NVIDIA NCA-GENL Exam Questions Simulate Actual NCA-GENL Exam

NCA-GENL exam dumps questions are designed to simulate the actual exam. This means that you will get a feel for the types of questions you can expect to see on the exam, as well as the format and difficulty level. In addition, NVIDIA-Certified Associate NCA-GENL dumps are often accompanied by detailed explanations and answers. This means that if you get a question wrong, you can learn from your mistake and understand why the correct answer is the right one. Test free online NCA-GENL exam dumps below.

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1. Which of the following is a key characteristic of Rapid Application Development (RAD)?

Iterative prototyping with active user involvement.

Extensive upfront planning before any development.

Linear progression through predefined project phases.

Minimal user feedback during the development process.

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2. 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.

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3. In the context of language models, what does an autoregressive model predict?

The probability of the next token in a text given the previous tokens.

The probability of the next token using a Monte Carlo sampling of past tokens.

The next token solely using recurrent network or LSTM cells.

The probability of the next token by looking at the previous and future input tokens.

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

NumPy

Pandas

HuggingFace Transformers

Scikit-learn

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

Auto-encoder model

Siamese model

Encoder model

SVM model

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7. Which of the following optimizations are provided by TensorRT? (Choose two.)

Data augmentation

Variable learning rate

Multi-Stream Execution

Layer Fusion

Residual connections

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8. In neural networks, the vanishing gradient problem refers to what problem or issue?

The problem of overfitting in neural networks, where the model performs well on the training data but poorly on new, unseen data.

The issue of gradients becoming too large during backpropagation, leading to unstable training.

The problem of underfitting in neural networks, where the model fails to capture the underlying patterns in the data.

The issue of gradients becoming too small during backpropagation, resulting in slow convergence or stagnation of the training process.

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9. Which of the following is a feature of the NVIDIA Triton Inference Server?

Model quantization

Dynamic batching

Gradient clipping

Model pruning

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10. 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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11. Which technique is designed to train a deep learning model by adjusting the weights of the neural network based on the error between the predicted and actual outputs?

Gradient Boosting

Principal Component Analysis

K-means Clustering

Backpropagation

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12. When should one use data clustering and visualization techniques such as tSNE or UMAP?

When there is a need to handle missing values and impute them in the dataset.

When there is a need to perform regression analysis and predict continuous numerical values.

When there is a need to reduce the dimensionality of the data and visualize the clusters in a lower-dimensional space.

When there is a need to perform feature extraction and identify important variables in the dataset.

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13. What is the purpose of the NVIDIA NGC catalog?

To provide a platform for testing and debugging software applications.

To provide a platform for developers to collaborate and share software development projects.

To provide a marketplace for buying and selling software development tools and resources.

To provide a curated collection of GPU-optimized AI and data science software.

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14. What is the correct order of steps in an ML project?

Model evaluation, Data preprocessing, Model training, Data collection

Model evaluation, Data collection, Data preprocessing, Model training

Data preprocessing, Data collection, Model training, Model evaluation

Data collection, Data preprocessing, Model training, Model evaluation

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15. In evaluating the transformer model for translation tasks, what is a common approach to assess its performance?

Analyzing the lexical diversity of the model’s translations compared to source texts.

Comparing the model’s output with human-generated translations on a standard dataset.

Evaluating the consistency of translation tone and style across different genres of text.

Measuring the syntactic complexity of the model’s translations against a corpus of professional translations.

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16. 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.

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17. What is the main difference between forward diffusion and reverse diffusion in diffusion models of Generative AI?

Forward diffusion focuses on generating a sample from a given noise vector, while reverse diffusion reverses the process by estimating the latent space representation of a given sample.

Forward diffusion uses feed-forward networks, while reverse diffusion uses recurrent networks.

Forward diffusion uses bottom-up processing, while reverse diffusion uses top-down processing to generate samples from noise vectors.

Forward diffusion focuses on progressively injecting noise into data, while reverse diffusion focuses on generating new samples from the given noise vectors.

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18. 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.

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19. 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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20. Why might stemming or lemmatizing text be considered a beneficial preprocessing step in the context of computing TF-IDF vectors for a corpus?

It reduces the number of unique tokens by collapsing variant forms of a word into their root form, potentially decreasing noise in the data.

It enhances the aesthetic appeal of the text, making it easier for readers to understand the document’s content.

It increases the complexity of the dataset by introducing more unique tokens, enhancing the distinctiveness of each document.

It guarantees an increase in the accuracy of TF-IDF vectors by ensuring more precise word usage distinction.

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21. What is the main consequence of the scaling law in deep learning for real-world applications?

With more data, it is possible to exceed the irreducible error region.

The best performing model can be established even in the small data region.

Small and medium error regions can approach the results of the big data region.

In the power-law region, with more data it is possible to achieve better results.

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22. Which of the following principles are widely recognized for building trustworthy AI? (Choose two.)

Conversational

Low latency

Privacy

Scalability

Nondiscrimination

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23. 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.

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24. In Natural Language Processing, there are a group of steps in problem formulation collectively known as word representations (also word embeddings).

Which of the following are Deep Learning models that can be used to produce these representations for NLP tasks? (Choose two.)

Word2vec

WordNet

Kubernetes

TensorRT

BERT

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25. 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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26. What is a Tokenizer in Large Language Models (LLM)?

A method to remove stop words and punctuation marks from text data.

A machine learning algorithm that predicts the next word/token in a sequence of text.

A tool used to split text into smaller units called tokens for analysis and processing.

A technique used to convert text data into numerical representations called tokens for machine learning.

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27. In the context of evaluating a fine-tuned LLM for a text classification task, which experimental design technique ensures robust performance estimation when dealing with imbalanced datasets?

Single hold-out validation with a fixed test set.

Stratified k-fold cross-validation.

Bootstrapping with random sampling.

Grid search for hyperparameter tuning.

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28. Which aspect in the development of ethical AI systems ensures they align with societal values and norms?

Achieving the highest possible level of prediction accuracy in AI models.

Implementing complex algorithms to enhance AI’s problem-solving capabilities.

Developing AI systems with autonomy from human decision-making.

Ensuring AI systems have explicable decision-making processes.

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29. In the field of AI experimentation, what is the GLUE benchmark used to evaluate performance of?

AI models on speech recognition tasks.

AI models on image recognition tasks.

AI models on a range of natural language understanding tasks.

AI models on reinforcement learning tasks.

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