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Instant Download NVIDIA : NCA-GENL Questions & Answers as PDF & Test Engine
- Exam Code: NCA-GENL
- Exam Name: NVIDIA Generative AI LLMs
- Updated: Oct 20, 2026
- No. of Questions: 97 Questions and Answers
- Download Limit: Unlimited
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NVIDIA NCA-GENL Exam Overview:
| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA Certified Associate - Generative AI LLMs |
| Exam Number: | NCA-GENL |
| Related Certifications: | NVIDIA Certified Associate Generative AI LLMs Certification |
| Certificate Validity Period: | 2 years |
| Available Languages: | Simplified Chinese, English |
| Exam Price: | USD 125 |
| Exam Format: | Multiple-Choice, Multiple-Select |
| Exam Duration: | 60 minutes |
| Real Exam Qty: | 50-60 |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online proctored via Certiverse platform |
| Official Syllabus URL: | https://www.nvidia.com/en-us/learn/certification/generative-ai-llm-professional |
NVIDIA NCA-GENL Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Analysis and Visualization | 14% | - Data Preprocessing and Feature Engineering - Data Analysis and Visualization Methods |
| Topic 2: Experimentation | 22% | - Model Evaluation Metrics - Experiment Design and Evaluation - AI Experimentation and Testing |
| Topic 3: Trustworthy AI | 10% | - Robustness - Prompt Engineering Techniques - Model Alignment Principles - Explainability and Transparency - Content Credentials |
| Topic 4: Core Machine Learning and AI Knowledge | 30% | - Neural Networks - Large Language Model Architecture - Fundamentals of Machine Learning |
| Topic 5: Software Development | 24% | - Containerized Inference Pipelines - NVIDIA Triton Inference Server - LLM Integration and Deployment - Python Libraries for LLMs |
Your NCA-GENL Questions, Answered Around the Clock
- Trustworthy AI (10%)
- Experimentation (22%)
- Core Machine Learning and AI Knowledge (30%)
NVIDIA Generative AI LLMs Sample Questions:
Which of the following is a parameter-efficient fine-tuning approach that one can use to fine-tune LLMs in a memory-efficient fashion?
- A. TensorRT
- B. LoRA
- C. Chinchilla
- D. NeMo
Correct Answer: B 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
Which of the following best describes Word2vec?
- A. A deep learning algorithm used to generate word embeddings from text data.
- B. A database management system designed for storing and querying word data.
- C. A programming language used to build artificial intelligence models.
- D. A statistical technique used to analyze word frequency in a text corpus.
Correct Answer: A 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
In the context of developing an AI application using NVIDIA's NGC containers, how does the use of containerized environments enhance the reproducibility of LLM training and deployment workflows?
- A. Containers encapsulate dependencies and configurations, ensuring consistent execution across systems.
- B. Containers reduce the model's memory footprint by compressing the neural network.
- C. Containers automatically optimize the model's hyperparameters for better performance.
- D. Containers enable direct access to GPU hardware without driver installation.
Correct Answer: A 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
In ML applications, which machine learning algorithm is commonly used for creating new data based on existing data?
- A. Generative adversarial network
- B. K-means clustering
- C. Support vector machine
- D. Decision tree
Correct Answer: A 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
Which of the following claims is correct about TensorRT and ONNX?
- A. TensorRT is used for model deployment and ONNX is used for model interchange.
- B. TensorRT is used for model creation and ONNX is used for model interchange.
- C. TensorRT is used for model deployment and ONNX is used for model creation.
- D. TensorRT is used for model creation and ONNX is used for model deployment.
Correct Answer: A 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
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