FREE PDF QUIZ 2025 NVIDIA NCA-GENM–THE BEST TEST TOPICS PDF

Free PDF Quiz 2025 NVIDIA NCA-GENM–The Best Test Topics Pdf

Free PDF Quiz 2025 NVIDIA NCA-GENM–The Best Test Topics Pdf

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Tags: NCA-GENM Test Topics Pdf, Reliable NCA-GENM Study Guide, New NCA-GENM Exam Simulator, Latest NCA-GENM Exam Camp, Guaranteed NCA-GENM Passing

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NVIDIA Generative AI Multimodal Sample Questions (Q172-Q177):

NEW QUESTION # 172
You're working with a client to develop a generative A1 model for creating personalized marketing content. During requirements acquisition, the client expresses a desire for 'highly creative' and 'unique' outputs. However, they struggle to articulate specific aesthetic preferences. How would you best approach translating these subjective requirements into concrete model training and prompt engineering strategies?

  • A. B and D
  • B. Use a pre-trained style transfer model to apply different artistic styles to the generated content, offering the client a diverse range of options to choose from and identify their preferred aesthetic.
  • C. Implement a system for interactive prompt refinement, allowing the client to iteratively modify prompts and observe the resulting outputs in real-time, facilitating a collaborative exploration of the model's creative potential.
  • D. Focus solely on quantitative metrics like perplexity and FID score to ensure the model generates diverse and high-quality content, assuming that 'creative' and 'unique' will naturally emerge.
  • E. Conduct extensive A/B testing with a large user group, presenting them with various model outputs and gathering feedback on which content they perceive as most 'creative' and 'unique'. Use this feedback to refine the model and prompts.

Answer: A

Explanation:
Subjective requirements like 'creative' and 'unique' require iterative exploration and client feedback. AIB testing (B) provides quantitative data on user perception. Interactive prompt refinement (D) allows the client to actively shape the model's output and discover their preferences. Quantitative metrics alone (A) are insufficient for capturing subjective qualities. Style transfer (C) can be helpful but doesn't directly address the client's specific vision. Thus, iterative AIB testing and Interactive prompt refinement would be the ideal method.


NEW QUESTION # 173
You are building a multimodal model to predict stock prices using financial news articles (text), historical stock prices (time-series), and company logos (images). You have preprocessed the data and are ready to train your model. Which of the following architectures would be MOST suitable for effectively integrating these three modalities?

  • A. A model that combines a Transformer for text, an LSTM for time-series, and a CNN for images, with a late fusion strategy using a weighted averaging of predictions.
  • B. Separate models for each modality trained independently, and then ensembled together at the prediction stage.
  • C. A simple feed forward neural network with concatenated features from all modalities.
  • D. A model that converts all data into a single text format and uses a large language model (LLM) for prediction.
  • E. A model that uses a Transformer encoder for each modality, followed by a shared Transformer decoder for prediction, enabling cross-modal attention at the decoder level.

Answer: A,E

Explanation:
Combining a Transformer for text, an LSTM for time-series, and a CNN for images with a late fusion approach allows each modality to be processed by a suitable architecture and then combined to generate a final prediction. Using transformers in each modality with shared Transformer decoder can efficiently integrate and predict stock prices using cross modal attention . A simple feedforward network is unlikely to capture the temporal dependencies in the time-series data or the complex relationships between modalities. Ensembling independent models doesn't allow for cross-modal learning. Converting all data into text might lose valuable information from the other modalities. Therefore, hybrid architecture combining transformers, LSTMs, and CNNs with cross-modal attention or late fusion would be most effective.


NEW QUESTION # 174
Consider a multimodal emotion recognition system that uses both facial expressions and speech audio as input. You want to fuse the information from these two modalities. Which of the following fusion techniques would be most suitable if the modalities have significantly different temporal resolutions (e.g., facial expressions change more rapidly than overall vocal tone)?

  • A. Feature Extraction (extracting features)
  • B. Intermediate Fusion (using attention mechanisms to align features)
  • C. Decision Fusion (majority voting based on modality predictions)
  • D. Late Fusion (averaging probabilities from individual classifiers)
  • E. Early Fusion (concatenating raw features)

Answer: B

Explanation:
Intermediate fusion, particularly with attention mechanisms, is well-suited for modalities with different temporal resolutions. Attention allows the model to dynamically align and weight the features from each modality based on their relevance at different time steps, addressing the temporal misalignment issue. Early fusion would be problematic as the temporal differences are not handled. Late fusion ignores the potential interactions between the modalities. Decision fusion suffers from the same issues as late fusion. Feature extraction is not fusion technique.


NEW QUESTION # 175
When working with geospatial data in conjunction with text data (e.g., analyzing tweets related to specific geographical locations), what are some of the key challenges in terms of data curation and quality assessment, and how can these challenges be addressed?

  • A. The sparsity of geospatial data in certain regions, which can be mitigated by using spatial interpolation techniques to estimate values in unobserved areas.
  • B. The lack of tools for analyzing geospatial data with textual information, requiring custom software development.
  • C. Different coordinate systems and projections used in geospatial datasets, which can be resolved by transforming all data to a common coordinate system.
  • D. Inaccurate or ambiguous geolocation information in text data, which can be addressed by using geocoding services and verifying location accuracy with external data sources.
  • E. Geospatial data is inherently accurate and requires no specific curation or quality assessment.

Answer: A,C,D

Explanation:
Geospatial data often suffers from inaccuracies, inconsistencies in coordinate systems, and sparsity. Addressing these challenges requires geocoding, coordinate system transformations, and spatial interpolation techniques. Many tools are available for geospatial-textual analysis.


NEW QUESTION # 176
You are building a real-time multimodal system that processes live video and audio streams to detect potentially dangerous situations. Latency is a critical constraint. Which of the following strategies is MOST important to minimize latency in this system?

  • A. Optimizing the model architecture for efficient computation, using techniques like model quantization, knowledge distillation, and reducing the number of layers.
  • B. Converting the video stream to text transcripts before processing.
  • C. Employing extensive data augmentation during training.
  • D. Using a very deep neural network to achieve the highest possible accuracy, regardless of latency.
  • E. Using a large batch size during inference to maximize GPU utilization.

Answer: A

Explanation:
Minimizing latency requires optimizing the model for efficient computation. Techniques like model quantization (reducing the precision of the weights), knowledge distillation (transferring knowledge from a larger model to a smaller one), and reducing the number of layers can significantly reduce the computational cost and inference time. Large batch sizes increase latency, and deep networks generally have higher latency due to increased computations.


NEW QUESTION # 177
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