Real NCA-GENL Dumps – NVIDIA Correct Answers updated on 2026 [Q51-Q69]

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NVIDIA-Certified Associate NCA-GENL Exam Practice Dumps

NVIDIA NCA-GENL Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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.
Topic 3	Software Development: This section of the exam measures the skills of Machine Learning Developers and covers writing efficient, modular, and scalable code for AI applications. It includes software engineering principles, version control, testing, and documentation practices relevant to LLM-based development.
Topic 4	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 5	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 6	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.

NO.51 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.

NO.52 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

NO.53 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.

NO.54 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.

NO.55 Which of the following is an activation function used in neural networks?

Sigmoid function

K-means clustering function

Mean Squared Error function

Diffusion function

NO.56 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.

NO.57 You are using RAPIDS and Python for a data analysis project. Which pair of statements best explains how RAPIDS accelerates data science?

RAPIDS enables on-GPU processing of computationally expensive calculations and minimizes CPU- GPU memory transfers.

RAPIDS is a Python library that provides functions to accelerate the PCIe bus throughput via word- doubling.

RAPIDS provides lossless compression of CPU-GPU memory transfers to speed up data analysis.

NO.58 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 bystatistically 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.59 Which metric is primarily used to evaluate the quality of the text generated by language models?

Perplexity

Precision

Recall

Accuracy

NO.60 When designing an experiment to compare the performance of two LLMs on a question-answering task, which statistical test is most appropriate to determine if the difference in their accuracy is significant, assuming the data follows a normal distribution?

Chi-squared test

Paired t-test

Mann-Whitney U test

ANOVA test

NO.61 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

NO.62 In the development of Trustworthy AI, what is the significance of ‘Certification’ as a principle?

It ensures that AI systems are transparent in their decision-making processes.

It requires AI systems to be developed with an ethical consideration for societal impacts.

It involves verifying that AI models are fit for their intended purpose according to regional or industry- specific standards.

It mandates that AI models comply with relevant laws and regulations specific to their deployment region and industry.

NO.63 When implementing data parallel training, which of the following considerations needs to be taken into account?

The model weights are synced across all processes/devices only at the end of every epoch.

A master-worker method for syncing the weights across different processes is desirable due to its scalability.

A ring all-reduce is an efficient algorithm for syncing the weights across different processes/devices.

The model weights are kept independent for as long as possible increasing the model exploration.

NO.64 Which calculation is most commonly used to measure the semantic closeness of two text passages?

Hamming distance

Jaccard similarity

Cosine similarity

Euclidean distance

NO.65 Which of the following claims is correct about TensorRT and ONNX?

TensorRT is used for model deployment and ONNX is used for model interchange.

TensorRT is used for model deployment and ONNX is used for model creation.

TensorRT is used for model creation and ONNX is used for model interchange.

TensorRT is used for model creation and ONNX is used for model deployment.

NO.66 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

NO.67 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.68 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.69 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.

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Real NCA-GENL Dumps – NVIDIA Correct Answers updated on 2026 [Q51-Q69]

NVIDIA NCA-GENL Exam Syllabus Topics:

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