NCA-GENL NVIDIA Generative AI LLMs Exam Free Dumps

dumps2025-05-29T11:40:21+08:00

To become NVIDIA-Certified Associate Generative AI LLMs certified, you need to take and pass NCA-GENL exam. This certification validates the foundational concepts for developing, integrating, and maintaining AI-driven applications using generative AI and large language models (LLMs) with NVIDIA solutions. There are 50-60 multiple-choice questions in real NCA-GENL exam, and you have 1 hour to answer all the questions. The exam price is $125. The available language is English. We provide the latest NVIDIA Generative AI LLMs exam dumps, which can ensure you pass. Free NVIDIA Generative AI LLMs NCA-GENL exam dumps are available below.

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1. Why do we need positional encoding in transformer-based models?

To represent the order of elements in a sequence.

To prevent overfitting of the model.

To reduce the dimensionality of the input data.

To increase the throughput of the model.

2. What is Retrieval Augmented Generation (RAG)?

RAG is an architecture used to optimize the output of an LLM by retraining the model with domain-specific data.

RAG is a methodology that combines an information retrieval component with a response generator.

RAG is a method for manipulating and generating text-based data using Transformer-based LLMs.

RAG is a technique used to fine-tune pre-trained LLMs for improved performance.

3. In the context of fine-tuning LLMs, which of the following metrics is most commonly used to assess the performance of a fine-tuned model?

Model size

Accuracy on a validation set

Training duration

Number of layers

4. Which of the following claims is correct about quantization in the context of Deep Learning? (Pick the 2 correct responses)

Quantization might help in saving power and reducing heat production.

It consists of removing a quantity of weights whose values are zero.

It leads to a substantial loss of model accuracy.

Helps reduce memory requirements and achieve better cache utilization.

It only involves reducing the number of bits of the parameters.

5. What is the primary purpose of applying various image transformation techniques (e.g., flipping, rotation, zooming) to a dataset?

To simplify the model's architecture, making it easier to interpret the results.

To artificially expand the dataset's size and improve the model's ability to generalize.

To ensure perfect alignment and uniformity across all images in the dataset.

To reduce the computational resources required for training deep learning models.

6. Which technique is used in prompt engineering to guide LLMs in generating more accurate and contextually appropriate responses?

Training the model with additional data.

Choosing another model architecture.

Increasing the model's parameter count.

Leveraging the system message.

7. What are some methods to overcome limited throughput between CPU and GPU? (Pick the 2 correct responses)

Increase the clock speed of the CP

Using techniques like memory pooling.

Upgrade the GPU to a higher-end model.

Increase the number of CPU cores.

8. What is 'chunking' in Retrieval-Augmented Generation (RAG)?

Rewrite blocks of text to fill a context window.

A method used in RAG to generate random text.

A concept in RAG that refers to the training of large language models.

A technique used in RAG to split text into meaningful segments.

9. How does A/B testing contribute to the optimization of deep learning models' performance and effectiveness in real-world applications? (Pick the 2 correct responses)

A/B testing helps validate the impact of changes or updates to deep learning models by statistically analyzing the outcomes of different versions to make informed decisions for model optimization.

A/B testing allows for the comparison of different model configurations or hyperparameters to identify the most effective setup for improved performance.

A/B testing in deep learning models is primarily used for selecting the best training dataset without requiring a model architecture or parameters.

A/B testing guarantees immediate performance improvements in deep learning models without the need for further analysis or experimentation.

A/B testing is irrelevant in deep learning as it only applies to traditional statistical analysis and not complex neural network models.

10. 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?

Dropout

Random initialization

Transfer learning

Early stopping

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11. What is the fundamental role of LangChain in an LLM workflow?

To act as a replacement for traditional programming languages.

To reduce the size of AI foundation models.

To orchestrate LLM components into complex workflows.

To directly manage the hardware resources used by LLMs.

12. What type of model would you use in emotion classification tasks?

Auto-encoder model

Siamese model

Encoder model

SVM model

13. In the context of a natural language processing (NLP) application, which approach is most effective for implementing zero-shot learning to classify text data into categories that were not seen during training?

Use rule-based systems to manually define the characteristics of each category.

Use a large, labeled dataset for each possible category.

Train the new model from scratch for each new category encountered.

Use a pre-trained language model with semantic embeddings.

14. Which technology will allow you to deploy an LLM for production application?

Git

Pandas

Falcon

Triton

15. Which Python library is specifically designed for working with large language models (LLMs)?

NumPy

Pandas

HuggingFace Transformers

Scikit-learn

16. Transformers are useful for language modeling because their architecture is uniquely suited for handling which of the following?

Long sequences

Embeddings

Class tokens

Translations

NCA-GENL NVIDIA Generative AI LLMs Exam Free Dumps

NCA-GENL NVIDIA Generative AI LLMs Exam Free Dumps

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