NCA-GENL Software Development Practice Question
When designing a scalable inference microservice, which factor most significantly impacts the 'Time to First Token' (TTFT) for concurrent users?
⚠ Common exam trap
Candidates often focus on decoding speed or output tokens, failing to realize that TTFT is primarily constrained by the compute-heavy prefill phase of the prompt.
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
✓
The prefill phase efficiency and GPU throughput.
TTFT is primarily driven by the time taken to process the input prompt and initialize the decoding state. In a multi-user environment, resource contention and efficient scheduling become the main inhibitors. Optimizing the prefill phase, which is compute-bound, is critical for achieving low TTFT, allowing users to perceive the application as responsive even during high load periods.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The total number of output tokens generated.
Why it's wrong here
Total output tokens impact the total latency but not the TTFT. TTFT is measured before the completion of the generation phase. Therefore, the length of the final response has no bearing on how quickly the model emits the very first token in the sequence.
- ✓
The prefill phase efficiency and GPU throughput.
Why this is correct
The prefill phase is where the model processes the prompt and calculates the initial KV cache. High GPU throughput and optimized kernels (like those in TensorRT-LLM) directly reduce the duration of this compute-heavy phase, which is the primary contributor to the time elapsed before the first token appears.
- ✗
The network latency between the user and the load balancer.
Why it's wrong here
While network latency affects overall response time, it is an external factor outside the inference engine's control. Within the context of microservice design, the internal processing efficiency of the GPU is the dominant variable that developers can tune to ensure optimal performance for concurrent user requests.
- ✗
The number of active vector databases connected to the service.
Why it's wrong here
Vector database latency contributes to the retrieval phase but does not directly impact the TTFT of the generation phase once the prompt is delivered. While crucial for RAG, it is separate from the model's internal prompt processing speed, which remains the primary driver for TTFT.
About these practice questions
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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
This NCA-GENL practice question is part of Courseiva's free NVIDIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the NCA-GENL exam.