NCA-GENM Practice Questions

A.  

It requires AI systems to be developed with an ethical consideration for societal impacts.

B.  

It ensures that AI systems are transparent in their decision-making processes.

C.  

It mandates that AI models comply with relevant laws and regulations specific to their deployment region and industry.

D.  

It involves verifying that AI models are fit for their intended purpose according to regional or industry-specific standards.

A.  

The model's processing speed in recognizing faces of different races.

B.  

The model's accuracy in recognizing individuals of different races.

C.  

The model's ability to recognize various facial expressions.

D.  

The model's compatibility with different operating systems.

A.  

A/B testing is used to measure the impact of changes in the user interface of a ML application.

B.  

A/B testing helps in optimizing the hyperparameters of a machine learning model.

C.  

A/B testing is irrelevant in ML software engineering.

D.  

A/B testing helps in evaluating the performance and effectiveness of different machine learning models.

A.  

Decision tree

B.  

Support vector machine (SVM)

C.  

K-means clustering

D.  

Generative adversarial network (GAN)

A.  

To determine the best AI model architecture.

B.  

To determine the ethical implications of AI model usage.

C.  

To study the impact of AI models on human behavior.

D.  

To analyze the cost-effectiveness of AI model development.

A.  

It requires fewer data samples for learning.

B.  

It can capture more complex patterns and relationships in data.

C.  

It is more reliable than unimodal learning.

D.  

It is easier to collect multimodal data than unimodal data.

A.  

CLIP is used to generate image captions from textual input.

B.  

CLIP is used to convert textual input into image embeddings.

C.  

CLIP provides a common embedding space for both the textual and image modalities.

D.  

CLIP is used to enhance datasets through data augmentation for text-to-image generation.

A.  

Training a model using multiple precision levels, such as using both single-precision and double-precision floating-point numbers.

B.  

Training a model using diverse data types while addressing challenges related to missing or incomplete information.

C.  

Training a model using different types of data, such as text, images, audio, time series, and geospatial information.

D.  

Training a model using incomplete or missing information from different modalities.

A.  

To generate new data based on the training set.

B.  

To distinguish between real and generated data.

C.  

To optimize the training process.

D.  

To calculate the loss function and update the generator.

A.  

NVIDIA Metropolis

B.  

NVIDIA NeMo

C.  

NVIDIA DeepStream

D.  

NVIDIA Clara