NCA-GENL Software Development Practice Question
When deploying an LLM, what is the 'Time to First Token' (TTFT) metric used to measure?
⚠ Common exam trap
Candidates frequently confuse 'Time to First Token' (TTFT) with 'Total Latency' or 'Tokens Per Second,' failing to realize TTFT specifically measures the initial delay before the generation process starts.
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 latency experienced before generation begins.
TTFT measures the delay between sending an inference request and receiving the first piece of generated text. This is a crucial metric for user experience because a long TTFT makes an application feel unresponsive. Developers must optimize model loading, initial compute, and network latency to keep this time low, ensuring that the generative response feels immediate and interactive for the end user during the application's runtime.
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 time taken to train the model.
Why it's wrong here
Training time is measured in hours, days, or even weeks for large LLMs. TTFT is a millisecond-level measurement relevant to the inference phase only. Confusing these two metrics would lead to a fundamental misunderstanding of the performance requirements for a real-time production inference service.
- ✓
The latency experienced before generation begins.
Why this is correct
TTFT captures the initial delay before the model produces the first token. This includes processing the prompt and preparing the model context, which is the primary indicator of responsiveness in generative AI applications. Reducing this latency is essential for creating high-quality, real-time user experiences in production.
- ✗
The average number of tokens generated per second.
Why it's wrong here
The average number of tokens per second is a throughput metric, not a latency metric like TTFT. While throughput is important for system capacity, TTFT specifically tracks the responsiveness of the system, which is a different aspect of performance that governs the user's initial impression.
- ✗
The amount of memory required to store the model.
Why it's wrong here
Memory requirements are measured in gigabytes of VRAM. TTFT is a time-based performance metric. These are distinct concepts, as memory usage relates to the static capacity of the hardware, while TTFT relates to the dynamic speed of the model's inference engine during a live request.
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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.