NCA-GENL
Free NVIDIA Generative AI & LLMs (NCA-GENL) Practice Test
12 exam-style questions with full explanations — no sign-up. Score yourself, then close your gaps with the full course.
Exam fee ~$1357 exam domainsLevel Beginner2 timed practice tests in the course
✅ Free practice — no sign-up📝 Real exam-style questions💡 Detailed explanations💸 30-day money-back via Udemy
A financial services company needs to predict customer loan default risk using historical data with 47 features including income, credit score, and payment history. The model must provide probability scores and explain which features most influenced each prediction for regulatory compliance. Which approach BEST meets these requirements?
Gradient boosting models excel at tabular data with mixed feature types, provide probability outputs, and SHAP (SHapley Additive exPlanations) values offer mathematically rigorous, per-prediction explanations that satisfy regulatory requirements. This combination delivers both high accuracy and interpretability.
A financial services company is building a document classification system that must process 10,000-word legal contracts. The system needs to understand references between clauses that may be 8,000 words apart. Which architectural component of transformers makes this feasible compared to RNNs?
Self-attention allows transformers to compute direct relationships between any two tokens in O(1) steps, regardless of their distance in the sequence. This eliminates the vanishing gradient problem that prevents RNNs from learning long-range dependencies beyond a few hundred tokens.
A developer is building an AI assistant that must generate SQL queries from natural language. Initial tests show the model frequently generates syntactically correct but logically incorrect queries (e.g., using wrong JOIN types or missing WHERE clauses). Which prompt engineering technique would BEST improve query accuracy?
Few-shot learning with examples and explanations teaches the model the logical patterns needed for accurate SQL generation. Including reasoning helps the model understand WHY certain JOINs or clauses are used, directly addressing the logical correctness issue.
A financial services company is deploying a generative AI system to assist loan officers in making credit decisions. The system generates explanations for recommended approval or denial of loan applications. Which approach BEST ensures the system remains aligned with the company's responsible AI principles while maintaining regulatory compliance?
Human-in-the-loop workflows are critical for high-stakes decisions like credit approval. This approach ensures human oversight, maintains accountability, creates an audit trail, and allows humans to catch potential bias or errors before they impact customers. This aligns with responsible AI principles requiring human agency and oversight in consequential decisions.
A data scientist is preparing a dataset containing customer purchase history for machine learning. The dataset has a 'purchase_date' column in ISO format (YYYY-MM-DD). To improve model performance for predicting seasonal buying patterns, which feature engineering approach would be MOST effective?
Cyclical features using sine/cosine transformations preserve the periodic nature of temporal data (e.g., December is close to January, Sunday wraps to Monday). This is critical for seasonal patterns where the model needs to understand that month 12 and month 1 are adjacent in the cycle, not 11 months apart.
A company is evaluating their generative AI chatbot that provides customer support responses. They want to measure how well the AI's answers match the quality and accuracy of responses written by their expert support team. The evaluation dataset contains 500 customer questions with corresponding expert-written reference answers. Which evaluation approach would BEST measure the similarity between AI-generated responses and expert responses?
Semantic similarity using embeddings (e.g., sentence-transformers, OpenAI embeddings) captures meaning and intent rather than exact wording. This is ideal for evaluating support responses where different phrasings can convey the same helpful information. Cosine similarity on embeddings is the industry standard for semantic evaluation in 2026.
A financial services company needs to deploy a Llama 2 70B model for real-time fraud detection with sub-100ms latency requirements. The model will handle 10,000 concurrent requests during peak hours. Which NVIDIA deployment solution BEST meets these requirements?
Triton Inference Server with TensorRT-LLM provides the lowest latency for LLM inference through kernel fusion, quantization (FP8/INT8), and in-flight batching. Dynamic batching optimizes throughput for concurrent requests. A100 GPUs provide sufficient memory bandwidth for 70B models with TensorRT-LLM optimizations.
During neural network training, validation loss decreases for 15 epochs, then increases for the next 10 epochs while training loss continues to decrease. Training accuracy is 94% and validation accuracy is 78%. What is the PRIMARY issue and the BEST solution?
The divergence between training and validation performance (94% vs 78%) combined with increasing validation loss while training loss decreases is the classic signature of overfitting. Dropout prevents co-adaptation of neurons and early stopping would have halted training at epoch 15 when validation performance peaked.
During inference, a GPT-based model generates text by sampling the next token from a probability distribution. The temperature parameter is set to 0.2. What behavior should the development team expect?
Low temperature (0.2) sharpens the probability distribution, making the model strongly favor the highest-probability tokens. This produces more deterministic, conservative outputs that stick closely to patterns seen during training, reducing randomness.
A company needs its LLM application to consistently return product recommendations in a specific JSON format with fields: product_id (integer), name (string), price (float), and reasoning (string). Early tests show the model sometimes returns valid JSON but with incorrect field types or missing fields. What is the MOST reliable approach to ensure consistent output format?
Structured output modes (like OpenAI's JSON mode with schema or Anthropic's tool use) enforce the output format at the model level, guaranteeing the response matches the specified JSON schema. This is the most reliable method for consistent formatting.
During red-teaming exercises, a security researcher discovers that a customer-facing chatbot can be manipulated through carefully crafted prompts to reveal training data that includes customer email addresses. What is the MOST comprehensive mitigation strategy?
This defense-in-depth approach addresses the problem at multiple levels: output filtering provides immediate protection, retraining on sanitized data eliminates the root cause (PII in training data), and regular adversarial testing ensures ongoing security. This comprehensive strategy follows responsible AI safety principles by combining preventive, detective, and corrective controls.
A machine learning pipeline processes streaming data with multiple numerical features that have different scales (age: 0-100, income: 20000-500000, credit_score: 300-850). The team notices that gradient-based models are converging slowly and performing poorly. Which preprocessing technique should be applied FIRST to address this issue?
StandardScaler (z-score normalization) is the primary solution for features with different scales in gradient-based models. It transforms each feature to have mean=0 and standard deviation=1, ensuring all features contribute equally to gradient calculations and distance metrics. This directly addresses the slow convergence issue caused by scale differences.
Ready for the real thing?
The full course: two full-length practice tests, video lessons for every exam domain, hands-on labs and detailed explanations.
$54.99 $17.99 with code FREETEST33 — valid through August 23.
Get my $17.99 deal →NCA-GENL exam — quick answers
How much does the NCA-GENL exam cost?
The exam fee is approximately $135 and varies by region — confirm current pricing with the certification vendor before you book.
What topics are on the exam?
It covers 7 domains: Machine Learning and Neural Network Fundamentals (~14%), Transformer Architecture and Large Language Models (~15%), Prompt Engineering (~13%), Alignment, Safety and Responsible AI (~12%), Data Preprocessing, Feature Engineering, Analysis and Visualisation (~14%), Experimentation and Evaluation (~12%), LLM Integration, Deployment and the NVIDIA Software Stack (~20%). The full course has a dedicated chapter, lab and practice-test coverage for each.
Is this practice test really free?
Yes — all questions on this page are free with explanations and no sign-up. The paid Udemy course adds two full-length timed exams, video lessons and hands-on labs.
How do I get the discount?
Use code FREETEST33 at checkout for $17.99 (list $54.99) through August 23 — the enroll button applies it automatically.
Will this prepare me for the real exam?
The questions mirror the real exam's style and are mapped to the official domains. This is exam-focused preparation — combine the free test with the full course's timed simulations to gauge your readiness.