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NVIDIA NCA-GENM Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Software Development & Engineering	15%	- Integration and deployment of multimodal AI systems - Python libraries for multimodal AI
Topic 2: Data Analysis & Visualization	10%	- Visualization techniques for multimodal data - Data preprocessing and feature engineering
Topic 3: Trustworthy AI	5%	- Ensuring fairness and transparency - Ethical considerations in AI development
Topic 4: Experimentation	25%	- A/B testing - Hypothesis testing - Experimental design - Model evaluation and comparison
Topic 5: Core ML & AI Knowledge	20%	- Key algorithms and techniques - Basic concepts and terminology
Topic 6: Multimodal Data	15%	- Applications and use cases - Handling and integrating text, image, and audio data
Topic 7: Performance Optimization	10%	- Techniques for optimizing AI performance - Monitoring and improving system efficiency

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NVIDIA Generative AI Multimodal Sample Questions (Q42-Q47):

NEW QUESTION # 42
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: Mean imputation for missing pixels; Text: Standardizing abbreviations using a predefined mapping.
B. Image: Replacing missing pixels with zero; Text: Ignoring abbreviations during analysis.
C. Image: Median imputation for missing pixels; Text: Using a fuzzy matching algorithm to correct inconsistencies in abbreviations.
D. Image: Deleting rows with missing pixel values; Text: Removing all abbreviations from the text data.
E. Image: KNN imputation for missing pixels; Text: Applying regular expressions to expand abbreviations.

Answer: C,E

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 # 43
You are building a multimodal model to classify news articles using both text and images. The text data is processed using spaCy, and image data is processed using Keras. You've noticed that the model is heavily biased towards the text dat a. Which of the following techniques would be MOST effective in addressing this modality imbalance?

A. Applying TF-IDF to the text data to reduce the impact of common words.
B. Reducing the dimensionality of the image feature vectors using Principal Component Analysis (PCA).
C. Normalizing the length of text sequences to a fixed size before feeding into the model.
D. Using data augmentation techniques on the image dataset, such as random rotations and flips.
E. Implementing modality-specific weighting in the loss function, giving a higher weight to the image loss.

Answer: E

Explanation:
Modality-specific weighting directly addresses the imbalance by penalizing errors from the underrepresented modality (images) more heavily. Data augmentation helps improve the image model's performance but doesn't directly address the imbalance. TF-IDF and text sequence normalization focus on text processing. PCA can reduce dimensionality but doesn't directly address the imbalance problem. Therefore, modality-specific weighting is the most effective.

NEW QUESTION # 44
Which of the following techniques are MOST effective for improving the energy efficiency of a large-scale Generative A1 model during inference, while minimizing performance degradation?

A. Increasing the batch size significantly
B. Model quantization (e.g., INT8)
C. Gradient accumulation
D. Pruning (removing less important weights)
E. Knowledge distillation to a smaller model

Answer: B,D,E

Explanation:
Model quantization reduces the memory footprint and computational cost by representing weights with fewer bits. Knowledge distillation trains a smaller, faster model to mimic the behavior of a larger model. Pruning removes redundant connections, reducing the number of computations. Gradient accumulation is for training, not inference. Increasing batch size may improve throughput but not necessarily energy efficiency per sample and might even decrease it due to increased memory usage.

NEW QUESTION # 45
You are experimenting with a text-to-image generative model. You notice that when prompted with descriptions containing specific demographic information (e.g., 'a black doctor'), the generated images consistently reflect stereotypes. What steps can you take during the experiment evaluation phase to identify and mitigate this bias? (Select TWO)

A. Increase the size of the training dataset to dilute the effect of any biased examples.
B. Use a bias detection metric to quantify the presence of bias in the generated images, comparing output distributions across different demographic groups.
C. Filter out all examples containing demographic information from the training dataset.
D. Randomly shuffle the training dataset to minimize bias.
E. Conduct a human evaluation study where participants assess the generated images for stereotypical representations.

Answer: B,E

Explanation:
Bias detection metrics (B) and human evaluation (D) are essential for identifying and quantifying bias in generated content. Increasing data size (A) alone might not solve the issue. Filtering demographic information (C) can lead to underrepresentation and unfair outcomes. Random shuffling (E) does not directly address inherent biases in the training data.

NEW QUESTION # 46
You're developing a multimodal model that combines text and audio for sentiment analysis. The text component is performing well, but the audio component contributes very little to the overall accuracy. What's the MOST likely reason and how could you address it?

A. The audio data is not preprocessed correctly. Apply aggressive noise reduction techniques.
B. The text component is simply too dominant. Reduce the weight given to the text component in the final prediction.
C. The audio features are not properly aligned with the text features. Use a cross-modal attention mechanism to improve alignment.
D. The audio data is too large. Downsample the audio data to reduce computational cost.
E. The audio data is irrelevant. Remove the audio component entirely.

Answer: C

Explanation:
Misalignment between audio and text features is a common problem in multimodal models. Cross-modal attention mechanisms allow the model to learn which parts of the audio are most relevant to specific parts of the text, improving the integration of information. While other options might offer minor improvements, they don't address the core issue of feature misalignment. Removing the audio component defeats the purpose of a multimodal model. Downsampling and noise reduction might help slightly, but won't solve a fundamental alignment problem.

NEW QUESTION # 47
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