NCA-GENM - NVIDIA Certified Associate: Multimodal Generative AI Practice Questions: Trustworthy AI Domain

Correct Answer: B

Explanation: Option B is correct because fairness-aware algorithms can help balance the system's performance across different modalities, ensuring that no single modality introduces bias into the system. Option A is incorrect as it reduces the richness of the data, potentially missing important context. Option C is incorrect because focusing on the most abundant modality can exacerbate bias. Option D is incorrect as excluding modalities can lead to a loss of valuable information.

Correct Answer: B

Explanation: Employing a diverse group of annotators helps to capture a wide range of perspectives and reduce biases that might be introduced by a homogeneous group. Option A, automating the annotation process, could perpetuate existing biases. Option C, using synthetic data, might not fully capture the diversity of real-world scenarios. Option D, limiting dataset size, may reduce data diversity and potentially increase bias.

Correct Answer: B

Explanation: Ensuring transparency in how the system reaches its conclusions is crucial for responsible AI development, especially in sensitive fields like healthcare. This helps build trust and allows for the identification of any biases. Options A, C, and D are incorrect as they do not prioritize transparency or responsible development practices.

Correct Answer: B

Explanation: Conducting regular audits for potential biases in model predictions ensures that the AI system remains fair and unbiased over time, which is a key aspect of responsible AI development. While model compression (A) and reducing carbon footprint (D) are important for efficiency and sustainability, they do not directly address bias. Integrating more modalities (C) can enhance robustness but does not inherently ensure trustworthiness.

Correct Answer: C

Explanation: Option C is correct because the TAO Toolkit simplifies transfer learning, allowing developers to incorporate diverse data, which is crucial for reducing bias. Option A is incorrect as no dataset can be guaranteed to be bias-free. Option B is incorrect because TAO does not offer automated bias detection tools. Option D is incorrect as automation alone does not ensure trustworthiness or bias mitigation.

Correct Answer: B

Explanation: Option B is correct because having a diverse group of annotators can provide multiple perspectives, reducing the risk of bias in the annotations. Option A is incorrect because while automation can reduce human error, it may not address underlying biases. Option C is incorrect as focusing only on frequent samples can lead to biased representations. Option D is incorrect because using a single annotator may introduce personal biases.

Correct Answer: B

Explanation: Option B is correct because NVIDIA's NeMo framework allows for fine-tuning models on specific datasets, which is crucial for assessing and mitigating bias. Option A is incorrect as using pre-trained models without customization does not address specific biases. Option C is incorrect as it focuses on performance rather than trustworthiness. Option D is incorrect as GPU memory usage is not directly related to developing trustworthy AI.

Correct Answer: A

Explanation: Option A is correct because NVIDIA DIGITS provides visualization tools that help in understanding and interpreting model decisions, which is crucial for transparency. Option B is incorrect as NVIDIA Nsight Systems is primarily used for performance analysis. Option C is incorrect as DeepStream is designed for video analytics rather than model transparency. Option D is incorrect because RAPIDS focuses on data science workflows, not directly on model decision transparency.

Correct Answer: B

Explanation: Option B is correct because RAPIDS AI provides data preprocessing capabilities that can be used to analyze and visualize dataset distributions, helping to identify potential biases. Option A is incorrect because inference optimization does not directly relate to bias detection. Option C is incorrect as RAPIDS does not automatically generate synthetic data. Option D is incorrect because cloud integration does not address bias detection.

Correct Answer: C

Explanation: Balancing the representation of different modalities in the training data is crucial for ensuring fairness in multimodal AI models, as it prevents certain modalities from dominating the learning process. Option A is related to performance, not fairness. Option B is important for model relevance but does not specifically address fairness. Option D focuses on computation speed and does not relate to fairness considerations.