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NCA-GENM Valid Test Prep - Reliable NCA-GENM Exam Preparation

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NVIDIA Generative AI Multimodal Sample Questions (Q355-Q360):

NEW QUESTION # 355
You're training a large language model (LLM) and notice that it struggles to maintain consistency and context over long passages of text. Which of the following architectural modifications would be most effective in addressing this issue?

A. Increasing the size of the vocabulary.
B. Implementing a sparse attention mechanism to reduce computational cost
C. Increasing the maximum sequence length the model can process.
D. Reducing the number of layers in the transformer architecture.
E. Using a smaller embedding dimension.

Answer: C

Explanation:
Increasing the maximum sequence length allows the model to consider a larger context window when generating text, improving its ability to maintain consistency and coherence over longer passages. Other options might address other issues, but increasing sequence length directly tackles the problem of limited context.

NEW QUESTION # 356
You are building an A1 model that takes video and corresponding subtitles as input to generate short summaries of video content. Which of the following strategies are most important to reduce the chance of your model generating biased summaries? (Select all that apply)

A. Randomly shuffle data during training.
B. Use a pre-trained language model that has been debiased.
C. Increase the number of training epochs.
D. Ensure the training dataset contains diverse representation of all demographic groups and viewpoints.
E. Evaluate the model's summaries on different demographic groups to identify and mitigate any disparities in performance.

Answer: B,D,E

Explanation:
Debiasing pre-trained models helps remove existing biases. A diverse training dataset is important to reduce the influence of any single biased viewpoint. Evaluating model performance on different demographic groups allows you to find and rectify performance disparities. Random data shuffling (D) and increasing training epochs (E) do not directly address bias. Note this is a very tough question as all choices seem viable but only options, A, B and C are the correct choice.

NEW QUESTION # 357
You are building a video summarization system that uses both visual (frame content) and audio (speech transcripts) information. You've noticed that the system tends to prioritize segments with clear speech but often misses important visual events that are not explicitly mentioned in the audio. How can you improve the system to better incorporate visual cues into the summarization process? (Select all that apply)

A. Fine-tune the audio transcription model to be more accurate.
B. Use a multimodal attention mechanism that allows the model to attend to both visual and audio features simultaneously.
C. All of the above.
D. Increase the weight given to audio features in the summarization model.
E. Train a separate model to detect important visual events (e.g., action recognition) and incorporate its output into the summarization process.

Answer: B,E

Explanation:
Training a separate model for visual event detection helps explicitly identify important visual cues. A multimodal attention mechanism allows the model to dynamically weigh the importance of visual and audio features. Increasing the weight of audio features would exacerbate the problem. While fine-tuning the audio model is helpful, it doesn't address the core issue of incorporating visual cues. Option E is incorrect as B is incorrect.

NEW QUESTION # 358
You are developing a multimodal model that processes text and time-series data to predict equipment failure. The text data consists of maintenance logs, and the time-series data consists of sensor readings. You observe that the model performs poorly when there are abrupt changes in sensor readings that are not reflected in the maintenance logs. How would you address this data integration challenge? (Select all that apply).

A. Ignore the sensor readings and focus only on the maintenance logs.
B. Downsample the time-series data to match the frequency of the text data.
C. All of the above
D. Use a transformer-based model with cross-attention to allow the model to attend to relevant time-series features when processing the text data.
E. Implement a change-point detection algorithm to identify abrupt changes in sensor readings and generate synthetic text descriptions of these events to augment the maintenance logs.

Answer: D,E

Explanation:
Implementing change-point detection and generating synthetic text descriptions helps bridge the gap between the two modalities. Cross-attention allows the model to dynamically integrate information from both modalities. Downsampling the time series data could lead to loss of information. Option E is incorrect because downsampling may hide crucial details in the time-series data.

NEW QUESTION # 359
You are working on a project that involves training a large language model (LLM) on a massive dataset of text and code. You have limited GPU memory and need to optimize the training process. Which of the following techniques would be MOST effective in reducing memory consumption during training?

A. Using a higher precision data type (e.g., float64 instead of float32).
B. Using a smaller learning rate.
C. Increasing the number of layers in the LLM.
D. Increasing the batch size.
E. Gradient accumulation and mixed-precision training (e.g., using FP16 or BFIoat16).

Answer: E

Explanation:
Gradient accumulation allows you to simulate a larger batch size without increasing memory consumption by accumulating gradients over multiple smaller batches. Mixed-precision training reduces the memory footprint of weights and activations. Increasing the batch size or using higher precision data types increases memory consumption. Using a smaller learning rate doesn't directly affect memory. More layers will also increase memory usage.

NEW QUESTION # 360
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