NCA-GENL Software Development Practice Question
A team is building an internal document assistant and wants the model to answer only from an approved corpus of HR policy PDFs. They will deploy the model with NVIDIA NIM and control grounding at generation time by injecting retrieved passages into the prompt. Which parameter combination in the NIM chat completions request best enforces this grounding while keeping responses deterministic for audit logs?
⚠ Common exam trap
The trap here is assuming that a sampling knob such as top_p or presence_penalty can enforce grounding, when grounding actually comes from the retrieved context placed in 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
✓
Set temperature to 0 and provide the retrieved passages inside the system and user messages as the only context, instructing the model to refuse when the answer is not present.
Grounding in a NIM-hosted model is achieved by supplying the approved passages as prompt context and constraining behavior through the system message, while temperature zero gives repeatable outputs for audit. The other choices either increase randomness, assume server-side PDF parsing that does not exist in the chat completions API, or apply sampling penalties that do not limit the model to the HR corpus.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Set temperature to 0 and provide the retrieved passages inside the system and user messages as the only context, instructing the model to refuse when the answer is not present.
Why this is correct
Setting temperature to 0 makes sampling greedy and repeatable for audit logs, and placing the retrieved passages directly in the system and user messages is exactly how retrieval-augmented grounding is enforced at generation time with an OpenAI-compatible NIM endpoint. The explicit refusal instruction constrains the model to the supplied HR corpus, which is the requirement here.
- ✗
Set temperature to 1.5 and rely on the model's pretrained HR knowledge, then post-filter answers with a regex for policy numbers.
Why it's wrong here
High temperature increases randomness, so the same question can produce different answers, breaking deterministic audit logging. Relying on pretrained knowledge means the model may answer from stale or fabricated policy content outside the approved corpus. A regex post-filter only catches formatting patterns, not ungrounded claims, so it cannot enforce grounding to the HR PDFs.
- ✗
Set top_p to 0.1 and pass the PDFs as base64 file attachments in the request body so NIM parses them automatically.
Why it's wrong here
NIM chat completions accept text messages, not arbitrary base64 PDF attachments that the server parses into retrieval context; that parsing is the application's retrieval layer responsibility. Restricting top_p alone narrows sampling but does not supply the approved corpus, so the model can still answer from pretraining. Determinism and grounding are separate concerns that this approach does not address.
- ✗
Set presence_penalty to 2.0 and include only the document titles so the model is discouraged from inventing policy details.
Why it's wrong here
Presence penalty discourages repeating tokens already used; it does not restrict the model to a source corpus. Titles alone give no substantive policy text, so the model must fall back on pretraining and may hallucinate specifics. This also leaves temperature unset, so outputs vary between calls, which conflicts with the deterministic audit requirement.
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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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