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NVIDIA Generative AI Multimodal Sample Questions:

1. Consider the following Python code snippet using Triton Inference Server's Python client. The code intends to send a request to a model that expects two input tensors: 'input_image' (shape: [1, 3, 224, 224], datatype: FP32) and 'input_text' (shape: [1 ,], datatype: BYTES). Identify potential issues in this code that could prevent successful inference.

A) The function is used incorrectly; it should directly accept the Triton datatype string (e.g., 'FP32').
B) The model name and input/output names must be specified, but they are missing in the code.
C) The input data for 'input_text' needs to be encoded to bytes using UTF-8 encoding before being passed to Triton.
D) All of the above.
E) The data is not converted to the appropriate NumPy datatype before being sent to Triton.

2. Consider a scenario where you are evaluating the performance of a multimodal A1 model that generates descriptions for images. However, the generated descriptions tend to be repetitive and lack diversity. Which of the following techniques can be employed to address this issue and encourage more diverse and creative outputs from the model? (Select TWO)

A) Increasing the model's training data size.
B) Utilizing a temperature scaling parameter during decoding and increasing its value.
C) Using a beam search decoding strategy with a small beam width.
D) Employing a nucleus sampling (top-p sampling) decoding strategy.
E) Using a greedy decoding strategy.

3. You are building a multimodal model for medical image diagnosis, using both radiology images (e.g., X-rays) and patient clinical notes.
The clinical notes are highly unstructured and contain significant medical jargon. What preprocessing steps would be MOST effective for improving the model's performance?

A) Applying basic text cleaning (removing punctuation, converting to lowercase) and using a standard word embedding (e.g., Word2Vec).
B) Translating the clinical notes into multiple languages and then back-translating to the original language.
C) Utilizing named entity recognition (NER) to identify medical entities (diseases, medications, etc.), and employing a medical-specific language model (e.g., BioBERT) for text embeddings.
D) Performing sentiment analysis on the clinical notes.
E) Directly feeding the raw clinical notes into the model without any preprocessing.

4. You are developing a multimodal generative A1 model that takes both image and text inputs. The image branch uses a ResNet50 pre- trained on ImageNet, while the text branch uses a BERT model. To effectively combine the features, you need to align their representations. Which of the following techniques is MOST suitable for projecting the image and text features into a common embedding space?

A) Fine-tuning the entire ResNet50 and BERT models jointly on the multimodal dataset.
B) Training separate linear projection layers for both ResNet50 and BERT outputs, followed by concatenation.
C) Employing Contrastive Learning with a shared embedding space and using positive and negative pairs of image and text.
D) Using Principal Component Analysis (PCA) to reduce the dimensionality of ResNet50 and BERT features before concatenation.
E) Direct concatenation of ResNet50 and BERT output features.

5. You are training a multimodal model to predict stock prices using news articles (text) and historical price charts (images). You notice the model is overfitting to the historical price charts and largely ignoring the news articles. What is a potential solution to mitigate this overfitting?

A) Use a simpler model architecture for processing text.
B) Increase the learning rate for the image processing component of the model.
C) Remove the image data entirely to prevent overfitting.
D) Apply stronger regularization (e.g., dropout, Ll/L2 regularization) to the image processing component and/or increase the weight of the text-based loss function.
E) Reduce the batch size for the text data.

Solutions: