TEST NCA-GENM PREP, FORMAL NCA-GENM TEST

Test NCA-GENM Prep, Formal NCA-GENM Test

Test NCA-GENM Prep, Formal NCA-GENM Test

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Tags: Test NCA-GENM Prep, Formal NCA-GENM Test, NCA-GENM Actual Exam, NCA-GENM Practice Exam Online, Reliable NCA-GENM Cram Materials

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NVIDIA Generative AI Multimodal Sample Questions (Q120-Q125):

NEW QUESTION # 120
You are developing a multimodal system for generating recipes from images of food. The system takes an image of a dish as input and outputs a recipe containing the ingredients and instructions. Which of the following evaluation metrics would be most suitable for assessing the correctness and completeness of the generated recipes? (Select all that apply)

  • A. Precision and recall of the ingredients mentioned in the generated recipe compared to a ground truth ingredient list.
  • B. Human evaluation of the generated recipe's clarity, coherence, and accuracy.
  • C. Inception Score of the input image.
  • D. BLEU score between the generated recipe and a reference recipe.
  • E. Calculating the cosine similarity between the word embeddings of the generated and reference recipes.

Answer: A,B

Explanation:
Precision and recall of ingredients directly assess whether the generated recipe includes the correct ingredients. Human evaluation provides a subjective assessment of the recipe's overall quality, clarity, and accuracy. BLEU score is a general text evaluation metric, but may not capture the specific requirements of recipe generation. Inception Score is relevant for image generation, not recipe generation. Cosine similarity of word embeddings can capture semantic similarity, but doesn't guarantee correctness of the recipe.


NEW QUESTION # 121
You are working with a large dataset of images for training a generative model. The dataset contains a significant amount of noise and outliers. Which of the following data preprocessing techniques would be MOST effective in mitigating the impact of noise and outliers on the model's performance?

  • A. Applying histogram equalization to all images.
  • B. Clipping pixel values to a specific range (e.g., [0, 255]).
  • C. Converting all images to grayscale.
  • D. Applying a Gaussian blur to all images.
  • E. Using a robust statistics-based normalization technique (e.g., Z-score normalization with median and interquartile range).

Answer: E

Explanation:
Robust statistics-based normalization techniques, such as Z-score normalization using the median and interquartile range (IQR), are less sensitive to outliers than traditional methods like mean and standard deviation. Clipping pixel values can help to limit the impact of extreme outliers, but it may also remove valid data. Histogram equalization and Gaussian blur can improve image quality, but they are not specifically designed to handle outliers. Converting to grayscale reduces information but doesn't address noise specifically.


NEW QUESTION # 122
Given the following Python code snippet utilizing spacy for text processing in a multimodal sentiment analysis pipeline, identify the potential issues and suggest improvements to enhance the accuracy and efficiency of the pipeline:

What improvements can be implemented?

  • A. The code doesn't account for the intensity of sentiment-bearing words. Introduce a weighting mechanism based on the part-of-speech tags to emphasize adjectives and adverbs.
  • B. The code uses the small spacy model, which might not be accurate for sentiment analysis. Use a larger model like 'en_core_web_lg' for better performance.
  • C. The code doesn't handle contractions or special characters. Implement preprocessing steps to normalize the text before processing it with spacy.
  • D. Replace spacy entirely with NLTK for sentiment analysis, as it provides better pre-trained sentiment models.
  • E. The code calculates sentiment based on individual tokens, ignoring context and negations. Integrate a sentiment analysis library like VADER or TextBlob for more accurate sentiment scoring.

Answer: B,C,E

Explanation:
The small spacy model might lack the necessary vocabulary and training data for accurate sentiment analysis. The code's token- based sentiment calculation ignores context and negations, leading to inaccurate scoring. The code also needs preprocessing to handle contractions and special characters effectively. Sentiment analysis libraries provide more robust sentiment scoring mechanisms. Weighting by POS tags can help, but a better sentiment library is preferrable. Switching to NLTK entirely isn't necessarily better, upgrading the spacy model is a better option.


NEW QUESTION # 123
Which of the following techniques can be used to reduce the computational cost and memory footprint of large language models (LLMs) during inference?

  • A. Pruning
  • B. Knowledge Distillation
  • C. Adding more layers
  • D. Quantization
  • E. Increasing the model size

Answer: A,B,D

Explanation:
Quantization reduces the precision of the model's weights and activations, which can significantly reduce memory footprint and speed up inference. Knowledge distillation involves training a smaller 'student' model to mimic the behavior of a larger 'teacher' model, reducing the computational cost. Pruning removes unimportant connections (weights) from the model, leading to a sparser network and lower computational requirements. Increasing the model size or adding more layers would increase the computational cost.


NEW QUESTION # 124
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. Perplexity, Word Error Rate (WER)
  • B. BLEU score, CIDEr, SPICE
  • C. CIDEr, SPICE
  • D. Inception Score (IS), Frechet Inception Distance (FID)
  • E. BLEU score, ROUGE score

Answer: B

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 # 125
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