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NCA-GENM認定試験を受験したいですか。NCA-GENM認証資格を取得したいですか。試験に準備する時間が足りないあなたは、どうやって試験に合格できますか。しようがないわけではないです。短時間の準備でも楽に試験に合格することができるようになりますよ。それでは、どのようにすればそれを達成できますか。実は方法がとても簡単です。すなわちCertJukenのNCA-GENM問題集を利用して試験の準備をすることです。
| Section | Objectives |
|---|---|
| Responsible and Trustworthy AI | - Ethical AI principles - Bias and safety considerations |
| Core AI and Machine Learning Fundamentals | - Machine learning basics
|
| Generative AI Concepts | - Generative models
|
| Multimodal AI Systems | - Cross-modal learning
|
| NVIDIA AI Ecosystem | - NVIDIA tools and frameworks
|
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質問 # 50
You're using Stable Diffusion with a custom prompt to generate images of landscapes. You notice that the generated images consistently lack detail and appear blurry, despite increasing the number of inference steps. Which of the following prompt engineering techniques, combined with appropriate parameter tuning, is MOST likely to address this issue and improve the image's sharpness and detail?
正解：B
解説：
Adding keywords specifically related to image quality ('photorealistic', 'high resolution', '8k', 'detailed') helps guide the model towards generating sharper and more detailed images. 'clip_skip' influences the model to incorporate more details into the output images. Adjusting 'clip_skip' along with quality prompt keywords will enhance the image quality.
質問 # 51
You are building a multimodal Generative AI model that takes text and images as input to generate a story. The text encoder uses a pre-trained BERT model, and the image encoder uses a pre-trained ResNet50 model. What is the BEST strategy to align the feature spaces of these two encoders during training to ensure effective multimodal fusion?
正解：D
解説：
Contrastive learning is a powerful technique for aligning feature spaces in multimodal learning. By encouraging similar representations for semantically related inputs and dissimilar representations for unrelated inputs, it allows the model to learn a shared representation space that facilitates effective fusion. Fine- tuning both encoders allows for adaptation to the specific task. Other methods are less effective for aligning high-dimensional feature spaces from different modalities.
質問 # 52
You're developing a multimodal A1 system that takes image data, text descriptions, and user interaction data (clicks, dwell time) to generate personalized product recommendations. To effectively combine these modalities and capture complex relationships, which model architecture would be most suitable?
正解：E
解説：
Deep learning architectures with attention mechanisms and cross-modal fusion layers are best suited for capturing complex relationships between different modalities. Attention mechanisms allow the model to focus on the most relevant features from each modality, while cross-modal fusion layers enable joint learning and prediction based on the combined representations. Linear regression, decision trees, KNN, and Naive Bayes are less capable of capturing complex, non-linear relationships in multimodal data.
質問 # 53
You are experimenting with different multimodal transformer architectures for a video understanding task. You are using a large pre- trained model and fine-tuning it on your specific dataset. You observe that the model is overfitting and struggling to generalize to unseen videos. Which of the following techniques would be most effective in mitigating overfitting in this scenario? (Choose two)
正解：A、C
解説：
Weight decay and dropout are standard regularization techniques that help prevent overfitting. Data augmentation increases the diversity of the training data, improving the model's ability to generalize. Reducing the number of layers is a potentially viable option, but requires experimentation to achieve optimum performance.
質問 # 54
You're building a multimodal model that predicts customer satisfaction based on their written reviews and associated call center audio recordings. You've pre-trained separate text and audio encoders. What's the MOST effective strategy to fuse these modalities for the final prediction task?
正解：A
解説：
An attention mechanism allows the model to dynamically learn the importance of each modality based on the input. This is more flexible and effective than simple concatenation or averaging (A, B, E), which treat both modalities equally. Fine-tuning only one encoder (C) doesn't leverage the benefits of multimodal fusion. While concatenation (A) is a common starting point, attention provides a more nuanced and powerful way to combine modalities. Adding features (E) is also not effective.
質問 # 55
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