NCA-GENL Core Machine Learning and AI Knowledge Practice Question
Exhibit
{
"policy_name": "model_access_control",
"enable_gradient_check": true,
"optimizer_type": "adam",
"learning_rate_scheduler": "cosine",
"weight_decay": 0.05,
"dropout_rate": 0.1
}Refer to the exhibit. Which hyperparameter configuration in the provided JSON is directly responsible for preventing overfitting through weight penalty?
⚠ Common exam trap
Candidates frequently select learning rate or dropout parameters, confusing general training dynamics or activation controls with the specific weight penalty mechanism of L2 regularization.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
weight_decay
The JSON defines a standard configuration for model training. The 'weight_decay' key is the parameter that implements L2 regularization. By penalizing large weight values in the loss function, it discourages the model from relying too heavily on specific features, thereby preventing overfitting. This is a common and essential hyperparameter to tune when training deep neural networks to ensure good generalization on unseen test data, as shown by the provided value of 0.05.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
dropout_rate
Why it's wrong here
While dropout is a regularization technique, it works by randomly setting activations to zero during training, not by penalizing weights. It forces the network to learn redundant representations, but it is technically distinct from weight decay, which is the specific mechanism for penalizing weight magnitude as requested in the question.
- ✓
weight_decay
Why this is correct
Weight decay is the standard term for L2 regularization in neural networks. It adds a penalty term to the loss function based on the square of the weights, effectively constraining the weights and preventing them from becoming unnecessarily large, which is the primary mechanism for controlling overfitting through penalty methods.
- ✗
learning_rate_scheduler
Why it's wrong here
The learning rate scheduler controls how the learning rate changes over the course of training. While it is crucial for convergence, it is not a penalty mechanism for weights. It helps the optimizer navigate the loss landscape, but it doesn't directly constrain the magnitude of individual weights during updates.
- ✗
enable_gradient_check
Why it's wrong here
Gradient checking is a verification technique to ensure that the backpropagation implementation is correct by comparing numerical gradients to calculated ones. It is a debugging tool and has no impact on regularization or the model's susceptibility to overfitting during the standard training cycle described in the policy file.
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Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
This NCA-GENL practice question is part of Courseiva's free NVIDIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the NCA-GENL exam.