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NCA-GENM certification training: NVIDIA Generative AI Multimodal & NCA-GENM study guide

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NVIDIA Generative AI Multimodal Sample Questions (Q30-Q35):

NEW QUESTION # 30
Consider a scenario where you are evaluating the performance of a multimodal A1 model that generates descriptions for images. However, the generated descriptions tend to be repetitive and lack diversity. Which of the following techniques can be employed to address this issue and encourage more diverse and creative outputs from the model? (Select TWO)

A. Utilizing a temperature scaling parameter during decoding and increasing its value.
B. Employing a nucleus sampling (top-p sampling) decoding strategy.
C. Using a beam search decoding strategy with a small beam width.
D. Using a greedy decoding strategy.
E. Increasing the model's training data size.

Answer: A,B

Explanation:
Nucleus sampling (top-p sampling) randomly samples from the smallest set of words whose cumulative probability mass exceeds a threshold p, encouraging more diverse outputs. Increasing the temperature scaling parameter makes the probability distribution flatter, leading to more exploration and less predictable (more creative) outputs. Beam search with a small beam width might still result in repetitive outputs. Increasing the training data size can help, but it might not directly address the lack of diversity. Greedy decoding always selects the most probable word, leading to repetitive and predictable outputs.

NEW QUESTION # 31
Which prompt engineering technique is most likely to improve the coherence and visual quality of images generated by a text-to-image model when generating complex scenes with multiple objects and intricate details?

A. Using only abstract and ambiguous language.
B. Employing a negative prompt to specify elements to avoid.
C. Using short, concise prompts with only a few keywords.
D. Exclusively describing the background and neglecting foreground elements.
E. Relying solely on the model's default style settings.

Answer: B

Explanation:
Employing a negative prompt allows you to explicitly instruct the model to avoid specific elements or artifacts that might degrade the visual quality of the generated image. This technique is particularly effective for complex scenes where you want to fine-tune the composition and appearance by preventing unwanted details or styles. Using shorter prompts or relying only on default styles is too general. Vague descriptions can lead to unpredictable outputs.

NEW QUESTION # 32
You are working with a pre-trained multimodal model that takes images and text as input. You want to fine-tune this model for a specific downstream task, but you have limited computational resources. Which of the following techniques would be most effective for reducing the memory footprint and computational cost during fine-tuning?

A. Fine-tuning the entire model with a small learning rate.
B. Freezing all layers of the pre-trained model and training only a small classification head.
C. Using quantization to reduce the precision of the model's weights and activations.
D. Applying knowledge distillation, where a smaller student model is trained to mimic the behavior of the pre-trained model.
E. Increasing the batch size to utilize the available memory more efficiently.

Answer: C,D

Explanation:
Quantization reduces the memory footprint of the model by using lower-precision representations for weights and activations. Knowledge distillation allows you to train a smaller, more efficient model that performs similarly to the larger pre-trained model. Freezing layers reduces the number of trainable parameters but may limit the model's ability to adapt to the new task. Fine-tuning the entire model, even with a small learning rate, is computationally expensive. Increasing batch size might lead to Out of Memory errors.

NEW QUESTION # 33
You are building a multimodal application that needs to understand both image and text dat a. You want to use a pre-trained model but fine-tune it for your specific task. Which of the following strategies is MOST effective for fine-tuning a large pre-trained multimodal model?

A. Fine-tune the attention mechanism between the text and image encoders, while keeping the encoder weights frozen.
B. Fine-tune only the image encoder layers, keeping the text encoder layers frozen.
C. Train a new classification head from scratch on top of the frozen pre-trained model.
D. Fine-tune only the text encoder layers, keeping the image encoder layers frozen.
E. Fine-tune the entire model, including both text and image encoder layers, using a small learning rate.

Answer: E

Explanation:
Fine-tuning the entire model with a small learning rate allows the model to adapt to the specific nuances of the new task while leveraging the knowledge already learned during pre-training. Freezing layers can limit adaptability. Training only a new head might not fully utilize the pre-trained features.

NEW QUESTION # 34
You are building a real-time multimodal application that requires processing both audio and video streams simultaneously. You need to minimize the latency of the system while maximizing throughput. Which of the following hardware and software optimizations would be most effective?

A. Using separate GPUs for audio and video processing and employing asynchronous data transfer techniques.
B. Using a CPU-based implementation for both audio and video processing.
C. Compressing the audio and video streams aggressively to reduce the amount of data that needs to be processed.
D. Offloading both audio and video processing to a single high-end GPIJ.
E. Using a high-latency, high-bandwidth network connection.

Answer: A

Explanation:
Using separate GPUs allows for parallel processing of audio and video streams. Asynchronous data transfer techniques minimize latency by allowing the CPU to continue processing other tasks while data is being transferred to the GPUs. While a single high-end GPU could handle both tasks, using separate GPUs maximizes parallelism. CPU-based implementations are generally slower than GPU-based implementations for multimedia processing. Aggressive compression can reduce data size but may also introduce artifacts and reduce the quality of the output. High-latency networks are detrimental to real-time applications.

NEW QUESTION # 35
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