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NVIDIA Generative AI Multimodal Sample Questions (Q221-Q226):

NEW QUESTION # 221
You are building a Generative A1 application that processes images and text. The image data has missing pixel values, and the text data contains inconsistencies in abbreviations. Which data preprocessing techniques are MOST suitable to address these issues effectively?

A. Image: Replacing missing pixels with zero; Text: Ignoring abbreviations during analysis.
B. Image: KNN imputation for missing pixels; Text: Applying regular expressions to expand abbreviations.
C. Image: Deleting rows with missing pixel values; Text: Removing all abbreviations from the text data.
D. Image: Median imputation for missing pixels; Text: Using a fuzzy matching algorithm to correct inconsistencies in abbreviations.
E. Image: Mean imputation for missing pixels; Text: Standardizing abbreviations using a predefined mapping.

Answer: B,D

Explanation:
KNN imputation is more robust than mean imputation for images as it considers neighboring pixels. Regular expressions and fuzzy matching provide more accurate abbreviation handling compared to simply removing or ignoring them. KNN imputation and Median imputations both can work well. Fuzzy Matching can also resolve ambiguities in abreviations

NEW QUESTION # 222
You have trained a text-to-image diffusion model. During inference, you notice that the generated images often lack fine-grained details and appear blurry. Which of the following techniques could you apply to improve the image quality without retraining the model?

A. Use a larger batch size during inference.
B. Increase the number of diffusion steps during sampling.
C. Reduce the learning rate during sampling.
D. Decrease the guidance scale during sampling.
E. Increase the model's capacity by adding more layers.

Answer: B

Explanation:
Increasing the number of diffusion steps during sampling allows the model to refine the generated image more thoroughly, leading to finer details and reduced blurriness. The guidance scale controls how closely the generated image adheres to the input text prompt; increasing it typically improves adherence but can sometimes reduce diversity. Batch size primarily affects computational efficiency. Reducing the learning rate is relevant during training, not inference. Adding model layers requires retraining.

NEW QUESTION # 223
You are building a system that takes an image of a scene and a short audio clip as input and generates a descriptive text. You want to evaluate the system's performance. Which of the following evaluation metrics are MOST suitable for assessing both the accuracy and the coherence of the generated descriptions in relation to the input image and audio?

A. CIDEr, SPICE
B. BLEU score, ROUGE score
C. Inception Score (IS), Frechet Inception Distance (FID)
D. BLEU score, CIDEr, SPICE
E. Perplexity, Word Error Rate (WER)

Answer: D

Explanation:
BLEU, CIDEr, and SPICE are all suitable for evaluating image captioning and similar generative tasks. BLEU measures the n-gram overlap between the generated text and reference texts. CIDEr specifically focuses on consensus-based image description evaluation, weighting n-grams that are more common among human-generated captions. SPICE focuses on semantic propositional content and captures object, attribute, and relationship triples. ROUGE focuses on recall, but the other 3 provide the best overall picture. Perplexity and WER are more suitable for language models, and Inception Score and FID are used for evaluating the quality of generated images.

NEW QUESTION # 224
You're using a pre-trained multimodal model that combines visual and textual information for a new downstream task: generating marketing slogans for product images. The model performs poorly, generating generic slogans that are unrelated to the specific product features. What is the MOST effective strategy to adapt this pre-trained model to your specific task?

A. Freeze the pre-trained model's weights and train a separate model to map the pre-trained model's output to marketing slogans.
B. Fine-tune the entire pre-trained model on a dataset of product images and corresponding marketing slogans.
C. Only fine-tune the visual encoder component of the pre-trained model.
D. Use the pre-trained model as is, without any adaptation.
E. Replace the model's output layer with a new layer trained specifically to generate marketing slogans.

Answer: B

Explanation:
Fine-tuning the entire pre-trained model (B) allows the model to learn the specific nuances of the new task while leveraging the knowledge it gained during pre-training. Replacing only the output layer (A) might not be sufficient. Freezing the pre-trained model (C) limits its ability to adapt to the new task. Only fine-tuning the visual encoder (D) might not address the language generation aspect. Using the model without adaptation (E) will likely result in poor performance.

NEW QUESTION # 225
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 prompts consisting only of keywords related to the desired image.
B. Using negative prompts to explicitly exclude unwanted features or styles.
C. Using overly verbose and descriptive prompts to maximize detail.
D. Using random prompts to explore the model's creative capabilities.
E. Using short, concise prompts to minimize ambiguity.

Answer: B

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 # 226
......

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2025 NCA-GENM Test Book | Excellent 100% Free NCA-GENM New Dumps Questions

Pass Guaranteed 2025 Unparalleled NCA-GENM: NVIDIA Generative AI Multimodal Test Book

NVIDIA Generative AI Multimodal Sample Questions (Q221-Q226):

NCA-GENM Guide Torrent and NCA-GENM Training Materials - NCA-GENM Exam Braindumps - Test4Cram

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