NCA-GENL Trustworthy AI Practice Question
A bank uses an NVIDIA NIM microservice to host an LLM for loan pre-screening. Before go-live, the risk team must confirm that the model's outputs are reproducible and that any change in behavior can be traced to a specific model version. Which deployment practice best satisfies this requirement?
⚠ Common exam trap
The trap here is assuming that capturing prompt and response logs is equivalent to model version control, when logging records behavior but never pins the artifact that generated it.
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
✓
Pin the NIM container to an immutable image digest and record the model name, digest, and inference parameters in a model registry entry for each release.
Reproducibility and traceability require freezing the exact serving artifact and recording it. An immutable image digest locks the weights, tokenizer, and runtime, while a registry entry maps each release to that digest and its inference parameters. Logging, parallelism, or higher temperature do not bind an output to a specific model version, so they cannot satisfy an audit that must reconstruct which model produced a decision.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Pin the NIM container to an immutable image digest and record the model name, digest, and inference parameters in a model registry entry for each release.
Why this is correct
Pinning the container to an immutable digest freezes the exact serving stack, including the model weights and tokenizer, while the registry entry ties a human-readable release to that digest and its sampling parameters. Any behavioral change then maps to a new registry record, giving auditors a reproducible, traceable lineage from output back to a specific artifact.
- ✗
Run the model on two GPUs in a tensor-parallel configuration so responses are identical across replicas.
Why it's wrong here
Tensor parallelism distributes one model across GPUs to reduce latency and memory pressure; it does not create a version record or guarantee bitwise-identical sampling across separate deployments. Two replicas of the same digest can still diverge if temperature or top-p differ, and nothing here identifies which weights produced a given output during an audit.
- ✗
Enable verbose prompt logging on the NIM endpoint and retain the logs for 30 days.
Why it's wrong here
Verbose prompt logging captures inputs and outputs but does not pin the model artifact or its runtime configuration, so a weight or tokenizer change during a rolling update would silently alter behavior with no version identifier attached. Retention windows also expire, breaking traceability for audits performed months later. It documents requests, not model provenance.
- ✗
Increase the model's temperature setting so the risk team can observe a wider range of outputs before approving the release.
Why it's wrong here
Raising temperature deliberately increases randomness, which directly undermines reproducibility and makes it harder to attribute a decision to a stable model behavior. It also degrades the determinism needed for regulated loan decisions. Observing more varied outputs is not evidence of version control and provides no artifact identifier for audit trails.
About these practice questions
This NCA-GENL question is part of Courseiva's 367-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.