NCA-GENM NVIDIA Practice Questions

Question #1 (Topic: Demo Questions)

After fine-tuning a large language model (LLM) for generating legal documents, what is the most
effective way to assess whether the fine-tuning has improved the model’s performance for this specific
task?

A.

Measuring the speed at which the fine-tuned model generates text, regardless of content accuracy

B.

Evaluating the model’s output against a benchmark dataset of legal documents that it has never

seen before.

C.

Comparing the fine-tuned model’s output with that of a non-fine-tuned model on random text generation tasks.

D.

Testing the fine-tuned model on a set of common, non-legal text generation tasks to measure

general improvement.

Correct Answer: B

Explanation:

Evaluating the model's output against a benchmark dataset of unseen legal documents ensures that the

fine-tuning improves performance for the specific legal task, making it a reliable method of assessment.

Question #2 (Topic: Demo Questions)

To enhance the trustworthiness of an AI system used for healthcare diagnostics, which approach involving NVIDIA technologies is most appropriate?

A.

Using NVIDIA GPUs to increase the speed of diagnosis without focusing on data security

B.

Prioritizing the collection of large amounts of patient data over maintaining patient privacy.

C.

Implementing NVIDIA Clara’s privacy-preserving federated learning to train the AI on sensitive patient data across multiple institutions without sharing the data

D.

Training the AI solely on data from a single medical institution to streamline the process.

Correct Answer: C

Explanation:

NVIDIA Clara’s privacy-preserving federated learning allows AI models to be trained on sensitive data across multiple institutions without directly sharing patient data, enhancing both privacy and performance.

Question #3 (Topic: Demo Questions)

You are deploying a multimodal AI system that integrates text, images, and voice commands to assist in
autonomous driving. The system must make real-time decisions based on input from various sensors in
different environments. Which strategy best ensures that the AI system remains reliable and trustworthy
in diverse driving conditions?

A.

Prioritizing the speed of decision-making over the accuracy of sensor interpretation

B.

Prioritizing the speed of decision-making over the accuracy of sensor interpretation

C.

Using a generic AI model without customizing it for different environments.

D.

Relying solely on data collected from one geographical region to train the AI system.

Correct Answer: D

Explanation:

NVIDIA's DRIVE platform provides robust simulation environments that allow for thorough testing and validation across different real-world scenarios, ensuring reliability in diverse conditions.

Question #4 (Topic: Demo Questions)

You are working on a project that involves training a multimodal AI model combining natural language

processing and computer vision to provide real-time captions for live video feeds. However, the captions
generated often lag behind the video. Upon investigation, you find that the language model is causing
the delay. Which of the following strategies is most likely to reduce the lag and improve synchronization?

A.

Increase the video frame rate.

B.

Reduce the input image resolution

C.

Use a more complex language model.

D.

Switch to a smaller language model.

Correct Answer: D

Explanation:

Switching to a smaller language model reduces processing time, minimizing the lag between the video feed and the generated captions.

Question #5 (Topic: Demo Questions)

You are developing a multimodal AI system that generates high-resolution images from complex English text prompts. The system must handle a variety of detailed descriptions and produce visually accurate results. What strategies are most effective for optimizing the performance of a text-t

A.

Implement a separate model for image refinement after initial generation.

B.

Incorporate a large and diverse set of image-text pairs for training.

C.

Use a transformer-based model for text encoding.

D.

Use a fixed text-to-image model without fine-tuning.

E.

Limit the model's training to simple, short text descriptions.

Next Question

Correct Answer: B, C

Explanation:

Using a diverse set of image-text pairs improves the model’s ability to handle a wide range of prompts, and transformer-based models are effective for encoding complex text descriptions.

NVIDIA NCA-GENM - NCA - Generative AI Multimodal Certification Exam

After fine-tuning a large language model (LLM) for generating legal documents, what is the mosteffective way to assess whether the fine-tuning has improved the model’s performance for this specifictask?

Measuring the speed at which the fine-tuned model generates text, regardless of content accuracy

Evaluating the model’s output against a benchmark dataset of legal documents that it has never

Comparing the fine-tuned model’s output with that of a non-fine-tuned model on random text generation tasks.

Testing the fine-tuned model on a set of common, non-legal text generation tasks to measure

general improvement.

Correct Answer: B

Evaluating the model's output against a benchmark dataset of unseen legal documents ensures that the

fine-tuning improves performance for the specific legal task, making it a reliable method of assessment.

To enhance the trustworthiness of an AI system used for healthcare diagnostics, which approach involving NVIDIA technologies is most appropriate?

Using NVIDIA GPUs to increase the speed of diagnosis without focusing on data security

Prioritizing the collection of large amounts of patient data over maintaining patient privacy.

Implementing NVIDIA Clara’s privacy-preserving federated learning to train the AI on sensitive patient data across multiple institutions without sharing the data

Training the AI solely on data from a single medical institution to streamline the process.

Correct Answer: C

NVIDIA Clara’s privacy-preserving federated learning allows AI models to be trained on sensitive data across multiple institutions without directly sharing patient data, enhancing both privacy and performance.

Prioritizing the speed of decision-making over the accuracy of sensor interpretation

Prioritizing the speed of decision-making over the accuracy of sensor interpretation

Using a generic AI model without customizing it for different environments.

Relying solely on data collected from one geographical region to train the AI system.

Correct Answer: D

NVIDIA's DRIVE platform provides robust simulation environments that allow for thorough testing and validation across different real-world scenarios, ensuring reliability in diverse conditions.

You are working on a project that involves training a multimodal AI model combining natural language

Increase the video frame rate.

Reduce the input image resolution

Use a more complex language model.

Switch to a smaller language model.

Correct Answer: D

Switching to a smaller language model reduces processing time, minimizing the lag between the video feed and the generated captions.

You are developing a multimodal AI system that generates high-resolution images from complex English text prompts. The system must handle a variety of detailed descriptions and produce visually accurate results. What strategies are most effective for optimizing the performance of a text-t

Implement a separate model for image refinement after initial generation.

Incorporate a large and diverse set of image-text pairs for training.

Use a transformer-based model for text encoding.

Use a fixed text-to-image model without fine-tuning.

Correct Answer: B, C

Using a diverse set of image-text pairs improves the model’s ability to handle a wide range of prompts, and transformer-based models are effective for encoding complex text descriptions.

After fine-tuning a large language model (LLM) for generating legal documents, what is the most
effective way to assess whether the fine-tuning has improved the model’s performance for this specific
task?