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AI0-001 AI Concepts and Techniques Practice Question

A team is deploying a sentiment analysis model that must achieve high precision and high recall. They have a labeled dataset of 10,000 samples. They want to minimize overfitting. Which THREE actions are most appropriate? (Select THREE.)

⚠ Common exam trap

CompTIA often tests the misconception that decreasing the learning rate is a regularization technique, when in fact it only affects optimization speed and not model complexity or overfitting prevention.

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

Apply L2 regularization to the model weights

L2 regularization (option B) penalizes large weights by adding a squared magnitude term to the loss function, which discourages the model from fitting noise in the training data. This directly reduces overfitting while maintaining high precision and recall by keeping the decision boundary smooth and generalizable.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Decrease the learning rate

    Why it's wrong here

    Learning rate affects training speed, not directly overfitting.

  • Apply L2 regularization to the model weights

    Why this is correct

    Penalizes large weights, reducing overfitting.

  • Use dropout layers in the neural network

    Why this is correct

    Randomly drops neurons during training, acting as regularization.

  • Increase the training batch size

    Why it's wrong here

    Larger batch sizes can lead to sharper minima and may not reduce overfitting.

  • Augment the training data with synthetic examples

    Why this is correct

    Increases effective dataset size, reducing overfitting.

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This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.