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NVIDIA Generative AI Multimodal Sample Questions (Q249-Q254):

NEW QUESTION # 249
You are tasked with deploying a generative A1 model for image inpainting using Triton Inference Server. The model takes an image with masked regions as input and outputs the completed image. You need to pre-process the input image before sending it to the server.
Which pre-processing steps are crucial for ensuring optimal performance and accuracy of the inpainting model?

A. Creating a binary mask indicating the regions to be inpainted.
B. Applying data augmentation techniques like random rotations and flips.
C. Resizing the image to a fixed resolution and normalizing pixel values to a specific range (e.g., [0, 1] or [-1, 1]).
D. Converting the image to grayscale.
E. Sharpening the image to enhance details.

Answer: A,C

Explanation:
Resizing and normalization ensure the input image conforms to the model's expected input size and data range. A binary mask is essential to indicate which regions the model should inpaint. Converting to grayscale would remove color information, hindering the inpainting process. Data augmentation should be done during the training phase. Sharpening is not typically a necessary pre-processing step for inpainting.

NEW QUESTION # 250
A financial institution is developing a multimodal A1 system to detect fraudulent transactions by analyzing transaction details (text), user images, and audio recordings of phone calls. Which of the following strategies is MOST crucial for handling the missing data that frequently occurs across these modalities?

A. Using a modality dropout technique during training, randomly masking modalities to force the model to learn robust representations from incomplete data.
B. Replacing missing data with a single, arbitrary placeholder value (e.g., -1 for numerical data, 'missing' for text) across all modalities.
C. Imputing missing data in each modality independently using modality-specific imputation techniques (e.g., mean imputation for numerical data, most frequent category for categorical data).
D. Ignoring transactions with missing data to simplify the model's training process.
E. Employing a joint imputation approach that leverages information from available modalities to predict and fill in missing values in other modalities.

Answer: A,E

Explanation:
Ignoring missing data or using simple imputation techniques can introduce bias and reduce the model's accuracy. A joint imputation approach is superior because it leverages the relationships between modalities to improve imputation accuracy. Modality dropout during training further enhances robustness to missing data in real-world scenarios.

NEW QUESTION # 251
You are working on a project that involves analyzing customer reviews which contains the following dataset: 1. customer_id(categorical) 2. customer_review(text) 3. product_image(image) 4. video_of_product_usage(video) What is the best way to handle and address the problem of skewness across each modailities?

A. Balance the dataset by oversampling under-represented data points within each modality independently.
B. Treat all modalitites with equal weights during model training, ignoring potential skewness issues.
C. Apply modality-specific weighting schemes that assign higher weights to modalities with less representation.
D. Do nothing about the skewness, as the model will learn to adapt to the imbalanced data distribution.
E. Design a loss function that explicitly penalizes the model for being biased towards dominant modalities.

Answer: A,C,E

Explanation:
Addressing skewness is crucial for preventing the model from being biased towards dominant modalities. Oversampling, modality- specific weighting, and a biased-aware loss function are all effective strategies for mitigating this problem.

NEW QUESTION # 252
You're building a system to translate customer service chat logs into summaries that a human agent can quickly review The chat logs are often informal, contain slang, and have grammatical errors. Which prompt engineering technique is MOST likely to improve the quality and accuracy of the summaries generated by a large language model (LLM)?

A. Using chain-of-thought prompting to encourage the LLM to explain its reasoning process before generating the summary.
B. Using a few-shot prompt with several examples of chat logs and their ideal summaries, explicitly demonstrating how to handle informality and errors.
C. Using a template prompt with predefined sections and keywords to guide the summarization process and ensure consistency across different chat logs.
D. Using a zero-shot prompt with a simple instruction like 'Summarize this chat log.'
E. Using a negative constraint prompt, explicitly stating what the LLM should not include in the summary (e.g., 'Do not include greetings or farewells.').

Answer: A,B,C,E

Explanation:
Few-shot prompting provides the LLM with examples to learn from, allowing it to better handle the nuances of informal language and errors. Chain-of-thought helps the model reason step-by-step, leading to better summaries. Negative constraints prevent irrelevant information. Template prompts provide structure and consistency. A zero-shot prompt is less effective in this scenario due to the complexity of the input data.

NEW QUESTION # 253
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. 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.
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. B and D
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. 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.

Answer: C

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 # 254
......

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