적중율좋은NCA-GENM시험유형시험자료

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NVIDIA NCA-GENM Exam Syllabus Topics:

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

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NCA-GENM최신 업데이트버전 인증시험자료 - NCA-GENM최고품질 시험덤프자료

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최신 NVIDIA-Certified Associate NCA-GENM 무료샘플문제 (Q49-Q54):

질문 # 49
You are training a multimodal model that combines text and images. You observe that the model is heavily biased towards the text modality and largely ignores the image data. Which of the following strategies could you use to address this modality imbalance? (Select all that apply)

A. Oversample the image data during training.
B. Increase the learning rate for the image-related parameters of the model.
C. Decrease the learning rate for the text-related parameters of the model.
D. Reduce the dimensionality of the image features to match the dimensionality of the text embeddings.
E. Use a modality-specific loss weighting scheme, assigning a higher weight to the loss component derived from the image data.

정답：A,B,C,E

설명：
Options A, B, C, and D are effective strategies for addressing modality imbalance. Increasing the learning rate for the underutilized modality (image) or decreasing it for the dominant one (text) encourages the model to pay more attention to the image data. Modality-specific loss weighting directly emphasizes the importance of the image modality during training. Oversampling image data ensures that the model sees more image examples and is less likely to be biased towards the text modality. Option E might lead to loss of important information from the image modality.

질문 # 50
You are building a system that uses text and images to generate 3D models. The text describes the object, and the images provide visual details. During training, you observe that the model heavily relies on the image input and largely ignores the text description. What technique can you employ to encourage the model to give more weight to the textual input?

A. Apply a higher dropout rate to the image embedding layer.
B. Increase the size of the text vocabulary.
C. Decrease the learning rate for the image processing branch of the model.
D. Increase the resolution of the input images.
E. Use a curriculum learning approach, starting with simpler text descriptions and gradually increasing the complexity.

정답：A,E

설명：
Explanation:A
Applying a higher dropout rate (B) to the image embedding forces the model to rely less on the image features. Curriculum learning (E) allows the model to first learn to associate simpler text descriptions with corresponding visual features, then gradually more complex descriptions are introduced.

질문 # 51
You are tasked with creating a multimodal AI application that analyzes social media posts containing text, images, and user profile information to predict the likelihood of a post going viral. Which feature engineering techniques are most effective for representing and integrating these different modalities?

A. Using a combination of TF-IDF for text, pixel values for images, and numerical features for user profile information. Then apply PCA for dimensionality reduction.
B. Using TF-IDF for text, pixel values for images, and one-hot encoding for user profile information.
C. Using character-level n-grams for text, edge detection for images, and boole an features for user profile information.
D. Using word embeddings (e.g., Word2Vec, GloVe) for text, pre-trained CNN features (e.g., from ResNet, Inception) for images, and embedding user profiles using a graph embedding technique.
E. Using bag-of-words for text, histogram of oriented gradients (HOG) for images, and simple numerical features (e.g., number of followers) for user profiles.

정답：D

설명：
Using word embeddings captures semantic meaning in text. Pre-trained CNN features provide high-level image representations. Graph embedding for user profiles captures relationships between users. These advanced techniques provide better representations than simple methods like TF-IDF, pixel values, or bag-of-words.

질문 # 52
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. Reduce the size of the model to prevent overfitting to the training data.
C. Apply aggressive data augmentation techniques to the training data, including random crops, rotations, and color jittering to simulate the variations in the test 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. Use a domain adaptation technique such as Domain Adversarial Neural Networks (DANN) to learn domain-invariant features.

정답：E

설명：
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.

질문 # 53
You are working on a project that involves analyzing customer reviews which contains the following dataset: 1. customer_id(categorical) 2. customer_review(text) 3. product_image(image) 4. video_of_product_usage(video) What is the best way to handle and address the problem of skewness across each modailities?

A. Do nothing about the skewness, as the model will learn to adapt to the imbalanced data distribution.
B. Balance the dataset by oversampling under-represented data points within each modality independently.
C. Apply modality-specific weighting schemes that assign higher weights to modalities with less representation.
D. Design a loss function that explicitly penalizes the model for being biased towards dominant modalities.
E. Treat all modalitites with equal weights during model training, ignoring potential skewness issues.

정답：B,C,D

설명：
Addressing skewness is crucial for preventing the model from being biased towards dominant modalities. Oversampling, modality- specific weighting, and a biased-aware loss function are all effective strategies for mitigating this problem.

질문 # 54
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