared to the operational risk of having no visibility into server health, model errors, or performance bottlenecks during a critical production deployment cycle.
- ✗
Set the batch size to 1 for all incoming requests.
Why it's wrong here
Forcing a batch size of 1 disables the performance advantages of vectorized hardware operations. NVIDIA GPUs are designed to process large blocks of data simultaneously; limiting the workload to single requests leads to low occupancy and significantly reduced total system throughput in high-load scenarios.
- ✗
Use the default backend for every model type.
Why it's wrong here
Triton supports specialized backends like TensorRT, ONNX, and PyTorch. Using the default backend for complex models often results in suboptimal execution because specialized backends provide hooks for hardware acceleration and model-specific optimizations that generic backends cannot provide, leading to poor overall performance during inference tasks.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
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