NVIDIA NCA-GENM : NVIDIA Generative AI Multimodal

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NVIDIA Generative AI Multimodal NCA-GENM Prüfungsfragen mit Lösungen:

1. Which of the following Python code snippets correctly demonstrates how to load pre-trained word embeddings (e.g., GloVe or Word2Vec) using spaCy and then calculate the cosine similarity between two words?

A)

B)

C)

D)

E)

2. Consider a scenario where you are building a multimodal model to generate realistic indoor scenes. You have access to text descriptions of the scene, 3D models of furniture, and ambient sound recordings. Which of the following loss functions would be most appropriate to ensure coherence and realism in the generated scenes?

A) KL Divergence loss between the generated sound and the input text.
B) A combination of adversarial loss (GAN) to ensure realism, a perceptual loss to match high-level features, and a semantic consistency loss to align the generated image with the input text description.
C) Cross-entropy loss for classifying different object categories in the scene.
D) Cosine similarity loss between the generated image and the input 3D models.
E) Mean Squared Error (MSE) between the generated image and a reference image.

3. You are developing a multimodal model that combines text and tabular data for predicting customer churn. The text data consists of customer reviews, and the tabular data includes demographics and transaction history. You've preprocessed both datasets. Which of the following approaches would be the MOST effective for integrating these modalities?

A) All of the above.
B) Use a Transformer-based model to encode the text and a separate neural network for the tabular data, then fuse the embeddings.
C) Concatenate the raw text and tabular data into a single feature vector.
D) Convert the text data into numerical features using techniques like TF-IDF, then concatenate these features with the tabular data.
E) Train separate models for text and tabular data, then average their predictions.

4. You are building a multimodal Generative A1 system to generate image captions based on both the visual content of an image and a short audio description of the scene. Which architectural approach would be MOST effective for fusing these two modalities into a coherent representation for caption generation?

A) Intermediate Fusion: Train separate image and audio encoders, then use cross-attention mechanisms to allow the image features to attend to the audio features (and vice-versa) at multiple layers of the model.
B) Concatenate the image file name with the audio file name before feeding into the LLM.
C) Late Fusion: Train separate image and audio encoders, then concatenate their high-level feature vectors before feeding into a caption generation model.
D) Early Fusion: Concatenate the raw image pixel data with the raw audio waveform data before feeding it into a single model.
E) Ignore the audio entirely, as images are sufficient for generating captions.

5. You're building a chatbot that can understand both text and images. The chatbot is intended to answer questions about images uploaded by users. However, you observe that when presented with complex scenes containing multiple objects, the chatbot struggles to accurately identify and describe the objects being queried. Which of the following strategies would be MOST beneficial in improving the chatbot's performance on complex visual scenes?

A) Integrate an object detection model to identify and localize objects in the image before feeding the information to the chatbot.
B) Train the chatbot on a dataset with only simple images containing a single object.
C) Remove the image processing component entirely.
D) Use a larger language model for the chatbot.
E) Reduce the resolution of the input images.

Fragen und Antworten: