- A
Gradient descent
Why wrong: Gradient descent is an optimization algorithm, not a regularization technique.
- B
Backpropagation
Why wrong: Backpropagation is used to compute gradients, not to prevent overfitting.
- C
Boosting
Why wrong: Boosting is an ensemble method that can increase overfitting risk.
- D
Dropout
Dropout is a regularization technique that randomly deactivates neurons to reduce overfitting.
Question 253 of 1,000
AIF-C01 AI and ML Fundamentals Practice Question
This AIF-C01 practice question tests your understanding of ai and ml fundamentals. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A machine learning engineer is training a neural network and wants to prevent overfitting. Which technique should they apply?
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
✓
Dropout
Dropout is a regularization technique that randomly drops a fraction of neurons during training, which prevents the network from becoming overly reliant on any single neuron and reduces co-adaptation. This forces the model to learn more robust features, effectively reducing overfitting by acting as an ensemble of sub-networks.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Gradient descent
Why it's wrong here
Gradient descent is an optimization algorithm, not a regularization technique.
- ✗
Backpropagation
Why it's wrong here
Backpropagation is used to compute gradients, not to prevent overfitting.
- ✗
Boosting
Why it's wrong here
Boosting is an ensemble method that can increase overfitting risk.
- ✓
Dropout
Why this is correct
Dropout is a regularization technique that randomly deactivates neurons to reduce overfitting.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between optimization techniques (gradient descent, backpropagation) and regularization techniques (dropout), trapping candidates who confuse training algorithms with overfitting prevention methods.
Detailed technical explanation
How to think about this question
During training, dropout randomly sets a fraction (e.g., 0.5 for hidden layers) of neuron activations to zero at each forward pass, effectively sampling a different thinned network per mini-batch. At test time, all neurons are used but their outputs are scaled by the dropout rate (inverted dropout) to maintain expected activation magnitude. This technique is particularly effective in large fully connected layers where co-adaptation is most problematic, such as in deep convolutional networks for image classification.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
AI and ML Fundamentals — This question tests AI and ML Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Dropout — Dropout is a regularization technique that randomly drops a fraction of neurons during training, which prevents the network from becoming overly reliant on any single neuron and reduces co-adaptation. This forces the model to learn more robust features, effectively reducing overfitting by acting as an ensemble of sub-networks.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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