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NVIDIA Generative AI Multimodal Sample Questions (Q166-Q171):

NEW QUESTION # 166
You are developing a multimodal model that combines text and tabular data for predicting customer churn. The text data consists of customer reviews, and the tabular data includes demographics and transaction history. You've preprocessed both datasets. Which of the following approaches would be the MOST effective for integrating these modalities?

A. Concatenate the raw text and tabular data into a single feature vector.
B. Use a Transformer-based model to encode the text and a separate neural network for the tabular data, then fuse the embeddings.
C. Train separate models for text and tabular data, then average their predictions.
D. All of the above.
E. Convert the text data into numerical features using techniques like TF-IDF, then concatenate these features with the tabular data.

Answer: B,E

Explanation:
Options C and D provides the most effective integration. Using a Transformer-based model for text allows it to capture complex relationships and dependencies in the text. A separate neural network handles tabular data effectively. Fusing the embeddings provides a unified representation. Option D is also valid because it allowst he model to incorporate the text and tabular data together as a single feature vector. Raw concatenation (A) is unlikely to work well. Averaging predictions (B) might not capture interactions between modalities.

NEW QUESTION # 167
You are building a multimodal model for medical diagnosis that combines patient medical history (text), medical images (X-rays, MRIs), and sensor data (heart rate, blood pressure). The dataset contains significant amounts of missing data across all modalities. What strategy is most appropriate for handling the missing data and ensuring the model's robustness and accuracy?

A. Imputing missing values using simple methods like mean imputation or filling with a constant value.
B. Removing all patients with missing data to create a clean dataset.
C. Using a multimodal variational autoencoder (MVAE) to learn a joint latent representation of the data and impute missing values based on the observed modalities.
D. Using a Generative Adversarial Network(GAN) to impute missing values based on the other avalible modalities.
E. Training seperate models for each avalible modality.

Answer: C,D

Explanation:
Removing patients with missing data can lead to a significant loss of information and bias the model. Simple imputation methods can introduce inaccuracies and fail to capture the relationships between modalities. Multimodal variational autoencoders (MVAEs) are specifically designed to handle missing data in multimodal datasets by learning a joint latent representation and imputing values based on the observed modalities. This approach is more robust and accurate than simple imputation methods. GAN can also be used to impute missing values.

NEW QUESTION # 168
You are tasked with deploying a generative A1 model trained with NeMo using Triton Inference Server. You want to leverage TensorRT for optimized inference. Which of the following steps is crucial to ensure compatibility and optimal performance?

A. Convert the NeMo model to a TorchScript representation for TensorRT optimization.
B. Directly deploy the NeMo model as a Python backend within Triton without any conversion.
C. Bake the Triton server into a Docker container that includes all NeMo dependencies.
D. Ensure that the Triton server is running on a CPU-only instance for maximum compatibility.
E. Export the NeMo model to ONNX format before deploying it to Triton+.

Answer: E

Explanation:
Exporting the NeMo model to ONNX (Open Neural Network Exchange) is essential for compatibility with Triton Inference Server and TensorRT optimization. ONNX provides a standard format that TensorRT can ingest and optimize for efficient inference on NVIDIA GPUs.

NEW QUESTION # 169
You are working on a Generative A1 Multimodal model that takes text and audio as input and generates a video. During training, you observe that the generated videos often lack coherence with the input text. What are the potential issues you would investigate? (Select THREE)

A. Lack of a strong conditioning mechanism to guide the video generation based on the input text and audio.
B. The training dataset does not contain enough diverse examples of text, audio, and video combinations.
C. The discriminator network is too powerful, leading to mode collapse.
D. Insufficient regularization in the generator network.
E. The input audio is too loud.

Answer: A,B,D

Explanation:
Insufficient regularization can cause overfitting and lack of generalization, leading to incoherence. A weak conditioning mechanism means the model isn't effectively using the input text to guide the video generation. A lack of diverse training examples limits the model's ability to learn the relationships between text, audio, and video. A too-powerful discriminator can lead to mode collapse, but primarily affects diversity, not necessarily coherence directly. Input audio loudness is a preprocessing issue, not a fundamental architectural problem.

NEW QUESTION # 170
Consider the following Python code snippet utilizing the Hugging Face Transformers library for multimodal processing. The objective is to perform visual question answering (VQA). Assume 'image' is a PIL Image object and 'question' is a string. However, the code is incomplete. Choose the options to complete the code.

Answer: B

Explanation:
The correct code uses ' AutoModelForSeq2SeqLM' because BLIP (used in the example) is a sequence-to-sequence model. The processor correctly handles the image and text, and 'model.generate' produces the answer which is then decoded. 'AutoModelForQuestionAnswering' is not a generic class and won't work correctly with BLIP without additional adaptation.

NEW QUESTION # 171
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NCA-GENM Valid Exam Duration & NCA-GENM Testking Exam Questions

NCA-GENM Test Answers - NVIDIA Generative AI Multimodal Test Torrent & NCA-GENM Guide Torrent

NVIDIA Generative AI Multimodal Sample Questions (Q166-Q171):

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