[2026] NVIDIA Generative AI Multimodal - NCA-GENM 무료 시험 문제

문제1

You are tasked with integrating a CLIP model into your application to generate images based on text descriptions. You want to ensure that the generated images closely reflect the nuances of the text prompt. Which prompt engineering technique is MOST suitable for achieving this?

A. Using random prompts to explore the model's creative capabilities.

B. Using short, concise prompts to minimize ambiguity.

C. Using prompts consisting only of keywords related to the desired image.

D. Using overly verbose and descriptive prompts to maximize detail.

E. Using negative prompts to explicitly exclude unwanted features or styles.

정답: E

설명: (Fast2test 회원만 볼 수 있음)

문제2

You are tasked with optimizing a Generative A1 model that processes both image and text dat a. The current model uses a simple concatenation of image features (extracted from a ResNet-50) and text embeddings (from BERT) as input to a transformer. You observe that the model struggles to generate coherent descriptions for complex images. Which of the following optimization strategies would be MOST effective in improving the model's understanding of the multimodal input?

A. Switch to a larger ResNet architecture (e.g., ResNet-101 ) while keeping the concatenation.

B. Increase the size of the transformer encoder layers.

C. Augment the text data with more examples.

D. Replace concatenation with a cross-attention mechanism between image features and text embeddings.

E. Reduce the learning rate by a factor of 10.

정답: D

설명: (Fast2test 회원만 볼 수 있음)

문제3

You are building a system that uses audio and video to detect emotional states of a user. What are the challenges to this system?

A. Subjectivity in emotional expression across cultures and individuals.

B. Synchronization issues between audio and video streams.

C. Variations in background noise affecting audio quality.

D. Differences in lighting conditions influencing facial expression recognition.

E. All of the above.

정답: E

설명: (Fast2test 회원만 볼 수 있음)

문제4

You're training a conditional GAN (cGAN) to generate images of handwritten digits conditioned on the digit label. You notice that the generated images are blurry and lack fine details, even after extensive training. Which of the following techniques could you implement to improve the sharpness and realism of the generated images?

A. Increase the learning rate of the generator.

B. Increase the dimensionality of the latent space.

C. Add batch normalization layers to the generator and discriminator.

D. Implement a perceptual loss function in addition to the adversarial loss.

E. Use spectral normalization on both the generator and discriminator.

정답: D

설명: (Fast2test 회원만 볼 수 있음)

문제5

You are using a pre-trained language model for text classification. You observe that the model performs well on the training data but poorly on unseen dat a. Which of the following techniques could help improve the model's generalization ability? (Select TWO)

A. Increasing the learning rate.

B. Applying data augmentation techniques (e.g., random synonym replacement, back-translation).

C. Using weight decay (L2 regularization).

D. Decreasing batch size.

E. Decreasing the amount of training data.

정답: B,C

설명: (Fast2test 회원만 볼 수 있음)

문제6

You are building a Generative A1 application that processes images and text. The image data has missing pixel values, and the text data contains inconsistencies in abbreviations. Which data preprocessing techniques are MOST suitable to address these issues effectively?

A. Image: Deleting rows with missing pixel values; Text: Removing all abbreviations from the text data.

B. Image: Mean imputation for missing pixels; Text: Standardizing abbreviations using a predefined mapping.

C. Image: Replacing missing pixels with zero; Text: Ignoring abbreviations during analysis.

D. Image: Median imputation for missing pixels; Text: Using a fuzzy matching algorithm to correct inconsistencies in abbreviations.

E. Image: KNN imputation for missing pixels; Text: Applying regular expressions to expand abbreviations.

정답: D,E

설명: (Fast2test 회원만 볼 수 있음)

문제7

In the context of multimodal data analysis, which of the following statements accurately describe the challenges associated with data alignment?

A. Misalignment can lead to spurious correlations and reduced model performance.

B. Data alignment is not relevant when using deep learning models.

C. Perfect data alignment is always achievable with proper preprocessing techniques.

D. Data alignment is only necessary when dealing with time-series data.

E. Data alignment ensures that data from different modalities refers to the same event or entity.

정답: A,E

설명: (Fast2test 회원만 볼 수 있음)

문제8

You're developing a multimodal model that combines text and audio for sentiment analysis. The text component is performing well, but the audio component contributes very little to the overall accuracy. What's the MOST likely reason and how could you address it?

A. The audio data is irrelevant. Remove the audio component entirely.

B. The audio features are not properly aligned with the text features. Use a cross-modal attention mechanism to improve alignment.

C. The audio data is not preprocessed correctly. Apply aggressive noise reduction techniques.

D. The text component is simply too dominant. Reduce the weight given to the text component in the final prediction.

E. The audio data is too large. Downsample the audio data to reduce computational cost.

정답: B

설명: (Fast2test 회원만 볼 수 있음)

문제9

You're developing a multimodal model that takes both image and audio inputs to predict a relevant text description. You observe that the model is heavily biased towards the image data, effectively ignoring the audio input. Which of the following techniques could you employ to address this modality imbalance and ensure the model effectively utilizes both input modalities?

A. Apply modality-specific dropout to the image pathway.

B. Reduce the dimensionality of the image features before fusion.

C. Oversample the audio data during training.

D. Increase the learning rate for the audio modality pathway during training.

E. Increase the batch size for each epoch.

정답: A,B,C,D

설명: (Fast2test 회원만 볼 수 있음)

문제10

Consider a multimodal A1 system that generates recipes based on images of ingredients. The system uses attention maps to highlight the relevant ingredients in the image. You observe that the attention maps are often noisy and highlight irrelevant parts of the image, leading to incorrect recipes. Which of the following strategies could BEST improve the quality and interpretability of the attention maps?

A. Apply L1 regularization to the attention weights to encourage sparsity.

B. Use a stronger image encoder, such as a larger ResNet or a Vision Transformer.

C. All of the above can improve the quality and interpretability of the attention maps.

D. Add more layers to the attention module.

E. Increase the size of the convolutional filters in the image encoder.

정답: A,B

설명: (Fast2test 회원만 볼 수 있음)

문제11

Consider a scenario where you are using a pre-trained multimodal model for image captioning and want to fine-tune it on a specific dataset. Which of the following strategies is MOST likely to lead to improved performance and faster convergence?

A. Fine-tune the entire model (image encoder and captioning head) with a very large learning rate.

B. Train a new captioning head from scratch while keeping the image encoder frozen.

C. Fine-tune the entire model with a smaller learning rate and gradually unfreeze layers, starting from the captioning head.

D. Randomly initialize the entire model and train from scratch.

E. Fine-tune only the captioning head (language model) while keeping the image encoder frozen.

정답: C

설명: (Fast2test 회원만 볼 수 있음)

문제12

You are working with a transformer-based multimodal model that processes both text and audio. You want to implement an efficient attention mechanism that reduces the computational cost associated with attending to the entire input sequence. Which of the following attention mechanisms would be MOST suitable for achieving this goal?

A. Scaled Dot-Product Attention

B. Global Attention

C. Multi-Head Attention

D. Sparse Attention

E. Local Attention

정답: D

설명: (Fast2test 회원만 볼 수 있음)

문제13

Which of the following are valid techniques for dealing with overfitting in a deep learning model trained on image data?

A. Adding Ll or L2 regularization.

B. Reducing the amount of training data

C. Implementing dropout layers.

D. Increasing the complexity of the model.

E. Using data augmentation techniques.

정답: A,C,E

설명: (Fast2test 회원만 볼 수 있음)

NVIDIA Generative AI Multimodal - NCA-GENM무료 덤프문제 풀어보기

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