NCA-GENM Generative AI Multimodal Exam Preparation Resources

To earn Generative AI Multimodal certification, you need to take and pass NCA-GENM exam. Learning NVIDIA NCA-GENM exam information is a required step. There are 50-60 multiple-choice questions in real NCA-GENM exam, and you have 1 hour to answer all the questins. The available language is English. The exam price is $125. NCA Generative AI Multimodal certification is valid for two years from issuance. We provide the latest Generative AI Multimodal NCA-GENM exam preparation resources, which are useful for you to take the test. Free Generative AI Multimodal NCA-GENM exam preparation resources are available below.

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1. What does 'modality alignment' refer to?

The integration of pretrained models to perform custom tasks involving different types of data.

The process of integrating diverse data types such as text, images, audio, time series, and geospatial information.

Addressing challenges related to missing or incomplete information across different modalities.

Aligning different modalities within multimodal data to ensure meaningful connections and associations.

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2. In multimodal machine learning, what does 'early fusion' refer to?

Integrating different modalities at the beginning of the model pipeline.

Ignoring certain modalities and only using one modality for analysis and prediction.

Training separate models for each modality and then combining their predictions.

Implementing the model in the early stages of development of the ML solution.

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3. Which metric is commonly used for evaluating Automatic Speech Recognition (ASR) models?

CTC Loss

F1 Score

Mean Opinion Score (MOS)

Word Error Rate (WER)

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4. How does CLIP understand the content of both text and images?

By converting text and images into a frequency domain for comparison.

Using contrastive learning to match images with text descriptions.

By translating images into text and comparing them with the prompt.

Through a database of predefined images with their descriptions.

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5. You are working with a large dataset and want to visualize the distribution of a continuous variable.

Which type of data visualization would be most appropriate?

Histogram chart

Bar chart

Line chart

Pie chart

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6. During the process of data cleansing, which of the following steps is NOT typically performed?

Identifying and handling missing values

Transforming data into a different format

Collecting additional data

Removing duplicates

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7. What is a main application of Triton Inference Server?

Triton Server can be used to generate images from pure noise.

Triton Server can be used to deploy Al models on the GPU only.

Triton Server can be used to execute GPU-accelerated graph analysis with cuGraph.

Triton Server can be used to deploy neural networks from various frameworks.

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8. Which visualization technique is suitable for representing the distribution of performance scores for different multimodal ML models over different modalities?

Heatmap

Histogram

Box plot

Pie chart

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9. You are developing a ML model for image classification. You have a dataset with 10,000 images of cats, dogs and birds.

Which of the following ML models would be the most appropriate choice for this task?

Logistic Regression

K-Means Clustering

Linear Regression

Convolutional Neural Network (CNN)

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10. Which framework is used for conversational Al models development?

NVIDIA Metropolis

NVIDIA Nemo

NVIDIA Deep Stream

NVIDIA Clara

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11. What is the role of (Contrastive Language-Image Pretraining)CLIP in-text-to-image generation?

CLIP is used to generate image captions from textual input.

CLIP is used to convert textual input into image embeddings.

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

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

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12. In a multimodal machine learning context, how are different modalities usually linked to each other?

Different modalities are linked through a shared representation that captures the relationships between the modalities.

Different modalities are linked through random connections.

Different modalities are linked through separate models that are ensembled by tree-based models.

Different modalities are not linked to each other in a multimodal machine learning context.

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13. In machine learning, what is the purpose of data normalization?

To remove irrelevant data from the dataset.

To increase the complexity of the dataset.

To convert data into a specific format for easier analysis.

To reduce the dimensionality of the dataset.

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14. You are developing a GenAI-Multimodal system that uses data from various sources.

What is one potential issue you need to consider in relation to bias in data?

The data used to train the Al system may not be representative of the population it is intended to serve.

Bias in data is irrelevant as long as the Al system produces accurate predictions.

Bias in data can only be addressed after the Al system has been deployed.

Bias in data is not a concern for Al systems as they are designed to be neutral and objective.

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15. What does mixed-precision training refer to?

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

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

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

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

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