NCP-GENL practice questions
NVIDIA · NCP-GENL · 200 questions
Original practice questions for the NVIDIA-Certified Professional: Generative AI LLMs (NCP-GENL) exam, covering LLM architecture, prompt engineering, data preparation, model optimization, fine-tuning, evaluation, GPU acceleration and optimization, model deployment, production monitoring and reliability, and safety/ethics/compliance — framed in civic and municipal scenarios such as city AI pilots, permitting chatbots, 311 assistants, public-health records, transit/water ops, and courts/clerks.
This course contains the use of artificial intelligence.
About the NCP-GENL exam
- Time allowed
- 2 hours
- Questions
- 60-70
- Languages
- English
- Format
- Online, proctored remotely
Exam details published by the vendor, checked 6 November 2026. Vendors change fees and formats without notice — confirm on the vendor's own page before you book.
Practice Quizzes
Test your knowledge with standard 20-question practice sets.
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Browse by Domain
Study specific topics at your own pace.
LLM Architecture · 12 questions
- A county assessor needs bilingual parcel summaries from deed text. The ML lead must choose between a classic encoder-decoder sequence-to-sequence model and a decoder-only LLM. When is the encoder-decoder structure the better architectural fit?
- A 311 translation service maps English complaint tickets into Spanish acknowledgment letters using a classic encoder-decoder layout. What roles do the encoder and decoder play?
- A transit authority diagrams why its schedule chatbot can relate a stop name mentioned early in the chat to a later transfer question. Which transformer mechanism best explains that long-range link?
- School-district IT compares multi-head attention to a single attention head when reading long IEP narrative PDFs. Why prefer multi-head self-attention?
- Public-health analysts want to cluster outbreak themes from clinic visit notes before any text generation. Which embedding approach fits that goal?
- A housing authority needs embeddings from a decoder-only municipal assistant for similarity search over caseworker chat turns. Which practice yields usable embeddings?
- Emergency management wants after-action summary drafts that sound less repetitive without inventing incident facts. Which generation approach best fits?
- A library events chatbot keeps looping the same programming blurb during sampling. What sampling control should staff apply to reduce that repetition?
- Courts clerk software must keep standardized continuance-notice wording highly predictable. How should decoder sampling be configured?
- Parks recreation copywriters use a decoder LLM to brainstorm summer camp theme names and want more diverse ideas. What sampling change is appropriate?
- A water utility RAG system places ordinance chunks in vector space so a ‘cross-connection’ query retrieves related rules. What do the embeddings represent?
- A county tax helpdesk embeds FAQ answers so ‘homestead exemption’ and ‘primary residence credit’ land near each other. What does that proximity illustrate?
Prompt Engineering · 26 questions
- A permitting office wants the model to check zoning district, setbacks, then fee class before answering. Which prompt pattern best structures that multi-step reasoning?
- Public works has only a few dozen labeled culvert-inspection notes and wants to adapt a model without full fine-tuning. Which approach fits?
- A city clerk needs meeting-minutes Q&A answers that cite ordinance section IDs inside a fixed output schema. What should the prompt template enforce?
- Housing caseworkers need a reusable prompt wrapper for eviction-resource chat that sets role, tone, and prohibited-advice boundaries. What should that template include?
- Fire inspection prompts should separate checklist reasoning from the final pass/fail narrative. Which technique supports that separation?
- A school nutrition bot must follow USDA meal pattern rules rather than generic diet tips. How should prompts be adapted?
- Transit paratransit booking prompts must ensure policy checks happen before ride-time suggestions. What ordering should the prompt enforce?
- County open-data portal chat should prefer CSV column glossary terms over invented field names. What system-prompt guidance helps?
- A brand-new stormwater fee FAQ goes live before any curated answer examples exist. Which prompting regime is appropriate to start?
- Clerk staff want to stabilize tone on ballot-explanation answers using a single gold sample. Which technique fits?
- Library reference chat must refuse medical diagnosis while still pointing residents to vetted links. How can few-shot prompting help?
- After residents confuse trash versus recycling rules, public works wants the model to distinguish correct sortation answers. What few-shot strategy helps?
- Emergency SMS rewrite starts with no approved alert phrasings, then later gains three ratified gold alerts. How should prompting evolve?
- A parks botanic-garden chatbot keeps mangling Latin plant names in resident answers. Staff have one gold reply that shows the required Genus species formatting. Which prompting change best fixes this narrow formatting failure?
- A county assessor wants appeal-letter drafts that sound locally empathetic rather than like a generic national template. Which prompting approach best improves adaptability for this civic tone?
- City IT wants to continue pretraining a decoder LLM on municipal code text so the model learns local ordinance phrasing before any task-specific prompts. Which training objective should they use?
- A transit agency is further adapting an open decoder-only checkpoint on rider-complaint narratives. Engineers debate masked language modeling versus causal language modeling. Which choice is appropriate?
- Public-health continued pretraining shuffles clinic-note documents across the batch but must still train a decoder with causal language modeling. How should token order be handled?
- A courts IT team wants a decoder LLM to absorb public opinion phrasing before any labeled classification work. Which approach best matches that goal?
- A permitting desk requires every model status line to be exactly one of Approved, Needs revision, or Denied. Which wrapping technique best enforces that consistency?
- A 311 assistant sometimes invents service-request types that are not in the city’s official taxonomy. What should the LLM wrapping module do before showing a suggestion to residents?
- A housing-authority chatbot sometimes drops rent-limit dollar amounts into casual prose where staff cannot parse them reliably. Which wrapper design best improves consistency?
- A library hours bot invents branch codes that do not exist in the facilities table. Which technique best blocks those invented IDs?
- An emergency-alert rewriter sometimes inserts phone numbers that are not on the approved contact list. What should the wrapping module do before send?
- A tax FAQ wrapper must not show ordinance answers that lack retrieved citation spans. Which behavior should the module implement?
- A water-quality chatbot must name contaminants only from EPA or state code lists. Which decoding approach best enforces that regulated vocabulary?
Data Preparation · 18 questions
- 311 ticket exports used for triage fine-tuning are missing many neighborhood fields, but latitude and longitude are usually present. What should curators do first in data preparation?
- Inspection severity labels for a classifier LLM are about 90% pass, so rare failures barely appear in training batches. Which curation step best addresses that imbalance?
- Public-health clinic notes vary from a few words to multi-page dumps with noisy whitespace. What cleaning step should analysts take before packing sequences for an LLM?
- Transit delay-reason codes look skewed, and staff suspect weather labels dominate training batches. What should the team do before model training?
- Assessor deed OCR exports include many null grantor names that would poison supervised training if left as empty strings. What is the sound data-curation choice?
- Housing complaint categories were renamed across several years, fracturing what should be the same classes. How should curators prepare the labeled set?
- Court-opinion fine-tuning requires JSONL with prompt and completion fields, but staff only have mixed DOCX dumps. What organization step is required?
- A parks chatbot dataset includes summer festival chatter and winter program text. How should the team split train, validation, and test to avoid leakage?
- Water SCADA narrative logs must be ingested by a NeMo-style pipeline that expects clean text records. Which format preparation is appropriate?
- Library catalog blurbs must be paired with subject headings for supervised tuning. How should the dataset be structured?
- Emergency after-action PDFs will feed both retrieval and fine-tuning. What organization practice keeps those sets aligned?
- County grant applications arrive as scanned TIFF images that must become modeling text. Which preparation step is appropriate?
- Municipal code is packed with section numbers like '12.04.030'. How should the team optimize tokenization for those civic identifiers?
- A bilingual city must choose WordPiece versus BPE vocabulary sizes for English–Spanish permitting forms under tight GPU memory caps. What trade-off should lead the decision?
- Public-health chemical names explode a generic tokenizer into many pieces. What tokenizer change fits specialized civic terminology?
- Transit GTFS stop names tokenize into too many pieces. What should analysts measure and retune?
- School-district IEP notes must run on small edge inference boxes in nurse offices. How should the tokenizer vocabulary be sized?
- Tax form checkboxes become unknown tokens during training. What should the team do before modeling?
Model Optimization · 34 questions
- City hall’s older inference GPU runs out of memory on a 70B permit assistant. Which optimization approach best fits the hardware?
- A library kiosk must keep a summarizer interactive on a single workstation GPU. Which technique specifically targets activation memory?
- Transit edge boxes need sparsity that maps to Tensor Core acceleration for delay explainers. Which choice fits?
- Public-health overnight jobs want to prune rarely used FFN weights using clinic FAQ statistics. Which pruning approach matches that goal?
- Courts want smaller continuance-notice models by cutting unused attention capacity and lowering weight precision. What combination is appropriate?
- On A100 nodes, county IT wants a stable tax FAQ model quantized after training and must report quality change versus FP16. What strategy fits?
- A housing eligibility model lost too much minority-language accuracy under PTQ. Which quantization path should the team choose next?
- H100 Tensor Core profiling for emergency alert rewriting shows risk if INT8 is too aggressive. What precision choice aligns with GPU generation and task risk?
- Water quality classifiers will ship to field tablets. What should engineers compare before choosing INT8 scope?
- School grading-rubric assistants stay on FP16 after INT8 calibration sets prove too small for reliable PTQ. Why is that decision sound?
- Neighborhood kiosks cannot host a 70B city-code teacher. How should the city create a deployable model?
- Clinic waiting-room tablets need a compact summarizer. How can a large public-health teacher transfer knowledge effectively?
- Transit keeps a large teacher on-prem DGX but must run inference on bus-yard rugged PCs. What deployment split is correct after distillation?
- Courts distill a multilingual teacher so the smaller student must keep Spanish continuance phrasing. What should the distillation objective emphasize?
- Housing chat distillation should teach the student to cite policy IDs the way the larger teacher does. How should losses be designed?
- County ML runs a distributed learning-rate sweep for LoRA on ordinance QA. What is the primary goal of that search?
- 311 intent fine-tuning saturates GPU memory as batch size rises. What adjustment correctly continues effective batch growth?
- Parks captioning needs overnight trials of dropout and weight decay combinations. Which approach matches systematic tuning?
- Assessor deed summarization validation loss improves with warmup plus cosine decay versus a constant learning rate. What should the team select?
- Emergency management wants to lock classifier hyperparameters. What confirms the chosen settings are stable?
- After quantizing a city clerk letter model, staff need consistent legal boilerplate. How should they judge optimization impact on wording consistency?
- Transit chatbot latency improved after several optimizations. How should the team isolate each contribution?
- A public-health summarizer feels flat after distillation. What evaluation step helps recover diversity without factual drift?
- Housing eligibility answers got worse after pruning while staff also lowered temperature. How can they prove what caused the drop?
- Library catalog blurbs lost quality after sparsity was applied. What ablation helps test whether decoding can recover quality?
- Residents ask follow-up ordinance questions in one long municipal code session. Which optimization keeps those turns fast?
- City ML validated FP16 accuracy for a permitting assistant on A100 hardware. What optimization export fits next?
- Council-meeting transcript assistants must cover multi-hour sessions without paying full-context attention cost. Which method fits?
- Edge emergency kiosks lack GPU tactics needed for TensorRT rebuilds. What optimization choice matches the hardware?
- A water utility chatbot streams tokens to residents and needs low first-token latency. Which combination helps?
- County IT wants encoder weights that already understand property-record language before classifiers train. What pretraining approach fits?
- Clerk staff want an encoder backbone that links agenda items to matching minutes paragraphs. Which objective helps?
- After MLM pretraining on ordinances, the county needs a smaller encoder for embedding search. What conceptual next step fits?
- Public-health staff distill models for clinic FAQ chat versus for embedding search. What distinction should they keep clear?
Fine-Tuning · 26 questions
- 311 supervisors labeled preferred versus rejected triage replies and lack full RLHF infrastructure. Which alignment approach fits?
- Permit reviewers have gold “complete application” letters ready. What alignment order should the city follow?
- Housing counselors ranked groups of resource-referral answers from best to worst. Which method can use those group preferences?
- Public-health leaders can staff clinician raters for outbreak SMS tone and accept higher ops cost. When is RLHF the better choice?
- Court self-help drafts sometimes invent filing fees. How should preference data encode that failure?
- Transit customer-service SFT is about to start. What data curation step supports intentional policy compliance?
- After SFT, resident satisfaction with a city assistant has stalled despite more gold demos. What should the team consider next?
- Ordinance retrieval should map paraphrased resident questions to the same code section. Which fine-tuning signal helps?
- A city wants a short-term rental FAQ specialty without duplicating the shared base LLM. Which PEFT choice fits?
- Library branches need children’s desk help and research-desk help without reloading full model weights. What approach fits?
- A floodplain office has only a tiny labeled Q&A set and scarce GPU hours. Which parameter-efficient approach fits best?
- The county assessor wants photo captions and deed snippets for the same parcel to land near each other in embedding space. What training signal should staff use?
- Emergency management needs Spanish and Vietnamese alert packs on one shared decoder backbone. How should the packs ship?
- A water utility freezes its base LLM weights and only trains adapters when the chemical glossary updates. What is the main benefit?
- Permit QA fine-tuning shows falling train loss while validation exact-match plateaus for three consecutive evals. What should the team do?
- A public-health LLM project summarizes outbreak briefs and also classifies triage intents. How should metrics be chosen?
- A clerk chatbot begins quoting memorized party names from training cases. How can early stopping help?
- A housing alignment run must improve preference win-rate without raising hallucination rate. What early-stop rule fits?
- Transit delay-explainer training needs reliable early stopping. How should validation be scheduled?
- A school IEP summarizer improves BLEU but clinician Likert scores fall. What should the team prioritize for stopping and promotion?
- After applying a LoRA adapter, a tax bot invents rebate amounts. What impact assessment action is appropriate?
- Clerk Q&A invents ordinance numbers. Which fine-tuning change best mitigates that hallucination pattern?
- Housing ops will only allow PEFT updates in production. Why does that policy improve safety?
- Public works must prove a pothole FAQ LoRA helps more than it harms. What assessment design fits?
- Emergency alerts invent unverifiable shelter bed counts. Which fine-tuning mitigation targets that failure?
- Parks venue names change overnight and a full SFT cycle will not finish before morning events. What should staff apply?
Evaluation · 14 questions
- City clerks must decide if an agenda summarizer is ready for go-live. Which evaluation approach is most appropriate?
- Housing referral replies are scored by an LLM-as-a-judge for helpfulness. What additional step keeps the process trustworthy?
- Tax FAQ validation perplexity drops sharply while BLEU on letter drafts barely moves. How should analysts read that?
- Public-health outbreak SMS candidates need clarity ranking and medical safety checks. Which layered evaluation fits?
- Error analysis shows a 311 bot fails when residents use slang street names missing from the gazetteer. What did the team diagnose?
- Permit assistants omit floodplain checks across a week of logs. What error-analysis action is appropriate?
- A court FAQ invents forms that look real. How should error taxonomy handle that versus outdated-but-real forms?
- A transit chatbot flips morning versus evening headways after daylight-saving changes. What should the team correlate?
- A county must compare ordinance QA quality and latency on on-prem DGX versus cloud GPUs. What approach is correct?
- A city moves ordinance QA from on-prem DGX to cloud GPUs. What keeps quality deltas attributable to the platform?
- Emergency model quality matches on cloud and DGX, but p95 latency meets SLA only on-prem. What should ops document?
- A housing AI program needs scalable assessment across automatic scores, judges, humans, and error reviews. Which design fits?
- Public works wants models blocked from promotion unless evaluation is complete. What CI practice fits?
- School-district LLM governance must stay scalable yet deep. Which evaluation cadence is appropriate?
GPU Acceleration and Optimization · 28 questions
- City DGX fine-tuning of a large ordinance model no longer fits DDP replicas per GPU. What multi-GPU choice fits?
- A county continued-pretrain of a 70B civic model needs layers split across GPUs. Which parallelism applies?
- Housing trainers stage a deep encoder across four GPUs while microbatches keep flowing. What technique is that?
- A 311 intent model already fits on one GPU. How should the city speed fine-tuning with spare accelerators?
- Long council transcripts exceed one GPU’s activation memory for context. Which approach helps?
- A city MoE router model places different experts on different GPUs. What parallelism is that?
- Public-health bilingual clinical-note adaptation runs on an 8-GPU node. Which hybrid strategy fits when neither pure DP nor pure TP alone is ideal?
- Even a single layer of an assessor multimodal stack exceeds one GPU. What is required?
- Transit fine-tuning on A100s needs higher tokens/sec. Which optimization should be enabled?
- Clerk GPU jobs raise microbatch until memory spikes. What tuning restores steadier throughput?
- A long housing SFT run shows loss spikes under FP16. Which mixed-precision choice is more appropriate?
- Emergency model serving sees alert spikes and underfilled GPU batches. How should ops keep Tensor Cores busy?
- Water-utility overnight jobs need larger effective batches than activations allow. What technique helps?
- School-district trainers see GPUs idle between steps waiting on data. What host-side fix helps?
- Parks captioning enables AMP and must prove Tensor Core gains to budget owners. What should they measure?
- Profiling a city LLM shows Softmax+MatMul dominating step time. What attention-path action fits?
- Housing LoRA has only two GPUs and needs a larger effective batch. Which technique mimics that batch?
- A long-context ordinance model must spread QKV GEMMs across GPUs. What distribution approach fits?
- Public-health wants reproducible clinic-model runs with gradient accumulation. What configuration rule matters?
- Courts see memory-bound attention GEMM timelines on DGX. Which kernel choice is appropriate?
- County assessor parcel-description fine-tuning OOMs mid-epoch on fixed GPUs. Which change keeps the effective batch workable without buying hardware?
- Transit’s long-context schedule LLM uses sequence-parallel attention, but wall time barely improves. What should ops prioritize?
- Nsight Systems shows the city’s 311 trainer idle on GPU while CPU tokenization saturates. What fix matches the profile?
- Emergency-management fine-tunes fragment GPU memory mid-run despite average free VRAM looking fine. What should ops do?
- Kernel timelines for clerk SFT show unfused LayerNorm dominating step time. Which change is most appropriate?
- Housing-authority inference latency spikes line up with heavy host↔device copies in the profiler. What is the right response?
- Water-lab LLM OOMs only on rare very long reports; memory profiles show activation peaks. What policy fits?
- School-district IT’s custom CUDA preprocess kernel is slow. Nsight Compute shows low SM occupancy. What next?
Model Deployment · 18 questions
- A county needs fast ordinance retrieval-only search under a tight VRAM budget. Which model-type choice fits?
- Resident complaint translation needs bilingual rewrite quality, while FAQ chat must stay on a slim GPU. How should architecture be split?
- Public-health field tablets are memory-tight but need outbreak label triage. Which tradeoff is sound?
- City hall needs long-form meeting-minute summaries and short 311 chat replies under different latency SLAs. Which pairing fits?
- The assessor similarity-search service must stay under a hard GPU memory ceiling. Which decision is correct?
- Emergency bilingual alert rewrite produces dangerous errors on a tiny decoder but is accurate on a costlier encoder-decoder. What should leadership accept?
- Permitting LLM traffic spikes at noon. How should the city package and serve inference?
- The library needs catalog embeddings plus a generative reranker in one serving graph. Which approach fits?
- Transit must meet a p95 latency SLA for rider queries while still using dynamic batching. What should ops tune?
- Housing and city-hall inference must match clinic staging behavior. What container practice is essential?
- Public works has one GPU for a small ticket classifier and a large work-order summarizer. How can Triton help?
- Clerk filing assistants need tokenization before the LLM backend. How should the containerized pipeline be structured?
- Building-season peaks deepen the permitting inference queue. Which Kubernetes control fits?
- A school lab must run the same NIM-style LLM image used in staging. What approach is appropriate?
- Ordinance answers must retrieve, generate, then schema-validate before residents see them. How should serving be managed?
- The 311 assistant runs on a Kubernetes serving mesh. What live monitoring should ops implement with serving?
- Water-utility Dockerized LLM pods must not schedule onto CPU-only workers. What Kubernetes control fits?
- Courts are rolling out a new clerk LLM on Kubernetes while most replicas must stay on the proven build. Which serving practice best limits blast radius?
Production Monitoring and Reliability · 14 questions
- City AI ops is standing up reliability visibility for the permitting LLM. Which dashboard metric set best matches production monitoring needs?
- Public-health wants the clinic chatbot board to reflect answer quality, not only whether the service is up. What should they add beside uptime?
- Transit leadership cares whether the rider assistant stays available when morning peak load hits. Which reliability metric should the SLO board treat as first-class?
- Housing chat starts returning HTTP 500s. Structured logs show vector database timeouts lining up with the spike. What is the right monitoring response?
- After a tokenizer update, the clerk bot suddenly returns many empty responses. How should ops treat that signal?
- An overnight deploy made the emergency alert rewriter behave oddly. Which logging practice best supports root-cause diagnosis?
- Each week the city wants proof the live ordinance QA LLM has not silently regressed versus last month. What practice fits?
- Housing chat win-rate versus the prior version drops right after a LoRA swap. What should production reliability practice trigger?
- Public works wants nightly evidence on a candidate LLM without exposing residents to it yet. Which approach fits continuous version benchmarking?
- The city wants the 311 intent head refreshed on a schedule with traceable artifacts. Which production practice fits?
- Parks chat keeps breaching latency SLOs. Ops wants production automation before a full model retrain. What fits?
- Courts require that any production clerk LLM can be reproduced exactly from registry state. Which versioning discipline is required?
- Residents ask whether the permitting assistant is trustworthy and currently working. Which transparency practice supports uptime trust?
- Emergency information LLM answers must continue if one city GPU pool fails. Which reliability architecture fits?
Safety, Ethics, and Compliance · 10 questions
- Before launching a benefits chatbot, what responsible AI deployment practice should the city complete?
- A school-district LLM will generate student-facing disciplinary language features. Which responsible go-live control fits?
- Housing referral replies may favor some neighborhoods or languages. How should the city audit the LLM for fairness?
- Permit staff worry the assistant sounds harsher to homeowners than to contractors for similar prompts. What bias audit fits?
- City ops already tracks latency for the 311 LLM. Which monitoring configuration adds ethical compliance signals?
- Public-health production monitors show refusal rates collapsing overnight. What does that safety signal suggest?
- Bias detection finds the county chatbot underperforms on rural dialects. Which mitigation strategy fits?
- Courts want to detect whether self-help answers change unfairly with resident identity cues. Which bias detection method fits?
- The tax FAQ bot must not give personalized legal tax-evasion advice. Which control implements that restriction?
- An emergency chatbot must not invent open shelter bed counts. Which guardrail behavior is correct?
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