100% Real NCA-GENL dumps – Brilliant NCA-GENL Exam Questions PDF [Q50-Q73]

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100% Real NCA-GENL dumps – Brilliant NCA-GENL Exam Questions PDF

NCA-GENL Exam PDF [2026] Tests Free Updated Today with Correct 97 Questions

NVIDIA NCA-GENL Exam Syllabus Topics:

Topic	Details
Topic 1	Python libraries for LLMs: Covers key Python frameworks and tools — such as LangChain, Hugging Face, and similar libraries — used to build and interact with LLMs.
Topic 2	Prompt engineering: Focuses on techniques for designing and refining input prompts to effectively guide LLM outputs toward desired results.
Topic 3	Alignment: Addresses methods for ensuring LLM behavior is safe, accurate, and consistent with human intentions and values.
Topic 4	Experiment design: Focuses on structuring controlled tests and workflows to systematically evaluate LLM performance and outcomes.
Topic 5	Software development: Covers the programming practices and coding skills required to build, maintain, and deploy generative AI applications.

NO.50 Which metric is commonly used to evaluate machine-translation models?

F1 Score

BLEU score

ROUGE score

Perplexity

NO.51 Transformers are useful for language modeling because their architecture is uniquely suited for handling which of the following?

Long sequences

Embeddings

Class tokens

Translations

NO.52 Which technique is used in prompt engineering to guide LLMs in generating more accurate and contextually appropriate responses?

Training the model with additional data.

Choosing another model architecture.

Increasing the model’s parameter count.

Leveraging the system message.

NO.53 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.

NO.54 Which of the following optimizations are provided by TensorRT? (Choose two.)

Data augmentation

Variable learning rate

Multi-Stream Execution

Layer Fusion

Residual connections

NO.55 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.56 Which of the following is a key characteristic of Rapid Application Development (RAD)?

Linear progression through predefined project phases.

Extensive upfront planning before any development.

Iterative prototyping with active user involvement.

Minimal user feedback during the development process.

NO.57 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.

NO.58 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.

NO.59 You are in need of customizing your LLM via prompt engineering, prompt learning, or parameter-efficient fine-tuning. Which framework helps you with all of these?

NVIDIA TensorRT

NVIDIA DALI

NVIDIA Triton

NVIDIA NeMo

NO.60 In the context of developing an AI application using NVIDIA’s NGC containers, how does the use of containerized environments enhance the reproducibility of LLM training and deployment workflows?

Containers automatically optimize the model’s hyperparameters for better performance.

Containers encapsulate dependencies and configurations, ensuring consistent execution across systems.

Containers reduce the model’s memory footprint by compressing the neural network.

Containers enable direct access to GPU hardware without driver installation.

NO.61 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.

NO.62 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.63 Which technology will allow you to deploy an LLM for production application?

Git

Pandas

Falcon

Triton

NO.64 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.

NO.65 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.

NO.66 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.67 In the context of transformer-based large language models, how does the use of layer normalization mitigate the challenges associated with training deep neural networks?

It reduces the computational complexity by normalizing the input embeddings.

It stabilizes training by normalizing the inputs to each layer, reducing internal covariate shift.

It increases the model’s capacity by adding additional parameters to each layer.

It replaces the attention mechanism to improve sequence processing efficiency.

NO.68 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.69 What distinguishes BLEU scores from ROUGE scores when evaluating natural language processing models?

BLEU scores determine the fluency of text generation, while ROUGE scores rate the uniqueness of generated text.

BLEU scores analyze syntactic structures, while ROUGE scores evaluate semantic accuracy.

BLEU scores evaluate the ‘precision’ of translations, while ROUGE scores focus on the ‘recall’ of summarized text.

BLEU scores measure model efficiency, whereas ROUGE scores assess computational complexity.

NO.70 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.71 Your company has upgraded from a legacy LLM model to a new model that allows for larger sequences and higher token limits. What is the most likely result of upgrading to the new model?

The number of tokens is fixed for all existing language models, so there is no benefit to upgrading to higher token limits.

The newer model allows for larger context, so the outputs will improve without increasing inference time overhead.

The newer model allows the same context lengths, but the larger token limit will result in more comprehensive and longer outputs with more detail.

The newer model allows larger context, so outputs will improve, but you will likely incur longer inference times.

NO.72 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.73 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.