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NVIDIA NCA-GENM Exam Syllabus Topics:

Section	Objectives
NVIDIA AI Ecosystem	- NVIDIA tools and frameworks 1. GPU-accelerated AI workflows 2. NeMo framework usage
Core AI and Machine Learning Fundamentals	- Machine learning basics 1. Neural networks fundamentals 2. Supervised and unsupervised learning
Multimodal AI Systems	- Multimodal model design - Cross-modal learning 1. Text-image integration 2. Audio-visual understanding
Generative AI Concepts	- Generative models 1. Transformers and LLM basics 2. Diffusion models
Responsible and Trustworthy AI	- Ethical AI principles - Bias and safety considerations

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NVIDIA Generative AI Multimodal Sample Questions (Q24-Q29):

NEW QUESTION # 24
You are building a system that identifies objects in images based on spoken commands. You have trained a model but notice that it performs poorly when the spoken command contains synonyms or paraphrases of the training data. Which of the following techniques would BEST address this issue?

A. Employing a word embedding model (e.g., Word2Vec, GloVe) or contextual embeddings (e.g., BERT) to represent the spoken commands, allowing the model to generalize to semantically similar phrases.
B. Simplifying the spoken commands to use only a limited vocabulary.
C. Using data augmentation techniques such as rotating and scaling the images.
D. Reducing the learning rate of the model.
E. Increasing the size of the training dataset.

Answer: A

Explanation:
Option C is the most effective solution. Word embeddings or contextual embeddings capture the semantic meaning of words and phrases, enabling the model to understand synonyms and paraphrases. Options A, B, D, and E address different issues but are not directly related to the semantic understanding of spoken commands. Increasing data size (A) might help, but semantic understanding is key. Image augmentation (B) is irrelevant to the text understanding. Simplifying commands (D) limits the system's capabilities. Reducing learning rate (E) might improve training stability but doesn't address the semantic understanding.

NEW QUESTION # 25
You are building a system that translates sign language videos into spoken text. You have a dataset of videos and corresponding text transcriptions. You notice that the test data contains significant variations in lighting conditions and camera angles compared to the training dat a. Which of the following techniques would be MOST effective in addressing this domain shift and improving the generalization of your model?

A. Only evaluate on a subset of the test data that closely resembles the training data.
B. Use a domain adaptation technique such as Domain Adversarial Neural Networks (DANN) to learn domain-invariant features.
C. Reduce the size of the model to prevent overfitting to the training data.
D. Fine-tune the model on a small subset of the test data to adapt to the specific characteristics of the test distribution.
E. Apply aggressive data augmentation techniques to the training data, including random crops, rotations, and color jittering to simulate the variations in the test data.

Answer: B

Explanation:
Domain adaptation techniques (C) are specifically designed to address domain shift by learning features that are invariant to the source and target domains. Data augmentation (A) can help but might not be sufficient. Fine-tuning on test data (B) is data leakage and invalidates the test set. Reducing model size (D) may not address the core issue of domain shift. Selecting a subset of the test data (E) defeats the purpose of testing generalization.

NEW QUESTION # 26
Which metric is commonly used to evaluate machine-translation models?

A. BLEU score
B. Accuracy
C. F1 score
D. Mean Absolute Error (MAE)

Answer: A

Explanation:
BLEU (Bilingual Evaluation Understudy) is the standard automatic metric for evaluating machine translation quality. It measures n-gram precision - the overlap of contiguous word sequences (unigrams through typically 4-grams) between the model's translated output and one or more human reference translations - combined with a brevity penalty to discourage overly short translations that could otherwise achieve artificially high precision. BLEU scores range from 0 to 1 (or 0-100 as a percentage), with higher scores indicating closer alignment to reference translations.
The distractors represent metrics standard to other task families: F1 score (A) evaluates classification tasks by balancing precision and recall over discrete positive/negative predictions, ill-suited to open-ended text generation where there is no fixed set of "correct" tokens. Accuracy (B) similarly assumes a discrete correct
/incorrect judgment, inappropriate for translation where multiple valid phrasings can convey the same meaning. Mean Absolute Error (C) is a regression metric measuring average magnitude of numeric prediction error, irrelevant to text output evaluation entirely.
It's worth noting BLEU has known limitations - it correlates imperfectly with human judgments of fluency and can penalize valid paraphrases - which has motivated complementary metrics like METEOR, ROUGE (more common for summarization), and learned metrics like BERTScore, though BLEU remains the benchmark most commonly referenced for translation specifically.
Reference: Core Machine Learning and AI Knowledge / Multimodal Data domains - task-specific evaluation metrics (BLEU for translation, WER for ASR, MOS for TTS).

NEW QUESTION # 27
Consider the following code snippet used within a U-Net architecture. What is its purpose?
torch.cat ([up, skip], dim=1)

A. It performs a matrix multiplication between the 'up' and 'skip' tensors.
B. It concatenates the 'up' and 'skip' tensors along the channel dimension.
C. It multiplies the 'up' and 'skip' tensors element-wise.
D. It subtracts the 'skip' tensor from the 'up' tensor.
E. It performs an element-wise addition of the 'up' and 'skip' tensors.

Answer: B

Explanation:
The 'torch.cat([up, skip], dim=1) function concatenates two tensors, 'up' and 'skip' , along the channel dimension (dim=1) In the context of a U-Net, 'up' represents the upsampled feature map from the decoder path, and 'skip' represents the corresponding feature map from the encoder path. Concatenating them allows the decoder to combine both coarse-grained and fine-grained information for better image reconstruction.

NEW QUESTION # 28
You are experimenting with a multimodal model that takes both text and audio as input. During evaluation, you notice that the model is heavily biased towards the text input, largely ignoring the audio. Which of the following techniques could you employ to mitigate this modality imbalance and encourage the model to effectively utilize both inputs? (Select all that apply)

A. Replace audio features with raw audio waveform.
B. Use a contrastive loss function that encourages alignment between text and audio representations.
C. Increase the learning rate for the audio encoder.
D. Reduce the size of the text encoder.
E. Apply modality-specific dropout to the text encoder.

Answer: B,E

Explanation:
Modality imbalance is a common issue in multimodal learning. Applying modality-specific dropout to the dominant modality (text, in this case) forces the model to rely more on the other modality (audio). A contrastive loss directly encourages the model to learn aligned representations between the two modalities. Increasing the audio encoder's learning rate (A) might help, but it is less targeted than dropout or contrastive loss. Reducing the text encoder size (D) is unlikely to be helpful in a controlled way. Replacing Audio features with raw waveform might introduce noise.

NEW QUESTION # 29
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