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| Section | Objectives |
|---|---|
| NVIDIA AI Ecosystem | - NVIDIA tools and frameworks
|
| Multimodal AI Systems | - Multimodal model design - Cross-modal learning
|
| Core AI and Machine Learning Fundamentals | - Machine learning basics
|
| Generative AI Concepts | - Generative models
|
| Responsible and Trustworthy AI | - Bias and safety considerations - Ethical AI principles |
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NEW QUESTION # 13
You are working on a project to generate realistic images from text descriptions. You've trained a diffusion model, but the generated images often lack fine-grained details and exhibit artifacts. Which of the following techniques would be MOST effective in improving the image quality and fidelity?
Answer: A
Explanation:
Classifier-free guidance allows you to control the influence of the text description on the generated image. By adjusting the guidance scale, you can find a balance between generating images that are faithful to the text description and generating diverse and high-quality images. Increasing diffusion steps, batch size, or reducing the learning rate may help but is less targeted towards improving fidelity specifically.
NEW QUESTION # 14
You're building a multimodal model to predict stock prices using news articles (text) and historical price data (time series). You observe that the model performs well on the training data but poorly on unseen dat a. Which of the following techniques would be MOST effective in addressing this issue?
Answer: C
Explanation:
All the listed techniques combat overfitting. Increasing data size provides more examples. Regularization penalizes complex models. Early stopping prevents training too long on the training data. Data augmentation creates more robust and generalizable model by artifically expanding data through techniques like Tme series perturbation and Text paraphrasing.
NEW QUESTION # 15
In large-language models, what is the purpose of the attention mechanism?
Answer: A
Explanation:
The attention mechanism computes a set of weights over the tokens in the input (or context) sequence for each step of processing, reflecting how relevant each input token is to the computation currently being performed
- for instance, how relevant each word in a source sentence is to correctly translating a given target word, or how relevant each prior token is to predicting the next one in an autoregressive model. Mechanically, this is computed via query, key, and value projections: a query (representing the current focus) is compared against keys (representing each input token) to produce attention scores, which are normalized (typically via softmax) into weights and used to compute a weighted sum over the corresponding values - allowing the model to dynamically focus more on relevant tokens and less on irrelevant ones, rather than treating all input tokens with equal importance.
Option D describes positional encoding's role (covered directly in an earlier question in this set) - capturing token order - which is a distinct mechanism from attention; attention operates on token *content and relevance*, while positional encoding separately supplies *order* information as an input feature, since self- attention itself is permutation-invariant without it. Option A misdirects the weighting toward the output sequence specifically, when attention weights are computed primarily over the input/context tokens being attended to. Option C describes the decoding/generation procedure (autoregressive sampling), not attention's mechanism.
Reference: Core Machine Learning and AI Knowledge domain - transformer attention mechanism vs.
positional encoding.
NEW QUESTION # 16
You have been given a dataset with missing values. What is the first step you should take with the data?
Answer: D
Explanation:
Before deciding *how* to handle missing data, best practice requires understanding *why* it's missing - analyzing whether missingness is Missing Completely at Random (MCAR, no systematic pattern), Missing at Random (MAR, related to other observed variables but not the missing value itself), or Missing Not at Random (MNAR, related to the missing value itself, e.g., patients with severe symptoms being less likely to complete a survey field). This diagnostic step determines which downstream handling strategy is statistically appropriate: naive row deletion under MNAR conditions can introduce systematic bias into the remaining dataset, while mean/median imputation applied blindly can distort variance and correlational structure if missingness isn't actually random.
Options B, C, and D each jump directly to a specific remedial action without first establishing whether that action is appropriate for the missingness pattern present. Removing rows (B) sacrifices sample size and can bias results if missingness correlates with the outcome of interest. Filling with a default value (C) without understanding the pattern risks introducing artificial structure that doesn't reflect the true underlying data.
Removing entire columns (D) may discard genuinely informative features if missingness in that column is low or non-systematic.
Only after this initial pattern analysis should you select an appropriate strategy: listwise deletion, mean/median
/mode imputation, model-based imputation (e.g., MICE, k-NN imputation), or explicit missingness indicators as additional features.
Reference: Data Analysis and Visualization domain - missing data diagnosis (MCAR/MAR/MNAR) prior to imputation strategy selection.
NEW QUESTION # 17
You are tasked with optimizing a multimodal A1 model that processes both image and text data for generating image captions. The model exhibits slow inference times, particularly when handling high-resolution images. Which of the following optimization strategies would be MOST effective in reducing inference latency, considering the NVIDIA ecosystem?
Answer: B
Explanation:
TensorRT is specifically designed for optimizing deep learning models for inference on NVIDIA GPUs. It performs graph optimization, quantization, and kernel fusion to reduce latency and increase throughput. Increasing batch size can sometimes help, but may also lead to memory issues or increased latency for small batch sizes. A larger model will generally increase latency. Simpler loss functions and removing dropout affect training and generalization, not necessarily inference speed.
NEW QUESTION # 18
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