2025 NCA-GENM EXAM QUESTIONS PDF 100% PASS | VALID NCA-GENM: NVIDIA GENERATIVE AI MULTIMODAL 100% PASS

2025 NCA-GENM Exam Questions Pdf 100% Pass | Valid NCA-GENM: NVIDIA Generative AI Multimodal 100% Pass

2025 NCA-GENM Exam Questions Pdf 100% Pass | Valid NCA-GENM: NVIDIA Generative AI Multimodal 100% Pass

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NVIDIA Generative AI Multimodal Sample Questions (Q72-Q77):

NEW QUESTION # 72
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 directly generate images based on text prompts.
  • C. To act as a discriminator in a GAN setup.
  • D. To decode generated images back into text descriptions.
  • E. To optimize the hyperparameters of the generative model.

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 # 73
You are working with time-series data from IoT sensors alongside video footage from surveillance cameras to detect anomalies in a factory production line. What data preprocessing steps are crucial for effectively integrating and analyzing these modalities in a multimodal AI model?

  • A. Normalizing the time-series data to a consistent range.
  • B. Synchronizing the timestamps of the time-series data and video frames.
  • C. Converting the video footage to grayscale to simplify feature extraction.
  • D. All of the above.
  • E. Downsampling the video footage to reduce computational cost.

Answer: C

Explanation:
All the mentioned steps are crucial. Synchronizing timestamps is essential for temporal alignment. Normalizing time-series data ensures features are on the same scale, preventing bias. Downsampling video reduces computational burden, and grayscale conversion simplifies feature extraction without losing vital information for anomaly detection.


NEW QUESTION # 74
You are building an image generation pipeline that leverages both a U-Net and a pre-trained CLIP model. After generating an image with the U-Net, you want to use CLIP to assess how well the generated image aligns with a given text prompt. Which of the following steps are crucial for obtaining a meaningful similarity score between the image and the text using CLIP?

  • A. Encode the text prompt using CLIP's text encoder.
  • B. Calculate the cosine similarity between the image and text embeddings.
  • C. Fine-tune the CLIP model on your specific image generation task.
  • D. Resize the generated image to a very high resolution.
  • E. Encode the generated image using CLIP's image encoder.

Answer: A,B,E

Explanation:
To assess the alignment between a generated image and a text prompt using CLIP, you need to encode both the image and the text into vector representations using CLIP's respective encoders (image and text encoders). Then, calculate the cosine similarity between these embeddings to quantify their semantic relatedness. Fine-tuning CLIP is not typically necessary for this purpose. High resolution is not mandatory as CLIP works well on medium resolution images and it's embedded space.


NEW QUESTION # 75
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. Increase the size of the transformer encoder layers.
  • B. Reduce the learning rate by a factor of 10.
  • C. Switch to a larger ResNet architecture (e.g., ResNet-101 ) while keeping the concatenation.
  • D. Replace concatenation with a cross-attention mechanism between image features and text embeddings.
  • E. Augment the text data with more examples.

Answer: D

Explanation:
Cross-attention allows the model to learn which parts of the image are most relevant to each word in the text, enabling a more nuanced understanding of the relationship between the two modalities. Concatenation treats all features equally, which is less effective. Increasing transformer size or ResNet architecture might help but doesn't address the core issue of multimodal interaction.


NEW QUESTION # 76
You are integrating a generative A1 model into a client's existing software infrastructure. The client is concerned about data privacy and security. What steps should you take during data gathering, deployment, and integration to address these concerns, while also using NVIDIA tools effectively?
Select all that apply:

  • A. Implement differential privacy techniques during data collection and model training to protect sensitive information. Leverage NVIDIA's Merlin framework for privacy-preserving data preprocessing.
  • B. Implement federated learning, training the generative A1 model on the client's data in a distributed manner without directly accessing or transferring the raw data. Use NVIDIA FLARE for orchestrating the federated learning process.
  • C. Deploy the generative A1 model on-premises within the client's secure network, using Triton Inference Server to ensure controlled access and prevent data leakage.
  • D. Only utilize pre-trained open-source models
  • E. Avoid using any client data for training the generative A1 model, instead relying on publicly available datasets to minimize privacy risks.

Answer: A,B,C

Explanation:
Differential privacy (A) adds noise to the data to protect individual records. On-premises deployment (B) maintains control over data access. Federated learning (D) trains the model on decentralized data without centralizing it. Avoiding client data entirely (C) may limit the model's effectiveness. NVIDIA Merlin and FLARE are tools that provide methods to create safe and private architecture. (E) is not always the best approach since the model might be very generalized and not adapted to specific tasks.


NEW QUESTION # 77
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