NCA-GENL Premium Files Updated Sep-2026 Practice Valid Exam Dumps Question [Q38-Q62]

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NCA-GENL Premium Files Updated Sep-2026 Practice Valid Exam Dumps Question

Practice with NCA-GENL Dumps for NVIDIA-Certified Associate Certified Exam Questions & Answer

NVIDIA NCA-GENL Exam Syllabus Topics:

Section	Objectives
Core Machine Learning and AI Knowledge	– Transformer architecture and LLM basics – Prompt engineering basics – Neural networks fundamentals
Data Analysis and Visualization	– Data preprocessing – Feature engineering and augmentation
Software Development	– Python for AI/LLMs – LLM integration and deployment basics
Experimentation	– Experiment design – Model evaluation and iteration
Trustworthy AI	– AI safety and responsible AI principles – Bias and hallucination awareness

NEW QUESTION 38
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 39
In large-language models, what is the purpose of the attention mechanism?

To measure the importance of the words in the output sequence.

To determine the order in which words are generated.

To capture the order of the words in the input sequence.

To assign weights to each word in the input sequence.

NEW QUESTION 40
In the evaluation of Natural Language Processing (NLP) systems, what do ‘validity’ and ‘reliability’ imply regarding the selection of evaluation metrics?

Validity involves the metric’s ability to predict future trends in data, and reliability refers to its capacity to integrate with multiple data sources.

Validity ensures the metric accurately reflects the intended property to measure, while reliability ensures consistent results over repeated measurements.

Validity is concerned with the metric’s computational cost, while reliability is about its applicability across different NLP platforms.

Validity refers to the speed of metric computation, whereas reliability pertains to the metric’s performance in high-volume data processing.

NEW QUESTION 41
How can Retrieval Augmented Generation (RAG) help developers to build a trustworthy AI system?

RAG can enhance the security features of AI systems, ensuring confidential computing and encrypted traffic.

RAG can improve the energy efficiency of AI systems, reducing their environmental impact and cooling requirements.

RAG can align AI models with one another, improving the accuracy of AI systems through cross- checking.

RAG can generate responses that cite reference material from an external knowledge base, ensuring transparency and verifiability.

NEW QUESTION 42
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 43
What is the main difference between forward diffusion and reverse diffusion in diffusion models of Generative AI?

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

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 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 44
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.

NEW QUESTION 45
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

NEW QUESTION 46
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 47
Which of the following tasks is a primary application of XGBoost and cuML?

Inspecting, cleansing, and transforming data

Performing GPU-accelerated machine learning tasks

Training deep learning models

Data visualization and analysis

NEW QUESTION 48
Which of the following principles are widely recognized for building trustworthy AI? (Choose two.)

Conversational

Low latency

Privacy

Scalability

Nondiscrimination

NEW QUESTION 49
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.

NEW QUESTION 50
In ML applications, which machine learning algorithm is commonly used for creating new data based on existing data?

Decision tree

Support vector machine

Generative adversarial network

K-means clustering

NEW QUESTION 51
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 52
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 53
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.

NEW QUESTION 54
Which of the following optimizations are provided by TensorRT? (Choose two.)

Data augmentation

Variable learning rate

Multi-Stream Execution

Layer Fusion

Residual connections

NEW QUESTION 55
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.

NEW QUESTION 56
Transformers are useful for language modeling because their architecture is uniquely suited for handling which of the following?

Translations

Embeddings

Long sequences

Class tokens

NEW QUESTION 57
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 58
Which principle of Trustworthy AI primarily concerns the ethical implications of AI’s impact on society and includes considerations for both potential misuse and unintended consequences?

Certification

Data Privacy

Accountability

Legal Responsibility

NEW QUESTION 59
What is the purpose of the NVIDIA NeMo Toolkit?

NeMo focuses on the morphology of a language by studying its words, and how they are formed.

NeMo helps researchers to develop models that trade-off size with minimum loss impact.

NeMo facilitates the creation of models for speech recognition and natural language understanding.

NeMo helps researchers develop state-of-the-art models for computer vision based on convolutions.

NEW QUESTION 60
What metrics would you use to evaluate the performance of a RAG workflow in terms of the accuracy of responses generated in relation to the input query? (Choose two.)

Generator latency

Retriever latency

Tokens generated per second

Response relevancy

Context precision

NEW QUESTION 61
Transformers are useful for language modeling because their architecture is uniquely suited for handling which of the following?

Long sequences

Embeddings

Class tokens

Translations

NEW QUESTION 62
Which of the following contributes to the ability of RAPIDS to accelerate data processing? (Pick the 2 correct responses)

Ensuring that CPUs are running at full clock speed.

Subsampling datasets to provide rapid but approximate answers.

Using the GPU for parallel processing of data.

Enabling data processing to scale to multiple GPUs.

Providing more memory for data analysis.

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NVIDIA NCA-GENL Exam Syllabus Topics:

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