This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. 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 team is training a deep learning model using Horovod distributed training on SageMaker. They observe that the loss stops decreasing after a few epochs. Which technique should they implement to reduce overfitting?
Increase learning rate
Why wrong: Increasing learning rate can cause the loss to diverge.
Add more layers to the model
Why wrong: More layers increase capacity and may exacerbate overfitting.
Reduce the number of epochs
Why wrong: Reducing epochs may underfit but does not directly address overfitting.
Use dropout regularization
Dropout is a regularization technique that reduces overfitting.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use dropout regularization
Dropout regularization randomly drops a fraction of neurons during training, which prevents the model from relying too heavily on specific features and forces it to learn more robust representations. This directly addresses overfitting, which is the likely cause of the loss plateauing after a few epochs in a Horovod distributed training setup on SageMaker.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Increase learning rate
Why it's wrong here
Increasing learning rate can cause the loss to diverge.
Add more layers to the model
Why it's wrong here
More layers increase capacity and may exacerbate overfitting.
Reduce the number of epochs
Why it's wrong here
Reducing epochs may underfit but does not directly address overfitting.
Use dropout regularization
Why this is correct
Dropout is a regularization technique that reduces overfitting.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Cisco often tests the misconception that early stopping (reducing epochs) is a regularization technique to reduce overfitting, but the trap here is that early stopping only halts training and does not actively prevent the model from memorizing noise during the epochs it does train.
Detailed technical explanation
Dropout works by sampling a sub-network from the full network at each training step, effectively performing model averaging over an ensemble of thinned networks. In distributed training with Horovod, dropout is applied independently on each worker, which still yields consistent gradient updates because the expected output of the dropout layer is scaled to maintain the same total activation. A common real-world scenario is training on SageMaker with large image datasets, where dropout rates of 0.2 to 0.5 are typical for fully connected layers to combat overfitting.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Fundamentals of AI and ML — This question tests Fundamentals of AI and ML — Read the scenario before looking for a memorised answer..
The correct answer is: Use dropout regularization — Dropout regularization randomly drops a fraction of neurons during training, which prevents the model from relying too heavily on specific features and forces it to learn more robust representations. This directly addresses overfitting, which is the likely cause of the loss plateauing after a few epochs in a Horovod distributed training setup on SageMaker.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 25, 2026
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services 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 AIF-C01 exam.
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