Free PDF Quiz 2026 NVIDIA Pass-Sure NCA-GENM: Latest NVIDIA Generative AI Multimodal Exam Objectives

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NVIDIA NCA-GENM Exam Syllabus Topics:

Section	Weight	Objectives
Trustworthy AI	5%	- Ethical considerations in AI development - Ensuring fairness and transparency
Performance Optimization	10%	- Monitoring and improving system efficiency - Techniques for optimizing AI performance
Software Development & Engineering	15%	- Integration and deployment of multimodal AI systems - Python libraries for multimodal AI
Experimentation	25%	- A/B testing - Model evaluation and comparison - Experimental design - Hypothesis testing
Multimodal Data	15%	- Handling and integrating text, image, and audio data - Applications and use cases
Data Analysis & Visualization	10%	- Data preprocessing and feature engineering - Visualization techniques for multimodal data
Core ML & AI Knowledge	20%	- Basic concepts and terminology - Key algorithms and techniques

>> Latest NCA-GENM Exam Objectives <<

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NVIDIA Generative AI Multimodal Sample Questions (Q20-Q25):

NEW QUESTION # 20
You are fine-tuning a pre-trained language model for a specific task. You notice that the model performs well on the training data but poorly on the validation dat a. Which of the following techniques can help mitigate this overfitting problem? (Select TWO)

A. Increase the learning rate.
B. Apply weight decay (L2 regularization).
C. Decrease the batch size.
D. Use dropout regularization.
E. Increase the size of the training data.

Answer: B,D

Explanation:
Overfitting occurs when a model learns the training data too well and fails to generalize to new data. Weight decay (L2 regularization) penalizes large weights, preventing the model from becoming too complex. Dropout randomly deactivates neurons during training, forcing the model to learn more robust features. Increasing the learning rate might worsen overfitting. Decreasing the batch size can sometimes act as a regularizer, but its primary effect is on the training dynamics. While more training data is generally beneficial, if the new data is very similar to the existing training data, it won't necessarily solve the overfitting issue.

NEW QUESTION # 21
You are fine-tuning a pre-trained multimodal model for a new task. You have limited computational resources. Which of the following fine-tuning strategies would be the MOST computationally efficient while still achieving good performance?

A. Train a new random model from scratch for the task, which will avoid the need to load the pre-trained model.
B. Freeze all layers except the classification head and fine-tune only the classification head.
C. Freeze the lower layers of the model and fine-tune the upper layers and the classification head.
D. Fine-tune all the layers of the model.
E. Randomize the model to train, if it improves the training rate.

Answer: C

Explanation:
Freezing the lower layers and fine-tuning the upper layers and classification head strikes a balance between computational efficiency and performance. The lower layers typically capture more general features that are less specific to the task, while the upper layers capture more task-specific features. Freezing the lower layers reduces the number of trainable parameters, making the fine-tuning process more computationally efficient. Fine-tuning all layers is computationally expensive, freezing all layers except the classification head might not be sufficient for adapting to the new task, and training from scratch does not leverage the knowledge learned during pre-training. Randomizing model is not a general practice.

NEW QUESTION # 22
You are building an A1 model that takes video and corresponding subtitles as input to generate short summaries of video content. Which of the following strategies are most important to reduce the chance of your model generating biased summaries? (Select all that apply)

A. Increase the number of training epochs.
B. Ensure the training dataset contains diverse representation of all demographic groups and viewpoints.
C. Evaluate the model's summaries on different demographic groups to identify and mitigate any disparities in performance.
D. Randomly shuffle data during training.
E. Use a pre-trained language model that has been debiased.

Answer: B,C,E

Explanation:
Debiasing pre-trained models helps remove existing biases. A diverse training dataset is important to reduce the influence of any single biased viewpoint. Evaluating model performance on different demographic groups allows you to find and rectify performance disparities. Random data shuffling (D) and increasing training epochs (E) do not directly address bias. Note this is a very tough question as all choices seem viable but only options, A, B and C are the correct choice.

NEW QUESTION # 23
Consider a scenario where you're integrating CLIP with a generative model to create images from text prompts. Which of the following best describes the primary role of CLIP in this process?

A. To encode text prompts into a vector representation that guides the image generation process.
B. To optimize the hyperparameters of the generative model.
C. To decode generated images back into text descriptions.
D. To directly generate images based on text prompts.
E. To act as a discriminator in a GAN setup.

Answer: A

Explanation:
CLIP (Contrastive Language-Image Pre-training) serves as an encoder to map text prompts into a vector space. This vector representation is then used to guide the generative model towards creating images that align with the semantic meaning of the text prompt. CLIP doesn't generate images directly, decode images to text or optimize hyperparameters.

NEW QUESTION # 24
A research team is developing a multimodal model to predict stock prices using financial news articles, company filings (text), historical stock prices (time-series), and executive interviews (audio). They are experiencing significant performance issues due to inconsistent data quality across modalities. What specific strategies would you recommend to address these data quality challenges?

A. Implement audio transcription and sentiment analysis on executive interviews to extract key information and emotional tone.
B. All of the above.
C. Apply Named Entity Recognition (NER) to financial news and company filings to standardize company names and financial terms.
D. Normalize and scale historical stock prices to a consistent range to avoid dominance by high-magnitude values.
E. Focus exclusively on improving the quality of the most readily available data source.

Answer: B

Explanation:
All the options are essential. NER standardizes textual data, audio analysis extracts sentiment, and normalization prevents stock price dominance. Addressing data quality holistically across modalities is key.

NEW QUESTION # 25
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