NCA-GENL Software Development Practice Question
A developer needs to ensure that an LLM application remains deterministic across multiple runs. Which parameter configuration is most effective?
⚠ Common exam trap
Candidates often only set the temperature to zero while forgetting to fix the random seed, leading to non-deterministic behavior stemming from initialization or floating-point non-associativity.
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
✓
Set temperature to 0.0 and define a fixed seed.
Determinism in LLMs is achieved by minimizing the stochastic nature of the generation process. By setting the temperature to zero and fixing the seed, the model consistently follows the same probability path. This is vital for debugging, testing, and production scenarios where identical inputs must yield identical outputs, ensuring reliability in complex automated workflows and compliance with validation requirements.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase top_p to 1.0 and set temperature to 0.7.
Why it's wrong here
Setting top_p to 1.0 and temperature to 0.7 introduces high levels of randomness into the output. This configuration maximizes creative variance, making the output highly non-deterministic, which is the opposite of the requirement for consistency and repeatability in automated software testing or production pipelines.
- ✓
Set temperature to 0.0 and define a fixed seed.
Why this is correct
Temperature 0.0 forces the model to choose the most likely token (greedy decoding), while a fixed seed ensures the underlying noise in the sampling process remains constant. Combined, these create a highly deterministic environment where inputs consistently map to the same output tokens, satisfying the requirement.
- ✗
Disable streaming and use a large batch size for inference.
Why it's wrong here
Batch size and streaming affect throughput and latency, not the underlying token selection logic. Changing these parameters will not eliminate the stochasticity of the model's generation process. Determinism relies on sampling configuration, which is independent of how the API delivers the generated content to the client.
- ✗
Apply top_k filtering with a value of 50.
Why it's wrong here
Top_k filtering limits the pool of candidates but does not force a single path. Even with top_k enabled, the model still selects from the top 50, allowing for variation if the temperature is above zero. Therefore, it does not guarantee the determinism required for consistent testing.
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.