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NCA-GENL Exam Dumps | Valid NCA-GENL Exam Review

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NVIDIA NCA-GENL Exam Syllabus Topics:

Topic
Details

Topic 1

• Experimentation: This section of the exam measures the skills of ML Engineers and covers how to conduct structured experiments with LLMs. It involves setting up test cases, tracking performance metrics, and making informed decisions based on experimental outcomes.:

Topic 2

• This section of the exam measures skills of AI Product Developers and covers how to strategically plan experiments that validate hypotheses, compare model variations, or test model responses. It focuses on structure, controls, and variables in experimentation.

Topic 3

• Alignment: This section of the exam measures the skills of AI Policy Engineers and covers techniques to align LLM outputs with human intentions and values. It includes safety mechanisms, ethical safeguards, and tuning strategies to reduce harmful, biased, or inaccurate results from models.

Topic 4

• Prompt Engineering: This section of the exam measures the skills of Prompt Designers and covers how to craft effective prompts that guide LLMs to produce desired outputs. It focuses on prompt strategies, formatting, and iterative refinement techniques used in both development and real-world applications of LLMs.

Topic 5

• Data Analysis and Visualization: This section of the exam measures the skills of Data Scientists and covers interpreting, cleaning, and presenting data through visual storytelling. It emphasizes how to use visualization to extract insights and evaluate model behavior, performance, or training data patterns.

Topic 6

• Experiment Design

Topic 7

• LLM Integration and Deployment: This section of the exam measures skills of AI Platform Engineers and covers connecting LLMs with applications or services through APIs, and deploying them securely and efficiently at scale. It also includes considerations for latency, cost, monitoring, and updates in production environments.

Topic 8

• Python Libraries for LLMs: This section of the exam measures skills of LLM Developers and covers using Python tools and frameworks like Hugging Face Transformers, LangChain, and PyTorch to build, fine-tune, and deploy large language models. It focuses on practical implementation and ecosystem familiarity.

Topic 9

• Fundamentals of Machine Learning and Neural Networks: This section of the exam measures the skills of AI Researchers and covers the foundational principles behind machine learning and neural networks, focusing on how these concepts underpin the development of large language models (LLMs). It ensures the learner understands the basic structure and learning mechanisms involved in training generative AI systems.

Topic 10

• Software Development: This section of the exam measures the skills of Machine Learning Developers and covers writing efficient, modular, and scalable code for AI applications. It includes software engineering principles, version control, testing, and documentation practices relevant to LLM-based development.

 

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NVIDIA Generative AI LLMs Sample Questions (Q27-Q32):

NEW QUESTION # 27
You have access to training data but no access to test data. What evaluation method can you use to assess the performance of your AI model?

• A. Randomized controlled trial
• B. Cross-validation
• C. Greedy decoding
• D. Average entropy approximation

Answer: B

Explanation:
When test data is unavailable, cross-validation is the most effective method to assess an AI model's performance using only the training dataset. Cross-validation involves splitting the training data into multiple subsets (folds), training the model on some folds, and validating it on others, repeating this process to estimate generalization performance. NVIDIA's documentation on machine learning workflows, particularly in the NeMo framework for model evaluation, highlights k-fold cross-validation as a standard technique for robust performance assessment when a separate test set is not available. Option B (randomized controlled trial) is a clinical or experimental method, not typically used for model evaluation. Option C (average entropy approximation) is not a standard evaluation method. Option D (greedy decoding) is a generation strategy for LLMs, not an evaluation technique.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/model_finetuning.html
Goodfellow, I., et al. (2016). "Deep Learning." MIT Press.

 

NEW QUESTION # 28
Which of the following claims is correct about TensorRT and ONNX?

• A. TensorRT is used for model creation and ONNX is used for model interchange.
• B. TensorRT is used for model deployment and ONNX is used for model creation.
• C. TensorRT is used for model creation and ONNX is used for model deployment.
• D. TensorRT is used for model deployment and ONNX is used for model interchange.

Answer: D

Explanation:
NVIDIA TensorRT is a deep learning inference library used to optimize and deploy models for high- performance inference, while ONNX (Open Neural Network Exchange) is a format for model interchange, enabling models to be shared across different frameworks, as covered in NVIDIA's Generative AI and LLMs course. TensorRT optimizes models (e.g., via layer fusion and quantization) for deployment on NVIDIA GPUs, while ONNX ensures portability by providing a standardized model representation. Option B is incorrect, as ONNX is not used for model creation but for interchange. Option C is wrong, as TensorRT is not for model creation but optimization and deployment. Option D is inaccurate, as ONNX is not for deployment but for model sharing. The course notes: "TensorRT optimizes and deploys deep learning models for inference, while ONNX enables model interchange across frameworks for portability." References: NVIDIA Building Transformer-Based Natural Language Processing Applications course; NVIDIA Introduction to Transformer-Based Natural Language Processing.

 

NEW QUESTION # 29
You are working on developing an application to classify images of animals and need to train a neural model.
However, you have a limited amount of labeled data. Which technique can you use to leverage the knowledge from a model pre-trained on a different task to improve the performance of your new model?

• A. Early stopping
• B. Random initialization
• C. Dropout
• D. Transfer learning

Answer: D

Explanation:
Transfer learning is a technique where a model pre-trained on a large, general dataset (e.g., ImageNet for computer vision) is fine-tuned for a specific task with limited data. NVIDIA's Deep Learning AI documentation, particularly for frameworks like NeMo and TensorRT, emphasizes transfer learning as a powerful approach to improve model performance when labeled data is scarce. For example, a pre-trained convolutional neural network (CNN) can be fine-tuned for animal image classification by reusing its learned features (e.g., edge detection) and adapting the final layers to the new task. Option A (dropout) is a regularization technique, not a knowledge transfer method. Option B (random initialization) discards pre- trained knowledge. Option D (early stopping) prevents overfitting but does not leverage pre-trained models.
References:
NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp
/model_finetuning.html
NVIDIA Deep Learning AI:https://www.nvidia.com/en-us/deep-learning-ai/

 

NEW QUESTION # 30
Which of the following is an activation function used in neural networks?

• A. Diffusion function
• B. Sigmoid function
• C. Mean Squared Error function
• D. K-means clustering function

Answer: B

Explanation:
The sigmoid function is a widely used activation function in neural networks, as covered in NVIDIA's Generative AI and LLMs course. It maps input values to a range between 0 and 1, making it particularly useful for binary classification tasks and as a non-linear activation in early neural network architectures. The sigmoid function, defined as f(x) = 1 / (1 + e

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