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NCA-GENM Valid Exam Dumps, NCA-GENM Valid Test Pdf

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NVIDIA Generative AI Multimodal Sample Questions (Q129-Q134):

NEW QUESTION # 129
You are analyzing a dataset of customer reviews for a Generative A1-powered product. You want to identify the key themes and topics that customers are discussing. Which technique would be MOST appropriate for this task?

A. Time series analysis to track review volume over time.
B. Clustering analysis to group customers based on demographics.
C. Topic modeling (e.g., LDA, NMF) to discover underlying themes in the reviews.
D. Regression analysis to predict customer satisfaction scores.
E. Sentiment analysis to determine the overall positive or negative sentiment.

Answer: C

Explanation:
Topic modeling is specifically designed to discover the underlying themes and topics within a collection of documents (in this case, customer reviews). LDA (Latent Dirichlet Allocation) and NMF (Non-negative Matrix Factorization) are common topic modeling techniques. Sentiment analysis provides an overall sentiment score but doesn't identify specific themes. Regression analysis is for prediction, not theme discovery. Clustering groups customers, not review topics. Time series analysis tracks changes over time, not themes.

NEW QUESTION # 130
Consider a scenario where you are training a multimodal Generative A1 model using both image and text dat a. The image data is stored in a directory with millions of high-resolution images, and the text data is in a large CSV file. What is the MOST efficient way to load and preprocess this data for training, minimizing memory usage and maximizing throughput?

A. Use a distributed file system to store the data and load it directly into the model during training.
B. Convert all images to a single large video file and load it into memory along with the text data.
C. Resize all images to a very small size before loading them into memory.
D. Use a data generator (e.g., TensorFlow's 'tf.data.Dataset' or PyTorch's *DataLoadeN) to load and preprocess data in batches.
E. Load all images and text data into memory at once, preprocess them, and then feed them to the model.

Answer: D

Explanation:
Data generators are the most efficient way to handle large datasets because they load and preprocess data in batches, minimizing memory usage. Option A is infeasible for large datasets. Option C is not a standard or efficient approach. Option D is relevant for distributed training but doesn't address the memory issue of loading the entire dataset. Option E reduces memory usage but may sacrifice important image details.

NEW QUESTION # 131
You are building a multimodal model that takes video and audio as input. You want to fuse the information extracted from both modalities. Which of the following fusion techniques allows for learning temporal dependencies between modalities?

A. Late Fusion (averaging the probabilities from separate networks).
B. Maximum pooling across feature vectors from video and audio streams.
C. Attention-based Fusion using Transformers, allowing the model to weigh the importance of different parts of each modality over time.
D. Early Fusion (concatenating features before feeding into a single network).
E. Simple Addition of feature vectors from video and audio streams.

Answer: C

Explanation:
Attention-based Fusion, particularly using Transformers, is well-suited for capturing temporal dependencies in multimodal data. Transformers can learn which parts of each modality are most relevant at different points in time, enabling a more nuanced fusion of information. Early Fusion (A) fuses features statically and doesn't capture temporal dependencies directly. Late Fusion (B) also struggles to capture fine- grained temporal relationships. Simple addition (D) and max pooling (E) are too simplistic to model complex temporal interactions.

NEW QUESTION # 132
You are tasked with deploying a generative A1 model for image inpainting using Triton Inference Server. The model requires significant GPU memory and you want to maximize throughput. Which Triton configuration parameters would be MOST important to tune, and why?

A. 'optimization' (setting strategy to TRT to enable TensorRT optimization) and 'input_shape' (specifying the exact input shape).
B. 'dynamic_batching' (enabling it and setting and 'model_warmup' (specifying dummy inputs to pre-load the model).
C. 'instance_group' (setting count to the number of available GPUs and kind to KIND_GPU) and (increasing it to the largest value that fits in GPU memory).
D. Both B and C.
E. 'instance_group' (setting count to the number of available GPUs) and (setting a high value to accumulate requests).

Answer: D

Explanation:
'instance_group' with 'KIND_GPIY assigns the model to specific GPUs. Increasing (B) leverages GPU parallelism. Enabling 'dynamic_batching' and setting (C) allows Triton to dynamically batch requests to maximize throughput. Model warmup reduces first request latency. (A) is incomplete (missing KIND_GPU). (D) is relevant for latency optimization but not as crucial for throughput in a memory-constrained scenario. Therefore both B and C are most crucial in optimizing throughput while dealing with memory constraint.

NEW QUESTION # 133
You are building a multimodal model to translate spoken language into sign language animations. You have audio recordings of spoken words and corresponding sign language video sequences. Which architecture would be most suitable for this task?

A. A simple feedforward neural network.
B. A convolutional neural network (CNN) for audio and another CNN for video, followed by a fully connected layer.
C. A support vector machine (SVM) to classify audio and video features.
D. A generative adversarial network (GAN) to generate realistic sign language animations.
E. A recurrent neural network (RNN) or Transformer network with attention mechanisms to capture the temporal dependencies in both the audio and video sequences, and a connectionist temporal classification (CTC) loss function for alignment.

Answer: E

Explanation:
RNNs or Transformer networks with attention mechanisms are best suited for sequence-to-sequence tasks like translation, as they can capture temporal dependencies. CTC loss helps align the audio and video sequences, even if they are not perfectly synchronized. CNNs are good for feature extraction but don't handle sequential data as well as RNNs/Transformers.

NEW QUESTION # 134
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