NCA-GENM Valid Test Answers | Valid NCA-GENM Exam Discount

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NVIDIA Generative AI Multimodal Sample Questions (Q146-Q151):

NEW QUESTION # 146
You are building a retrieval-augmented generation (RAG) system that utilizes a knowledge graph to enhance the responses generated by a large language model. The knowledge graph contains information about entities and their relationships extracted from both text documents and image metadat a. However, you observe that the system often retrieves irrelevant or outdated information from the knowledge graph, leading to inaccurate or misleading responses. Which of the following strategies would be MOST effective in addressing this issue?

A. Increase the size of the knowledge graph.
B. Use a simpler language model for the generative component of the RAG pipeline.
C. Implement a mechanism to filter and rank the retrieved information based on relevance and recency, using both semantic similarity and temporal information.
D. None of the above.
E. Reduce the number of entities in the knowledge graph.

Answer: C

Explanation:
Filtering and ranking the retrieved information based on relevance and recency ensures that the system prioritizes the most accurate and up-to-date information from the knowledge graph. Simply increasing the size of the knowledge graph or using a simpler language model would not directly address the issue of irrelevant or outdated information.

NEW QUESTION # 147
You are using NeMo to fine-tune a large language model for a specific task. You notice that the model is overfitting to the training dat a. Which of the following techniques could you apply to mitigate overfitting in this scenario? (Select all that apply)

A. Decrease the learning rate.
B. Increase the batch size.
C. Add dropout layers to the model architecture.
D. Increase the size of the training dataset.
E. Implement weight decay (L2 regularization).

Answer: A,C,D,E

Explanation:
Overfitting occurs when a model learns the training data too well and performs poorly on unseen data. Increasing the size of the training dataset provides the model with more diverse examples. Decreasing the learning rate helps the model converge to a more generalizable solution. Weight decay penalizes large weights, preventing the model from becoming too specialized to the training data. Dropout randomly disables neurons during training, forcing the model to learn more robust features. Increasing batch size can sometime prevent model to converge You are developing a system that uses a generative AI model to create personalized avatars for users based on their descriptions.

NEW QUESTION # 148
You are experimenting with different loss functions for training a Variational Autoencoder (VAE) to generate images. You observe that using only the reconstruction loss (e.g., Mean Squared Error) results in blurry images. What other loss component is typically added to the VAE objective function to encourage the latent space to be well-structured and generate sharper images?

A. Contrastive loss
B. Hinge loss
C. Kullback-Leibler (KL) divergence loss
D. Cross-entropy loss
E. Perceptual loss

Answer: C

Explanation:
The Kullback-Leibler (KL) divergence loss is a crucial component of the VAE objective function. It measures the difference between the learned latent space distribution and a prior distribution (typically a standard Gaussian). Adding the KL divergence loss encourages the latent space to be well-structured and continuous, which helps generate sharper and more realistic images. The other loss functions serve different purposes and are not typically used in VAEs for this specific reason. Cross-entropy is for classification. Perceptual loss helps in transferring styles. Contrastive loss used to learn embedding. Hinge loss mostly used in SVM.

NEW QUESTION # 149
Which of the following are valid techniques for dealing with overfitting in a deep learning model trained on image data?

A. Using data augmentation techniques.
B. Reducing the amount of training data
C. Increasing the complexity of the model.
D. Adding Ll or L2 regularization.
E. Implementing dropout layers.

Answer: A,D,E

Explanation:
Overfitting occurs when a model learns the training data too well and performs poorly on unseen data. L1/L2 regularization penalizes large weights, preventing the model from becoming too complex. Data augmentation increases the Size and diversity of the training data, reducing overfitting. Dropout randomly deactivates neurons during training, preventing co-adaptation and improving generalization. Increasing model complexity or reducing training data would likely worsen overfitting.

NEW QUESTION # 150
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. 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).
B. 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.
C. 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.
D. Use topic modeling on the text reviews, then use logistic regression to predict the numerical rating based on the topic distributions and subscription plan.
E. Use a decision tree to predict the numerical rating based on the text reviews (using TF-IDF) and subscription plan.

Answer: C

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 # 151
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NCA-GENM VALID TEST ANSWERS | VALID NCA-GENM EXAM DISCOUNT

NCA-GENM Valid Test Answers | Valid NCA-GENM Exam Discount