NCA-GENM exam dumps

NCA-GENM practice question 221 of 228

NVIDIA-Certified Associate - Generative AI Multimodal. Associate level, NVIDIA. Free question with the correct answer and a full explanation.

NCA-GENM Question 221

Select 3

You are deploying a multimodal AI model for a healthcare application that involves image and text-based data. To ensure the trustworthiness of the AI system, what steps can you take by leveraging NVIDIA technologies and related techniques?

  1. A

    Use NVIDIA AI Explainability tools to provide insights into model predictions.

  2. B

    Train the AI model on a biased dataset to improve performance for underrepresented groups.

  3. C

    Leverage NVIDIA TensorRT to optimize model inference and ensure consistent outputs.

  4. D

    Apply federated learning techniques to protect patient data while training the model.

  5. E

    Use pretrained NVIDIA models without further validation for the specific application.

Show answer and explanation

Correct answers: A, C, D

Explanation

Ensuring AI trustworthiness in sensitive applications like healthcare involves transparency, optimization, and privacy protection. NVIDIA AI tools, such as Explainability tools and TensorRT, can improve interpretability and reliability. Federated learning safeguards sensitive data, while biased datasets and unvalidated pretrained models undermine trustworthiness.

  • A. Correct.

    NVIDIA AI Explainability tools help provide transparency into how model predictions are made, increasing trust in the system.

  • B. Incorrect.

    Training the model on a biased dataset is counterproductive and reduces trustworthiness, as it leads to unfair or incorrect outcomes.

  • C. Correct.

    NVIDIA TensorRT optimizes inference performance and ensures consistent outputs, which is critical for trustworthiness in sensitive applications like healthcare.

  • D. Correct.

    Federated learning allows training on decentralized data sources without sharing sensitive information, enhancing privacy and trustworthiness.

  • E. Incorrect.

    Using pretrained models without validation risks deploying a model that may not be reliable or trustworthy for the specific application.

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