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NVIDIA Generative AI Multimodal Sample Questions (Q381-Q386):

NEW QUESTION # 381
Consider a multimodal dataset containing text, images, and corresponding GPS coordinates. You want to build a model that predicts the sentiment of a social media post based on this dat a. Which of the following data preprocessing steps are crucial to ensure the model's performance and prevent data leakage?

A. Standardize the GPS coordinates (latitude and longitude) using a scaler fitted only on the training data.
B. Randomly shuffle the entire dataset before splitting it into training, validation, and test sets.
C. Normalize all text data to lowercase and remove punctuation.
D. Split the dataset into training, validation, and test sets based on time to avoid leakage of future information into the training set.
E. Resize all images to a uniform size.

Answer: A,C,D,E

Explanation:
Normalizing text (A) and resizing images (B) are standard preprocessing steps. Time-based splitting (C) prevents data leakage by ensuring that the model is not trained on future data. Standardizing GPS coordinates (E) with training data prevents the test data from influencing the scaling. Random shuffling before splitting (D) can lead to data leakage in time-series data.

NEW QUESTION # 382
You are building a Generative A1 model that generates captions for images. You want to evaluate the quality of the generated captions.
Which evaluation metrics are MOST suitable for this task?

A. Accuracy and Precision.
B. Mean Squared Error (MSE) and Root Mean Squared Error (RMSE).
C. Cosine Similarity and Euclidean Distance.
D. Fl-score and AUC.
E. BLEU, ROUGE, and CIDEr.

Answer: E

Explanation:
BLEU, ROUGE, and CIDEr are standard metrics used for evaluating the quality of generated text, particularly in image captioning and machine translation. These metrics compare the generated captions to reference captions and measure the similarity in terms of n-grams, word overlap, and other features. Other options are used for Classification problems (Accuracy Precision, Fl-score, AUC) and Regression Problems (MSE, RMSE).

NEW QUESTION # 383
You are using a pre-trained language model for text classification. You observe that the model performs well on the training data but poorly on unseen dat a. Which of the following techniques could help improve the model's generalization ability? (Select TWO)

A. Applying data augmentation techniques (e.g., random synonym replacement, back-translation).
B. Using weight decay (L2 regularization).
C. Decreasing batch size.
D. Decreasing the amount of training data.
E. Increasing the learning rate.

Answer: A,B

Explanation:
Weight decay penalizes large weights, preventing the model from overfitting to the training data. Data augmentation increases the diversity of the training data, making the model more robust to variations in unseen data. Increasing the learning rate can lead to instability and overfitting. Decreasing the amount of training data reduces the model's ability to learn general patterns.

NEW QUESTION # 384
You are tasked with integrating a CLIP model into your application to generate images based on text descriptions. You want to ensure that the generated images closely reflect the nuances of the text prompt. Which prompt engineering technique is MOST suitable for achieving this?

A. Using negative prompts to explicitly exclude unwanted features or styles.
B. Using overly verbose and descriptive prompts to maximize detail.
C. Using prompts consisting only of keywords related to the desired image.
D. Using random prompts to explore the model's creative capabilities.
E. Using short, concise prompts to minimize ambiguity.

Answer: A

Explanation:
Negative prompting is a powerful technique where you specify what you don't want in the generated image. This helps refine the output and steer the model away from undesirable artifacts or styles. For example, specifying "a futuristic city, but without flying cars".

NEW QUESTION # 385
Consider this Python code snippet using PyTorch:

A. torch.Size ([32, 5121). The issue is a dimension mismatch.
B. torch.Size ([32, 32]). This is correct and computes attention weights for each text-image pair in the batch independently
C.
D. torch.Size([256, 512]). This implementation is correct and efficient for cross-modal attention.
E. Error. The transpose operation is incorrect for achieving cross-modal attention.

Answer: C

Explanation:
The shape of the 'attention' tensor is torch.Size([32, 32]). The matrix multiplication of (32, 256) with (512, 32) results in a (32, 32) tensor. The crucial issue here is the batch-wise attention calculation. The attention weights are being computed between all text embeddings and all image embeddings in the batch. During training, this leads to 'information leakage' because the model is learning relationships between samples that shouldn't be related (i.e., different text-image pairs in the batch are influencing each other). For proper cross-modal attention, you would typically want to compute the attention weights only between corresponding text and image embeddings within the same sample.

NEW QUESTION # 386
......

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