Free NVIDIA NCA-GENL Practice Test & Real Exam Questions

Exam Code/Number: NCA-GENL
Exam Name/Title: NVIDIA Generative AI LLMs
Certification Provider: NVIDIA
Corresponding Certification: NVIDIA-Certified Associate

Exam Questions: 97
Updated On: Jun 27, 2026

Question #41

In the context of a natural language processing (NLP) application, which approach is most effective for implementing zero-shot learning to classify text data into categories that were not seen during training?

A. Use a large, labeled dataset for each possible category.

B. Train the new model from scratch for each new category encountered.

C. Use rule-based systems to manually define the characteristics of each category.

D. Use a pre-trained language model with semantic embeddings.

Discussion 0

Correct Answer: D Vote an answer

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Question #42

When comparing and contrasting the ReLU and sigmoid activation functions, which statement is true?

A. ReLU and sigmoid both have a range of 0 to 1.

B. ReLU is a linear function while sigmoid is non-linear.

C. ReLU is less computationally efficient than sigmoid, but it is more accurate than sigmoid.

D. ReLU is more computationally efficient, but sigmoid is better for predicting probabilities.

Discussion 0

Correct Answer: D Vote an answer

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Question #43

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?

A. Containers automatically optimize the model's hyperparameters for better performance.

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

C. Containers enable direct access to GPU hardware without driver installation.

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

Discussion 0

Correct Answer: B Vote an answer

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Question #44

In the context of data preprocessing for Large Language Models (LLMs), what does tokenization refer to?

A. Converting text into numerical representations.

B. Removing stop words from the text.

C. Splitting text into smaller units like words or subwords.

D. Applying data augmentation techniques to generate more training data.

Discussion 0

Correct Answer: C Vote an answer

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Question #45

You are working on developing an application to classify images of animals and need to train a neural model.
However, you have a limited amount of labeled data. Which technique can you use to leverage the knowledge from a model pre-trained on a different task to improve the performance of your new model?

A. Random initialization

B. Early stopping

C. Dropout

D. Transfer learning

Discussion 0

Correct Answer: D Vote an answer

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Question #46

What is the main consequence of the scaling law in deep learning for real-world applications?

A. The best performing model can be established even in the small data region.

B. Small and medium error regions can approach the results of the big data region.

C. With more data, it is possible to exceed the irreducible error region.

D. In the power-law region, with more data it is possible to achieve better results.

Discussion 0

Correct Answer: D Vote an answer

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Question #47

Which metric is commonly used to evaluate machine-translation models?

A. Perplexity

B. ROUGE score

C. BLEU score

D. F1 Score

Discussion 0

Correct Answer: B Vote an answer

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Question #48

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

A. To provide a consistent environment for model training and inference.

B. To reduce the computational resources needed for training models.

C. To directly increase the accuracy of machine learning models.

D. To automatically generate features for machine learning models.

Discussion 0

Correct Answer: A Vote an answer

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Question #49

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

A. Upgrade the GPU to a higher-end model.

B. Increase the number of CPU cores.

C. Increase the clock speed of the CPU.

D. Using techniques like memory pooling.

Discussion 0

Correct Answer: A,D Vote an answer

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Question #50

Which of the following best describes Word2vec?

A. A deep learning algorithm used to generate word embeddings from text data.

B. A programming language used to build artificial intelligence models.

C. A statistical technique used to analyze word frequency in a text corpus.

D. A database management system designed for storing and querying word data.

Discussion 0

Correct Answer: A Vote an answer

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