NCA-GENL Dumps Guarantee You Pass NCA-GENL Exam Easily

As new threats and vulnerabilities emerge, the NCA-GENL exam is updated to reflect the latest trends in NVIDIA-Certified Associate. By using NCA-GENL exam dumps questions, you can ensure that you're staying up-to-date with the latest exam content and are fully prepared to address new challenges in NVIDIA Generative AI LLMs. NVIDIA NCA-GENL exam dumps questions can help you optimize your preparation and ensure that you're fully prepared for the exam. Practice free NVIDIA NCA-GENL exam dumps questions below.

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1. Which calculation is most commonly used to measure the semantic closeness of two text passages?

Hamming distance

Jaccard similarity

Cosine similarity

Euclidean distance

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2. 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

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3. 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.

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4. You have developed a deep learning model for a recommendation system. You want to evaluate the performance of the model using A/B testing.

What is the rationale for using A/B testing with deep learning model performance?

A/B testing allows for a controlled comparison between two versions of the model, helping to identify the version that performs better.

A/B testing methodologies integrate rationale and technical commentary from the designers of the deep learning model.

A/B testing ensures that the deep learning model is robust and can handle different variations of input data.

A/B testing helps in collecting comparative latency data to evaluate the performance of the deep learning model.

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5. 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.

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6. 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.

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7. Which of the following prompt engineering techniques is most effective for improving an LLM's performance on multi-step reasoning tasks?

Retrieval-augmented generation without context

Few-shot prompting with unrelated examples.

Zero-shot prompting with detailed task descriptions.

Chain-of-thought prompting with explicit intermediate steps.

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

Auto-encoder model

Siamese model

Encoder model

SVM model

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9. When fine-tuning an LLM for a specific application, why is it essential to perform exploratory data analysis (EDA) on the new training dataset?

To uncover patterns and anomalies in the dataset

To select the appropriate learning rate for the model

To assess the computing resources required for fine-tuning

To determine the optimum number of layers in the neural network

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10. In transformer-based LLMs, how does the use of multi-head attention improve model performance compared to single-head attention, particularly for complex NLP tasks?

Multi-head attention reduces the model’s memory footprint by sharing weights across heads.

Multi-head attention allows the model to focus on multiple aspects of the input sequence simultaneously.

Multi-head attention eliminates the need for positional encodings in the input sequence.

Multi-head attention simplifies the training process by reducing the number of parameters.

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11. What is the purpose of few-shot learning in prompt engineering?

To give a model some examples

To train a model from scratch

To optimize hyperparameters

To fine-tune a model on a massive dataset

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12. 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.

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13. In the context of transformer-based large language models, how does the use of layer normalization mitigate the challenges associated with training deep neural networks?

It reduces the computational complexity by normalizing the input embeddings.

It stabilizes training by normalizing the inputs to each layer, reducing internal covariate shift.

It increases the model’s capacity by adding additional parameters to each layer.

It replaces the attention mechanism to improve sequence processing efficiency.

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14. Which metric is commonly used to evaluate machine-translation models?

F1 Score

BLEU score

ROUGE score

Perplexity

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

Long sequences

Embeddings

Class tokens

Translations

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

Git

Pandas

Falcon

Triton

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