VALID TEST NCA-GENM TUTORIAL | NCA-GENM LATEST BRAINDUMPS BOOK

Valid Test NCA-GENM Tutorial | NCA-GENM Latest Braindumps Book

Valid Test NCA-GENM Tutorial | NCA-GENM Latest Braindumps Book

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Tags: Valid Test NCA-GENM Tutorial, NCA-GENM Latest Braindumps Book, NCA-GENM Current Exam Content, Exam NCA-GENM Book, NCA-GENM Online Lab Simulation

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NCA-GENM Latest Braindumps Book - NCA-GENM Current Exam Content

We provide up-to-date NVIDIA Generative AI Multimodal (NCA-GENM) exam questions and study materials in three different formats. We have developed three variations of authentic NVIDIA NCA-GENM exam questions to cater to different learning preferences, ensuring that all candidates can effectively prepare for the NCA-GENM Practice Test. iPassleader offers NVIDIA Generative AI Multimodal (NCA-GENM) practice questions in PDF format, browser-based practice exams, and desktop practice test software.

NVIDIA Generative AI Multimodal Sample Questions (Q384-Q389):

NEW QUESTION # 384
Consider the following code snippet using NVIDIA Triton Inference Server. What is the purpose of the 'sequence_batching' configuration?

  • A. It enables batching of independent requests to improve throughput.
  • B. It automatically scales the number of model instances based on the input load.
  • C. It allows for processing sequences of inputs (e.g., time series data) by maintaining state between requests.
  • D. It optimizes the model for specific hardware architectures.
  • E. It enables dynamic batching based on request arrival times.

Answer: C

Explanation:
The 'sequence_batching' configuration in Triton Inference Server is designed to handle sequential data where the server needs to maintain state between requests. This is essential for tasks like time-series prediction or conversational AI where the context of previous inputs matters. A, E describe standard batching. C describes autoscaling and D describes model optimization.


NEW QUESTION # 385
You're developing a multimodal model that takes both image and audio inputs to predict a relevant text description. You observe that the model is heavily biased towards the image data, effectively ignoring the audio input. Which of the following techniques could you employ to address this modality imbalance and ensure the model effectively utilizes both input modalities?

  • A. Reduce the dimensionality of the image features before fusion.
  • B. Oversample the audio data during training.
  • C. Increase the batch size for each epoch.
  • D. Apply modality-specific dropout to the image pathway.
  • E. Increase the learning rate for the audio modality pathway during training.

Answer: A,B,D,E

Explanation:
Increasing the learning rate for the audio pathway allows it to update its weights more aggressively, potentially counteracting the image bias. Applying modality-specific dropout to the image pathway forces the model to rely less on image features and more on audio. Oversampling the audio data ensures that the model sees more examples from the audio modality during training. Reducing the dimensionality of the image features can prevent them from dominating the fusion process. Increasing batch size is not specific to each modality and does not directly deal with modality imbalance, but can influence training dynamics.


NEW QUESTION # 386
You're training a VQA (Visual Question Answering) model. During evaluation, you notice the model performs well on common object recognition tasks but struggles with questions requiring reasoning about object relationships or scene understanding. What are the MOST effective strategies to improve the model's performance on these complex reasoning tasks? (Choose two)

  • A. Train the model on a larger dataset with more diverse and complex questions/answers.
  • B. Decrease the learning rate.
  • C. Replace the LSTM with a simpler RNN in the question encoder.
  • D. Use a more sophisticated attention mechanism that attends to relevant image regions based on the question.
  • E. Increase the size of the image embedding.

Answer: A,D

Explanation:
More sophisticated attention mechanisms help the model focus on relevant image regions. A larger, more diverse dataset provides the model with more examples of complex reasoning scenarios. Increasing the image embedding size may help but is not as targeted. Decreasing the learning rate is a general optimization technique, and using a simpler RNN would likely degrade performance.


NEW QUESTION # 387
You have a dataset of customer reviews for a Generative A1 service. The dataset contains text reviews, numerical ratings (1-5 stars), and categorical data about the customer's subscription plan (Basic, Premium, Enterprise). You want to build a model to predict the numerical rating based on the text review and subscription plan. Which data analysis and modeling approach would be MOST suitable?

  • A. Use topic modeling on the text reviews, then use logistic regression to predict the numerical rating based on the topic distributions and subscription plan.
  • B. Use a decision tree to predict the numerical rating based on the text reviews (using TF-IDF) and subscription plan.
  • C. Calculate the average word length of the text reviews and use that as a feature in a linear regression model along with the subscription plan to predict the rating.
  • D. Train a deep learning model (e.g., BERT or RoBERTa) on the text reviews, concatenate the output embeddings with the one-hot encoded subscription plan, and use a regression layer to predict the numerical rating.
  • E. Perform sentiment analysis on the text reviews, then use linear regression to predict the numerical rating based on the sentiment score and subscription plan (one-hot encoded).

Answer: D

Explanation:
Using a pre-trained language model like BERT or RoBERTa captures the semantic meaning of the text reviews most effectively. Concatenating the embeddings with the subscription plan allows the model to learn the combined effect of both inputs. Regression layer is used as numeric ratings (1-5 stars) are provided as the target values. Sentiment and topic modeling can work as features but BERT/RoBERTa gives better context. Other options aren't able to capture complex context.


NEW QUESTION # 388
You are working with a multimodal model that combines text and image inputs. You want to analyze the model's attention mechanisms to understand which parts of the image are most relevant to specific words in the input text. What technique can you use to visualize and interpret the model's attention weights in this scenario?

  • A. t-SNE (t-distributed Stochastic Neighbor Embedding)
  • B. Confusion Matrix
  • C. ROC curves (Receiver Operating Characteristic curves)
  • D. PCA (Principal Component Analysis)
  • E. Attention Heatmaps

Answer: E

Explanation:
Attention heatmaps are a visualization technique used to highlight the regions of an image that the model is focusing on when processing specific words in the input text. By overlaying the attention weights onto the image, you can identify the most relevant areas. t-SNE and PCA are dimensionality reduction techniques used for visualizing high-dimensional data in lower dimensions. ROC curves and confusion matrices are used to evaluate the performance of classification models.


NEW QUESTION # 389
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