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| Section | Objectives |
|---|---|
| Multimodal AI Systems | - Multimodal model design - Cross-modal learning
|
| NVIDIA AI Ecosystem | - NVIDIA tools and frameworks
|
| Responsible and Trustworthy AI | - Ethical AI principles - Bias and safety considerations |
| Core AI and Machine Learning Fundamentals | - Machine learning basics
|
| Generative AI Concepts | - Generative models
|
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NEW QUESTION # 40
You are evaluating a multimodal model that generates descriptions for video clips. You have human ratings for the relevance, fluency, and coherence of the generated descriptions. Which statistical test is MOST appropriate for determining if there is a statistically significant difference in the median ratings for each of these criteria (relevance, fluency, coherence) between two different versions of your model?
Answer: A
Explanation:
Since you're interested in comparing the medians of the ratings and not assuming a normal distribution (which is often the case with subjective human ratings), a non-parametric test is more appropriate than a t-test or ANOVA. The Mann-Whitney U test (also known as the Wilcoxon rank-sum test) is used to compare the medians of two independent groups. The Kruskal-Wallis test is used when you have more than two groups.
NEW QUESTION # 41
Consider the following PyTorch code snippet used for training a Generative A1 model:
Answer: A,B
Explanation:
The code has two critical issues. First, 'optimizer.step()' is called only once per epoch after accumulating gradients from all batches. This is incorrect, as parameters aren't updated batch-wise. Second, is also called only once per epoch, meaning gradients from all batches accumulate. This will likely lead to a CUDAOOM error, especially for larger models.
NEW QUESTION # 42
You're developing a real-time multimodal A1 system that processes live video and audio streams. The system's performance is lagging behind requirements. Which of the following optimization strategies would be MOST effective in improving the system's throughput and reducing latency?
Answer: B,D,E
Explanation:
TensorRT optimizes models for NVIDIA GPUs, significantly improving inference speed and reducing latency. Mixed-precision inference reduces memory bandwidth requirements and allows for faster computations on Tensor Cores. Reducing the number of layers decreases the computational complexity of the model. RNNs are not typically faster than Transformers, especially when optimized with libraries like TensorRT. While a larger batch size can increase throughput, it also increases latency, which is detrimental for a real-time system.
NEW QUESTION # 43
You are training a multimodal model that combines text and images. You observe that the model is heavily biased towards the text modality and largely ignores the image data. Which of the following strategies could you use to address this modality imbalance? (Select all that apply)
Answer: B,C,D,E
Explanation:
Options A, B, C, and D are effective strategies for addressing modality imbalance. Increasing the learning rate for the underutilized modality (image) or decreasing it for the dominant one (text) encourages the model to pay more attention to the image data. Modality-specific loss weighting directly emphasizes the importance of the image modality during training. Oversampling image data ensures that the model sees more image examples and is less likely to be biased towards the text modality. Option E might lead to loss of important information from the image modality.
NEW QUESTION # 44
During the process of data cleansing, which of the following steps is NOT typically performed?
Answer: A
Explanation:
Data cleansing (or data cleaning) operates on data you already have: it identifies and resolves quality issues within an existing dataset - handling missing values (A), removing duplicate records (D), correcting formatting or type inconsistencies (B), fixing structural errors, and standardizing units or encodings.
Collecting additional data (C) belongs to a conceptually earlier and separate phase of the pipeline: data acquisition or data collection, which determines what data enters the pipeline in the first place, rather than what is done to improve the quality of data already collected.
This distinction matters operationally: a cleansing step is typically deterministic and reversible against the existing dataset (you can inspect, log, and audit exactly which rows were dropped or imputed), whereas collecting more data is a scoping decision that may require new labeling budgets, new consent/privacy review, or new data-source integration - a materially different workflow with different stakeholders.
That said, insufficient data volume discovered *during* cleansing (e.g., after removing corrupted records the sample size becomes too small for the target class) can trigger a decision to go back and collect more - but that action itself is not classified as a cleansing step; it is the trigger for restarting an earlier pipeline stage.
Reference: Data Analysis and Visualization domain - data cleansing vs. data acquisition pipeline stages.
NEW QUESTION # 45
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