NCA-GENL Questions PDF [2025] Use Valid New dump to Clear Exam [Q11-Q34]

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NCA-GENL Questions PDF [2025] Use Valid New dump to Clear Exam

Passing NVIDIA NCA-GENL Exam Using 2025 Practice Tests

NVIDIA NCA-GENL Exam Syllabus Topics:

Topic	Details
Topic 1	Data Analysis and Visualization: This section of the exam measures the skills of Data Scientists and covers interpreting, cleaning, and presenting data through visual storytelling. It emphasizes how to use visualization to extract insights and evaluate model behavior, performance, or training data patterns.
Topic 2	This section of the exam measures skills of AI Product Developers and covers how to strategically plan experiments that validate hypotheses, compare model variations, or test model responses. It focuses on structure, controls, and variables in experimentation.
Topic 3	Experiment Design
Topic 4	Alignment: This section of the exam measures the skills of AI Policy Engineers and covers techniques to align LLM outputs with human intentions and values. It includes safety mechanisms, ethical safeguards, and tuning strategies to reduce harmful, biased, or inaccurate results from models.
Topic 5	Data Preprocessing and Feature Engineering: This section of the exam measures the skills of Data Engineers and covers preparing raw data into usable formats for model training or fine-tuning. It includes cleaning, normalizing, tokenizing, and feature extraction methods essential to building robust LLM pipelines.
Topic 6	LLM Integration and Deployment: This section of the exam measures skills of AI Platform Engineers and covers connecting LLMs with applications or services through APIs, and deploying them securely and efficiently at scale. It also includes considerations for latency, cost, monitoring, and updates in production environments.
Topic 7	Fundamentals of Machine Learning and Neural Networks: This section of the exam measures the skills of AI Researchers and covers the foundational principles behind machine learning and neural networks, focusing on how these concepts underpin the development of large language models (LLMs). It ensures the learner understands the basic structure and learning mechanisms involved in training generative AI systems.

NO.11 Which tool would you use to select training data with specific keywords?

ActionScript

Tableau dashboard

JSON parser

Regular expression filter

NO.12 In Exploratory Data Analysis (EDA) for Natural Language Understanding (NLU), which method is essential for understanding the contextual relationship between words in textual data?

Computing the frequency of individual words to identify the most common terms in a text.

Applying sentiment analysis to gauge the overall sentiment expressed in a text.

Generating word clouds to visually represent word frequency and highlight key terms.

Creating n-gram models to analyze patterns of word sequences like bigrams and trigrams.

NO.13 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.

NO.14 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.

NO.15 What statement best describes the diffusion models in generative AI?

Diffusion models are probabilistic generative models that progressively inject noise into data, then learn to reverse this process for sample generation.

Diffusion models are discriminative models that use gradient-based optimization algorithms to classify data points.

Diffusion models are unsupervised models that use clustering algorithms to group similar data points together.

Diffusion models are generative models that use a transformer architecture to learn the underlying probability distribution of the data.

NO.16 Which library is used to accelerate data preparation operations on the GPU?

cuML

XGBoost

cuDF

cuGraph

NO.17 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.

NO.18 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.

NO.19 Which model deployment framework is used to deploy an NLP project, especially for high-performance inference in production environments?

NVIDIA DeepStream

HuggingFace

NeMo

NVIDIA Triton

NO.20 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.

NO.21 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.

NO.22 Which technology will allow you to deploy an LLM for production application?

Git

Pandas

Falcon

Triton

NO.23 In the context of preparing a multilingual dataset for fine-tuning an LLM, which preprocessing technique is most effective for handling text from diverse scripts (e.g., Latin, Cyrillic, Devanagari) to ensure consistent model performance?

Normalizing all text to a single script using transliteration.

Applying Unicode normalization to standardize character encodings.

Removing all non-Latin characters to simplify the input.

Converting text to phonetic representations for cross-lingual alignment.

NO.24 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.

NO.25 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.

NO.26 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

NO.27 In the context of preparing a multilingual dataset for fine-tuning an LLM, which preprocessing technique is most effective for handling text from diverse scripts (e.g., Latin, Cyrillic, Devanagari) to ensure consistent model performance?

Normalizing all text to a single script using transliteration.

Applying Unicode normalization to standardize character encodings.

Removing all non-Latin characters to simplify the input.

Converting text to phonetic representations for cross-lingual alignment.

NO.28 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.

NO.29 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.

NO.30 Which Python library is specifically designed for working with large language models (LLMs)?

NumPy

Pandas

HuggingFace Transformers

Scikit-learn

NO.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.

NO.32 When using NVIDIA RAPIDS to accelerate data preprocessing for an LLM fine-tuning pipeline, which specific feature of RAPIDS cuDF enables faster data manipulation compared to traditional CPU-based Pandas?

Automatic parallelization of Python code across CPU cores.

GPU-accelerated columnar data processing with zero-copy memory access.

Integration with cloud-based storage for distributed data access.

Conversion of Pandas DataFrames to SQL tables for faster querying.

NO.33 Which metric is commonly used to evaluate machine-translation models?

F1 Score

BLEU score

ROUGE score

Perplexity

NO.34 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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NCA-GENL Questions PDF [2025] Use Valid New dump to Clear Exam [Q11-Q34]

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