NCP-GENL Practice Questions with Explanations: 20 Scenarios [2026]

Your team serves a 70B-parameter instruct model with TensorRT-LLM (NVIDIA's inference engine builder for LLMs) on H100 GPUs. Finance wants each replica to fit on one 80 GB GPU. Traffic runs at concurrency 8 to 16 with short prompts and long generations, and your held-out eval set tolerates at most one point of accuracy loss. Which quantization plan should the engine use?

A support-ticket triage service uses an 8B model to assign one of 30 labels and a one-sentence justification. Volume is 20 million tickets per day, per-ticket cost must fall by roughly 4x, latency is not critical, and accuracy may drop at most two points against the current model. The GPU budget is fixed. Which optimization path fits best?

Your Triton Inference Server deployment runs a TensorRT-LLM engine with static batching at batch size 8 and pads every request to the maximum sequence length. Requests range from 100 to 6,000 tokens. GPU utilization sits near 35 percent while a queue of waiting requests grows during peak hours. Which TWO configuration changes will raise throughput the most? (Select TWO)

You are pretraining a 30B-parameter dense model on four DGX H100 nodes (32 GPUs). In BF16 the weights, gradients, and Adam optimizer state do not fit on one GPU. NVLink connects GPUs inside a node and InfiniBand connects the nodes. Which parallelism layout keeps the heaviest communication on the fastest links?

A 40B-parameter model trains with tensor and data parallelism at 8K-token sequence length and already runs at micro-batch size 1. Training now fails with out-of-memory errors. An Nsight Systems profile and a memory snapshot show activations, dominated by attention, taking most of each GPU's memory, while weights and optimizer state are already sharded. Which change addresses the actual bottleneck?

Across eight DGX nodes, your data-parallel training step spends 45 percent of its time in gradient all-reduce. NCCL bandwidth tests reach only a fraction of the InfiniBand line rate, GPUDirect RDMA is not active on the hosts, and the trainer issues many tiny all-reduce calls at the end of each backward pass. Which TWO actions cut communication time? (Select TWO)

An invoice-reconciliation service asks a model to compute line-item adjustments and return JSON. Zero-shot accuracy was 61 percent; adding chain-of-thought instructions raised it to 84 percent, but the reasoning text now spills into the response and breaks the JSON parser on one request in five. Which prompt design keeps the accuracy gain and fixes the parsing failures?

A customer-intent classifier prompts a hosted model with the same five hand-picked examples for every request across 40 intents. Accuracy on the ten rarest intents is poor, the context window cannot hold examples for every intent alongside long customer messages, and latency targets rule out multiple calls per request. What is the best way to improve rare-intent accuracy?

A logistics planner uses a model to build multi-step routing plans from constraints. Single-pass chain-of-thought answers are correct 70 percent of the time and vary between runs, and failed plans usually break one late-stage constraint. You have a latency budget for a few extra model calls per plan. Which TWO prompting changes raise reliability? (Select TWO)

You need to fine-tune a 13B-parameter model on 40,000 domain conversations, and the only hardware available for the next month is a single NVIDIA A10 with 24 GB of memory. The team accepts somewhat slower training but wants a result close to standard LoRA quality. Which approach makes the job feasible on this GPU?

After full fine-tuning an instruct model on 50,000 in-domain question-answer pairs, domain accuracy improved sharply, but the model now ignores formatting instructions, forgets its chat behavior, and refuses less appropriately than before. Stakeholders want the domain gains without the regression, and the training budget allows one more run. What should the next run change?

You supervised-fine-tune a chat model with LoRA on 30,000 multi-turn conversations. The fine-tuned model sometimes continues past its answer, generating a fabricated user turn and then replying to it, and it occasionally echoes the user's question before answering. Which TWO data-preparation and training choices most directly prevent these failures? (Select TWO)

You are assembling a continued-pretraining corpus of 200 million web documents for a domain model. A first pass showed heavy near-duplicate boilerplate and a long tail of low-quality pages. The team has a multi-node GPU cluster and a two-week deadline. Which pipeline handles the volume while removing both problems?

A base model's tokenizer splits your organization's chemistry nomenclature into six to ten subword tokens per term, so sequences run about twice as long as equivalent general text and long documents no longer fit the context window. You plan a continued-pretraining phase on 30 billion domain tokens. What is the right vocabulary strategy?

One model must serve two workloads: an interactive assistant with a 300 ms time-to-first-token target and 24-hour global traffic, and a nightly job that summarizes five million documents before 6 a.m. Both currently hit the same endpoint, and the nightly job pushes assistant p99 latency past its SLO. Which deployment design fixes this?