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 #31

Which of the following is an activation function used in neural networks?

A. Sigmoid function

B. Mean Squared Error function

C. Diffusion function

D. K-means clustering function

Discussion 0

Correct Answer: A Vote an answer

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Question #32

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

A. Data augmentation

B. Multi-Stream Execution

C. Residual connections

D. Layer Fusion

E. Variable learning rate

Discussion 0

Correct Answer: B,D Vote an answer

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Question #33

In the transformer architecture, what is the purpose of positional encoding?

A. To encode the semantic meaning of each token in the input sequence.

B. To encode the importance of each token in the input sequence.

C. To remove redundant information from the input sequence.

D. To add information about the order of each token in the input sequence.

Discussion 0

Correct Answer: D Vote an answer

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Question #34

Which of the following is a feature of the NVIDIA Triton Inference Server?

A. Model quantization

B. Model pruning

C. Dynamic batching

D. Gradient clipping

Discussion 0

Correct Answer: C Vote an answer

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Question #35

Which of the following claims is correct about quantization in the context of Deep Learning? (Pick the 2 correct responses)

A. Helps reduce memory requirements and achieve better cache utilization.

B. Quantization might help in saving power and reducing heat production.

C. It leads to a substantial loss of model accuracy.

D. It only involves reducing the number of bits of the parameters.

E. It consists of removing a quantity of weights whose values are zero.

Discussion 0

Correct Answer: A,B Vote an answer

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Question #36

Which of the following best describes the purpose of attention mechanisms in transformer models?

A. To compress the input sequence for faster processing.

B. To focus on relevant parts of the input sequence for use in the downstream task.

C. To convert text into numerical representations.

D. To generate random noise for improved model robustness.

Discussion 0

Correct Answer: B Vote an answer

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Question #37

Imagine you are training an LLM consisting of billions of parameters and your training dataset is significantly larger than the available RAM in your system. Which of the following would be an alternative?

A. Eliminating sentences that are syntactically different by semantically equivalent, possibly reducing the risk of the model hallucinating as it is trained to get to the point.

B. Discarding the excess of data and pruning the dataset to the capacity of the RAM, resulting in reduced latency during inference.

C. Using a memory-mapped file that allows the library to access and operate on elements of the dataset without needing to fully load it into memory.

D. Using the GPU memory to extend the RAM capacity for storing the dataset and move the dataset in and out of the GPU, using the PCI bandwidth possibly.

Discussion 0

Correct Answer: C Vote an answer

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Question #38

Which tool would you use to select training data with specific keywords?

A. Regular expression filter

B. Tableau dashboard

C. JSON parser

D. ActionScript

Discussion 0

Correct Answer: A Vote an answer

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Question #39

Which aspect in the development of ethical AI systems ensures they align with societal values and norms?

A. Achieving the highest possible level of prediction accuracy in AI models.

B. Implementing complex algorithms to enhance AI's problem-solving capabilities.

C. Developing AI systems with autonomy from human decision-making.

D. Ensuring AI systems have explicable decision-making processes.

Discussion 0

Correct Answer: D Vote an answer

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Question #40

What is the primary purpose of applying various image transformation techniques (e.g., flipping, rotation, zooming) to a dataset?

A. To artificially expand the dataset's size and improve the model's ability to generalize.

B. To reduce the computational resources required for training deep learning models.

C. To simplify the model's architecture, making it easier to interpret the results.

D. To ensure perfect alignment and uniformity across all images in the dataset.

Discussion 0

Correct Answer: A Vote an answer

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