최신 NCA-GENM 무료덤프 - NVIDIA Generative AI Multimodal

문제1

A multimodal A1 model is trained on a dataset containing biased text and images. This bias leads to the model generating outputs that reinforce negative stereotypes. Which of the following steps are crucial for addressing and mitigating this bias during the model development lifecycle? (Select TWO)

A. Using adversarial training techniques to encourage fairness.

B. Implementing model distillation to reduce the model size

C. Collecting a more diverse and representative dataset.

D. Increasing the learning rate during training.

E. Reducing the number of layers in the neural network.

정답: A,C

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문제2

You are tasked with optimizing a multimodal model that combines audio and text data for speech recognition. The model currently struggles with noisy audio environments. Which data augmentation technique would be MOST effective in improving the model's robustness to noise?

A. Adding Gaussian noise to the audio data.

B. Rotating the images used for visual context.

C. Normalizing the text data to lowercase.

D. Randomly masking parts of the text input.

E. Translating the text into different languages and back.

정답: A

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문제3

You are deploying a multimodal Generative A1 model on a cloud platform. The model takes video and text as input to generate video descriptions. The model's performance needs to be monitored to ensure it meets certain performance SLAs. Which of the following metrics are MOST crucial to monitor in a production environment to ensure both computational efficiency and output quality? (Select TWO)

A. Inference latency (time per request).

B. BLEU score (or similar text generation metric) for generated descriptions.

C. GPU utilization.

D. Model size on disk.

E. Number of lines of code in the model.

정답: A,B

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문제4

Consider a scenario where you are developing a multimodal model for medical diagnosis using patient medical history (text), X-ray images, and ECG data (time-series). A significant portion of the ECG data is missing due to sensor malfunction. Which of the following approaches would be MOST effective in handling the missing data and ensuring accurate diagnosis?

A. Replace the missing ECG data with the average values from the entire dataset.

B. Employ a multimodal fusion technique that is robust to missing modalities, such as attention mechanisms that dynamically weight the available data sources.

C. Train a separate model using only the available medical history and X-ray images, ignoring the ECG data altogether.

D. Impute the missing ECG values using time-series imputation techniques (e.g., Kalman filtering or interpolation).

E. Combine imputation of missing ECG data with a robust multimodal fusion technique.

정답: E

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문제5

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.

A.

B.

C.

D.

E.

정답: E

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문제6

You're developing a multimodal A1 system that takes image data, text descriptions, and user interaction data (clicks, dwell time) to generate personalized product recommendations. To effectively combine these modalities and capture complex relationships, which model architecture would be most suitable?

A. A deep learning architecture incorporating attention mechanisms and cross-modal fusion layers, with separate embedding layers for each modality, followed by a shared representation layer for joint learning and prediction.

B. A Naive Bayes classifier.

C. A k-nearest neighbors (KNN) algorithm.

D. A simple linear regression model.

E. A decision tree-based model.

정답: A

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문제7

You are tasked with building a system that generates realistic images based on both textual descriptions and a semantic segmentation map. The segmentation map provides spatial information about the objects present in the scene. Which of the following generative architectures is MOST appropriate for this multimodal task?

A. Autoregressive model like PixelCNN

B. Conditional Generative Adversarial Network (cGAN) with both text and segmentation map as conditions.

C. Vanilla Generative Adversarial Network (GAN)

D. Variational Autoencoder (VAE)

E. Diffusion model without conditioning

정답: B

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문제8

You are building a system that uses a Generative A1 model that combines images and natural language prompts to create photorealistic images. The training process is computationally intensive. Which NVIDIA technology is best suited to accelerate the training of this Generative A1 model, especially if it is distributed across multiple GPUs?

A. NVIDIA optiX

B. NVIDIA TensorRT

C. NVIDIA DALI

D. NVIDIA NCCL

E. NVIDIA NeMo

정답: D

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문제9

You are building a multimodal generative A1 application that uses CLIP to align text and image embeddings. You observe that the generated images lack detail and fidelity to the text prompt. Which of the following strategies would be MOST effective in improving image quality, and how could prompt engineering and Triton Inference Server play a role?

A. Increasing the CLIP model's text encoder's hidden layer size and using more aggressive data augmentation during CLIP training. Triton can be used to serve the augmented CLIP model at scale.

B. All of the above

C. Using a larger batch size during CLIP training and increasing the learning rate. Triton is not directly involved in model training.

D. Training a separate image super-resolution model to enhance the generated images after they are produced by the CLIP-guided generator. Triton can manage the concurrent execution of the generator and super-resolution models.

E. Refining the text prompts to be more descriptive and specific, incorporating stylistic details and relevant keywords. Triton can optimize the prompt embedding process.

정답: D,E

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문제10

Consider a scenario where you are evaluating the performance of a multimodal A1 model that generates descriptions for images. However, the generated descriptions tend to be repetitive and lack diversity. Which of the following techniques can be employed to address this issue and encourage more diverse and creative outputs from the model? (Select TWO)

A. Utilizing a temperature scaling parameter during decoding and increasing its value.

B. Using a greedy decoding strategy.

C. Employing a nucleus sampling (top-p sampling) decoding strategy.

D. Using a beam search decoding strategy with a small beam width.

E. Increasing the model's training data size.

정답: A,C

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문제11

You are experimenting with a text-to-image generative model. You notice that when prompted with descriptions containing specific demographic information (e.g., 'a black doctor'), the generated images consistently reflect stereotypes. What steps can you take during the experiment evaluation phase to identify and mitigate this bias? (Select TWO)

A. Conduct a human evaluation study where participants assess the generated images for stereotypical representations.

B. Randomly shuffle the training dataset to minimize bias.

C. Filter out all examples containing demographic information from the training dataset.

D. Increase the size of the training dataset to dilute the effect of any biased examples.

E. Use a bias detection metric to quantify the presence of bias in the generated images, comparing output distributions across different demographic groups.

정답: A,E

설명: (DumpTOP 회원만 볼 수 있음)

문제12

You are tasked with evaluating the trustworthiness of a multimodal A1 model that predicts diagnoses based on medical images and patient history text. Which of the following evaluation metrics or techniques are MOST relevant for assessing the model's trustworthiness in this critical application?

A. Robustness testing by introducing adversarial perturbations to the input data.

B. Measuring inference throughput (samples per second).

C. Accuracy and F1-score on a held-out test set.

D. Calibration error, measuring the alignment between predicted probabilities and actual outcomes.

E. Attribution methods (e.g., Grad-CAM) to visualize which parts of the image and text the model focuses on.

정답: A,D,E

설명: (DumpTOP 회원만 볼 수 있음)

문제13

You are evaluating a Generative A1 model for image captioning. Which of the following metrics is MOST appropriate for assessing the semantic similarity between the generated captions and the ground truth captions?

A. BLEU score

B. ROUGE score

C. CIDEr score

D. Inception Score

E. Perplexity

정답: C

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문제14

Consider the following Python code snippet that utilizes a pre-trained language model from the Hugging Face Transformers library:

Which of the following statements are TRUE regarding the generated output?

A. The output will contain a single sequence of text generated by the GPT-2 model, starting with the provided prompt.

B. The output will always be exactly 50 tokens long.

C. The GPT-2 model is guaranteed to generate grammatically correct and factually accurate text.

D. The parameter controls the number of different completion the model should return.

E. The output will always start with "The quick brown fox jumps over the lazy".

정답: A,D,E

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문제15

Consider the following scenario: You're training a GAN for generating high-resolution images (e.g., 1024x1024). You notice that the training process is unstable, with the generator and discriminator constantly oscillating. Which of the following architectural modifications and training techniques could help stabilize the training process?

A. Using Wasserstein GAN (WGAN) with gradient penalty (GP).

B. Increasing the learning rate of both the generator and discriminator.

C. Using ReLU activation functions in the discriminator.

D. Replacing standard convolutional layers with transposed convolutional layers in the generator.

E. Applying batch normalization in both the generator and discriminator.

정답: A,E

설명: (DumpTOP 회원만 볼 수 있음)

문제16

Which of the following techniques can be used to reduce the computational cost and memory footprint of large language models (LLMs) during inference?

A. Knowledge Distillation

B. Increasing the model size

C. Quantization

D. Adding more layers

E. Pruning

정답: A,C,E

설명: (DumpTOP 회원만 볼 수 있음)

문제17

You are tasked with building a multimodal generative A1 model that takes both image and text as input to generate a coherent video. Which of the following architectures is MOST suitable for this task, considering the need to fuse information from different modalities and generate sequential data?

A. A standard Convolutional Neural Network (CNN) followed by a fully connected layer.

B. A Support Vector Machine (SVM) classifier trained to predict the next frame based on image and text features.

C. A Generative Adversarial Network (GAN) trained solely on image data and later fine-tuned with text embeddings.

D. A simple recurrent neural network (RNN) that concatenates image feature vectors and text embeddings as input at each time step.

E. A Transformer-based architecture with separate encoders for image and text, followed by a decoder that generates video frames.

정답: E

설명: (DumpTOP 회원만 볼 수 있음)

문제18

Which prompt engineering technique is most likely to improve the coherence and visual quality of images generated by a text-to-image model when generating complex scenes with multiple objects and intricate details?

A. Relying solely on the model's default style settings.

B. Using short, concise prompts with only a few keywords.

C. Using only abstract and ambiguous language.

D. Employing a negative prompt to specify elements to avoid.

E. Exclusively describing the background and neglecting foreground elements.

정답: D

설명: (DumpTOP 회원만 볼 수 있음)

문제19

You are evaluating a generative A1 model for image captioning. The model produces captions that are grammatically correct but often miss key objects in the image. Which of the following evaluation metrics would be MOST suitable to identify this deficiency?

A. BLEU (Bilingual Evaluation Understudy)

B. ROUGE (Recall-Oriented Understudy for Gisting Evaluation)

C. CIDEr (Consensus-based Image Description Evaluation)

D. Inception Score

E. Perplexity

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

최신 NCA-GENM 무료덤프 - NVIDIA Generative AI Multimodal

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