NCA-GENM Authorized Exam Dumps | NCA-GENM Practice Exam
NCA-GENM Authorized Exam Dumps | NCA-GENM Practice Exam
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NVIDIA Generative AI Multimodal Sample Questions (Q257-Q262):
NEW QUESTION # 257
When training a multimodal generative model for image captioning, you notice the model generates grammatically correct but generic and uninformative captions. Which technique is MOST likely to improve the in formativeness and specificity of the generated captions?
- A. Increase the size of the image encoder.
- B. Employ a diverse beam search or sampling strategy during inference to encourage exploration of different caption possibilities.
- C. Decrese the size of the vocabulary.
- D. Decrease the learning rate during training.
- E. Use beam search during inference with a large beam size.
Answer: B
Explanation:
Diverse beam search or sampling strategies encourage the model to explore different caption possibilities during inference, leading to more diverse and informative captions. Standard beam search often converges to the most likely caption, which tends to be generic. Increasing the image encoder Size might improve image feature extraction but doesn't directly address the caption informativeness problem. Decreasing the learning rate is a general training technique that might improve convergence but doesn't specifically target caption informativeness.
NEW QUESTION # 258
Which statistical method is most appropriate for evaluating the agreement between multiple human annotators labeling images with severity scores on a scale of 1 to 5, for a multimodal medical imaging application?
- A. Chi-squared test
- B. Pearson correlation coefficient
- C. Spearman's rank correlation coefficient
- D. Krippendorff's Alpha
- E. Cohen's Kappa
Answer: D
Explanation:
Krippendorff's Alpha is the most appropriate measure when dealing with multiple annotators, ordinal data (severity scores on a scale), and potential for missing data. While Cohen's Kappa is suitable for two annotators, Krippendorff's Alpha generalizes to multiple annotators. Spearman's rank correlation is better suited for assessing the monotonic relationship between variables, but not agreement among raters. Pearson correlation requires interval data, while chi-squared is for categorical.
NEW QUESTION # 259
You are working with time-series data from IoT sensors alongside video footage from surveillance cameras to detect anomalies in a factory production line. What data preprocessing steps are crucial for effectively integrating and analyzing these modalities in a multimodal AI model?
- A. Synchronizing the timestamps of the time-series data and video frames.
- B. All of the above.
- C. Converting the video footage to grayscale to simplify feature extraction.
- D. Normalizing the time-series data to a consistent range.
- E. Downsampling the video footage to reduce computational cost.
Answer: C
Explanation:
All the mentioned steps are crucial. Synchronizing timestamps is essential for temporal alignment. Normalizing time-series data ensures features are on the same scale, preventing bias. Downsampling video reduces computational burden, and grayscale conversion simplifies feature extraction without losing vital information for anomaly detection.
NEW QUESTION # 260
You are fine-tuning a large pre-trained language model for a specific downstream task. During training, you observe that the model performs well on the training data but generalizes poorly to the validation dat a. Which of the following strategies could help improve the model's generalization performance?
- A. Decrease the learning rate.
- B. Increase the training data size by collecting more data.
- C. Increase the learning rate.
- D. Implement early stopping based on the validation loss.
- E. Increase the weight decay (L2 regularization).
Answer: A,B,D,E
Explanation:
Decreasing the learning rate can help the model to converge to a better solution and avoid overfitting. Increasing the training data size provides the model with more examples to learn from, improving generalization. Early stopping prevents the model from training for too long and overfitting the training data. Increasing weight decay adds more regularization, preventing the model from learning overly complex patterns. Increasing the learning rate might worsen overfitting.
NEW QUESTION # 261
You are working with a dataset of handwritten digits and training a Variational Autoencoder (VAE) to generate new digits. After training, you observe that the generated digits are blurry and lack sharp details. Which of the following modifications could potentially improve the quality of the generated digits in your VAE?
- A. Increasing the capacity of the encoder and decoder networks (e.g., adding more layers or neurons).
- B. Decreasing the dimensionality of the latent space.
- C. Using a simpler decoder architecture.
- D. Increasing the weight of the KL divergence term in the VAE loss function.
- E. Reducing the weight of the KL divergence term in the VAE loss function.
Answer: A,E
Explanation:
Increasing the capacity of the encoder and decoder allows the VAE to learn more complex representations of the data. Reducing the weight of the KL divergence term allows the model to prioritize reconstruction accuracy, which can lead to sharper details. Decreasing latent space dimensionality might restrict the model's ability to capture fine-grained details. A simpler decoder will lead to more blurry images. Increasing the KL divergence weight can lead to disentangled representations, but often at the cost of reconstruction quality (blurriness).
NEW QUESTION # 262
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