Updated Jul-2025 Premium NCA-GENL Exam Engine pdf – Download Free Updated 53 Questions [Q22-Q44]

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Updated Jul-2025 Premium NCA-GENL Exam Engine pdf – Download Free Updated 53 Questions

Authentic NCA-GENL Dumps With 100% Passing Rate Practice Tests Dumps

NEW QUESTION 22
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.

NEW QUESTION 23
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.

NEW QUESTION 24
In the Transformer architecture, which of the following statements about the Q (query), K (key), and V (value) matrices is correct?

Q, K, and V are randomly initialized weight matrices used for positional encoding.

K is responsible for computing the attention scores between the query and key vectors.

Q represents the query vector used to retrieve relevant information from the input sequence.

V is used to calculate the positional embeddings for each token in the input sequence.

NEW QUESTION 25
Which Python library is specifically designed for working with large language models (LLMs)?

NumPy

Pandas

HuggingFace Transformers

Scikit-learn

NEW QUESTION 26
In the context of a natural language processing (NLP) application, which approach is most effectivefor 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.

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?

Cross-validation

Randomized controlled trial

Average entropy approximation

Greedy decoding

NEW QUESTION 28
In the context of machine learning model deployment, how can Docker be utilized to enhance the process?

To automatically generate features for machine learning models.

To provide a consistent environment for model training and inference.

To reduce the computational resources needed for training models.

To directly increase the accuracy of machine learning models.

NEW QUESTION 29
When preprocessing text data for an LLM fine-tuning task, why is it critical to apply subword tokenization (e.
g., Byte-Pair Encoding) instead of word-based tokenization for handling rare or out-of-vocabulary words?

Subword tokenization reduces the model’s computational complexity by eliminating embeddings.

Subword tokenization creates a fixed-size vocabulary to prevent memory overflow.

Subword tokenization breaks words into smaller units, enabling the model to generalize to unseen words.

Subword tokenization removes punctuation and special characters to simplify text input.

NEW QUESTION 30
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.

NEW QUESTION 31
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.

NEW QUESTION 32
Which calculation is most commonly used to measure the semantic closeness of two text passages?

Hamming distance

Jaccard similarity

Cosine similarity

Euclidean distance

NEW QUESTION 33
In the development of trustworthy AI systems, what is the primary purpose of implementing red-teaming exercises during the alignment process of large language models?

To optimize the model’s inference speed for production deployment.

To identify and mitigate potential biases, safety risks, and harmful outputs.

To increase the model’s parameter count for better performance.

To automate the collection of training data for fine-tuning.

NEW QUESTION 34
Which feature of the HuggingFace Transformers library makes it particularly suitable for fine-tuning large language models on NVIDIA GPUs?

Built-in support for CPU-based data preprocessing pipelines.

Seamless integration with PyTorch and TensorRT for GPU-accelerated training and inference.

Automatic conversion of models to ONNX format for cross-platform deployment.

Simplified API for classical machine learning algorithms like SVM.

NEW QUESTION 35
What are the main advantages of instructed large language models over traditional, small language models (<
300M parameters)? (Pick the 2 correct responses)

Trained without the need for labeled data.

Smaller latency, higher throughput.

It is easier to explain the predictions.

Cheaper computational costs during inference.

Single generic model can do more than one task.

NEW QUESTION 36
What are some methods to overcome limited throughput between CPU and GPU? (Pick the 2 correct responses)

Increase the clock speed of the CPU.

Using techniques like memory pooling.

Upgrade the GPU to a higher-end model.

Increase the number of CPU cores.

NEW QUESTION 37
When designing prompts for a large language model to perform a complex reasoning task, such as solving a multi-step mathematical problem, which advanced prompt engineering technique is most effective in ensuring robust performance across diverse inputs?

Zero-shot prompting with a generic task description.

Few-shot prompting with randomly selected examples.

Chain-of-thought prompting with step-by-step reasoning examples.

Retrieval-augmented generation with external mathematical databases.

NEW QUESTION 38
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.

NEW QUESTION 39
Which of the following best describes the purpose of attention mechanisms in transformer models?

To focus on relevant parts of the input sequence for use in the downstream task.

To compress the input sequence for faster processing.

To generate random noise for improved model robustness.

To convert text into numerical representations.

NEW QUESTION 40
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.

NEW QUESTION 41
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 trainingdata 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.

NEW QUESTION 42
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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Updated Jul-2025 Premium NCA-GENL Exam Engine pdf – Download Free Updated 53 Questions [Q22-Q44]

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