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NCA-GENM Exam PDF - Latest NCA-GENM Test Practice

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NVIDIA Generative AI Multimodal Sample Questions (Q117-Q122):

NEW QUESTION # 117
You are tasked with deploying a generative A1 model for image inpainting using Triton Inference Server. The model requires significant GPU memory and you want to maximize throughput. Which Triton configuration parameters would be MOST important to tune, and why?

A. 'optimization' (setting strategy to TRT to enable TensorRT optimization) and 'input_shape' (specifying the exact input shape).
B. 'instance_group' (setting count to the number of available GPUs and kind to KIND_GPU) and (increasing it to the largest value that fits in GPU memory).
C. Both B and C.
D. 'dynamic_batching' (enabling it and setting and 'model_warmup' (specifying dummy inputs to pre-load the model).
E. 'instance_group' (setting count to the number of available GPUs) and (setting a high value to accumulate requests).

Answer: C

Explanation:
'instance_group' with 'KIND_GPIY assigns the model to specific GPUs. Increasing (B) leverages GPU parallelism. Enabling 'dynamic_batching' and setting (C) allows Triton to dynamically batch requests to maximize throughput. Model warmup reduces first request latency. (A) is incomplete (missing KIND_GPU). (D) is relevant for latency optimization but not as crucial for throughput in a memory-constrained scenario. Therefore both B and C are most crucial in optimizing throughput while dealing with memory constraint.

NEW QUESTION # 118
You are building a multimodal generative A1 model that combines text, images, and audio. You notice that the model performs well on text and images but struggles with audio, particularly in noisy environments. Which of the following strategies would be MOST effective in improving the model's performance with audio data?

A. Use transfer learning by pre-training the audio component of the model on a large audio dataset.
B. Increase the learning rate for the audio modality during training.
C. Apply data augmentation techniques specifically designed for audio, such as adding noise or varying the speed and pitch.
D. Decrease the weight of the audio modality in the loss function.
E. Reduce the dimensionality of the audio features to simplify the learning task.

Answer: A,C

Explanation:
Data augmentation (C) increases the robustness of the model to variations in audio, including noise. Transfer learning (E) allows the model to leverage knowledge from a large, pre-existing audio dataset, improving its initial performance.

NEW QUESTION # 119
You are developing a multimodal model that combines time-series data from sensor readings with natural language descriptions of events. The time-series data has varying sampling rates and the text descriptions are often vague and ambiguous. How would you best address the challenge of aligning and fusing these two modalities to improve model performance?

A. Ignore the time-series data and train the model only on the text descriptions.
B. Use a dynamic time warping (DTW) algorithm to align the time-series data with the text descriptions and then use a cross-modal attention mechanism for fusion.
C. Train separate models for time-series and text and average their predictions.
D. Resample the time-series data to a uniform sampling rate and directly concatenate it with the text embeddings.
E. Average the time-series data over a fixed time window and concatenate it with the text embeddings.

Answer: B

Explanation:
DTW helps align time-series data with varying lengths and temporal distortions to text. Cross-modal attention then effectively fuses the aligned modalities, allowing the model to learn relationships between them. Resampling and direct concatenation (A) doesn't account for temporal variations. Ignoring data (B) is counterproductive. Averaging (D) loses temporal information. Averaging separate model outputs (E) is a form of late fusion and less effective than joint learning after alignment.

NEW QUESTION # 120
You are building a multimodal model that takes video and audio as input. You want to fuse the information extracted from both modalities. Which of the following fusion techniques allows for learning temporal dependencies between modalities?

A. Attention-based Fusion using Transformers, allowing the model to weigh the importance of different parts of each modality over time.
B. Late Fusion (averaging the probabilities from separate networks).
C. Early Fusion (concatenating features before feeding into a single network).
D. Simple Addition of feature vectors from video and audio streams.
E. Maximum pooling across feature vectors from video and audio streams.

Answer: A

Explanation:
Attention-based Fusion, particularly using Transformers, is well-suited for capturing temporal dependencies in multimodal data. Transformers can learn which parts of each modality are most relevant at different points in time, enabling a more nuanced fusion of information. Early Fusion (A) fuses features statically and doesn't capture temporal dependencies directly. Late Fusion (B) also struggles to capture fine- grained temporal relationships. Simple addition (D) and max pooling (E) are too simplistic to model complex temporal interactions.

NEW QUESTION # 121
You are training a Generative Adversarial Network (GAN) to generate realistic images. After several epochs, you observe that the generator is consistently producing similar images, regardless of the input noise. This phenomenon is known as:

A. Underfitting
B. Mode collapse
C. Overfitting
D. Vanishing gradients
E. Exploding gradients

Answer: B

Explanation:
Mode collapse occurs when the generator in a GAN learns to produce a limited variety of outputs, often focusing on the most easily generated examples. This leads to a lack of diversity in the generated images. Vanishing and exploding gradients are related to the training process itself, overfitting relates to model complexity compared to the training data, and underfitting relates to insufficient model capacity.

NEW QUESTION # 122
......

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