試験NCA-AIIO トピック2 問題27 スレッド

NVIDIA NCA-AIIOのリアル試験問題集
問題 #: 27
トピック #: 2

You are working with a team of data scientists on an AI project where multiple machine learning models are being trained to predict customer churn. The models are evaluated based on the Mean Squared Error (MSE) as the loss function. However, one model consistently shows a higher MSE despite having a more complex architecture compared to simpler models. What is the most likely reason for the higher MSE in the more complex model?

A. Low learning rate in model training

B. Overfitting to the training data

C. Incorrect calculation of the loss function

D. Underfitting due to insufficient model complexity

おすすめの解答：B 解答を投票する

A complex model with higher MSE than simpler ones likely suffers from overfitting, where it learns training data noise rather than general patterns, reducing test performance. NVIDIA's training workflows (e.g., DGX, RAPIDS) emphasize regularization (e.g., dropout) to mitigate this, common in deep learning.
A low learning rate (Option A) slows convergence but doesn't inherently raise MSE. Incorrect loss calculation (Option C) would affect all models. Underfitting (Option D) contradicts the model's complexity.
Overfitting is NVIDIA-aligned for such scenarios.

Hayashi 2026-01-06 08:39:06

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試験NCA-AIIO トピック2 問題27 スレッド

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