Quick Answer
The correct first step is to apply regularization. This is because the dramatic gap between 99% training accuracy and 70% test accuracy is the classic signature of overfitting, where the model has memorized noise and specific patterns in the training data rather than learning generalizable features. Regularization techniques, such as L1 or L2 penalties added to the loss function, directly combat this by constraining model complexity, forcing it to find simpler, more robust decision boundaries. On the AWS Certified AI Practitioner AIF-C01 exam, this scenario tests your ability to diagnose overfitting from performance metrics and recall the primary remedy; a common trap is to immediately choose more data or feature engineering, but regularization is the first-line defense. In SageMaker, you can implement this via hyperparameters like `l1` or `l2` in built-in algorithms or by adding dropout layers in custom frameworks. Memory tip: think of the "99-70 gap" as your cue to "regularize first" — it’s the simplest fix before adding data or tuning architecture.
This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 binary classification model using Amazon SageMaker. They notice that the training accuracy is 99% but the test accuracy is only 70%. Which technique should they apply first to address this?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Reduce training data
Why wrong: Reducing data would likely increase overfitting.
Apply regularization
Regularization adds penalty for large weights, helping to reduce overfitting.
Increase learning rate
Why wrong: A higher learning rate may cause unstable training but does not directly address overfitting.
Increase model complexity
Why wrong: Increasing complexity would likely worsen 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
Apply regularization
The high training accuracy (99%) paired with significantly lower test accuracy (70%) is a classic symptom of overfitting, where the model memorizes the training data instead of learning generalizable patterns. Regularization (Option B) is the first-line technique to combat overfitting by adding a penalty to the loss function (e.g., L1 or L2 regularization), which discourages overly complex decision boundaries. In Amazon SageMaker, this can be implemented via hyperparameters like `l1` or `l2` in built-in algorithms or by adding dropout layers in a custom framework.
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.
Reduce training data
Why it's wrong here
Reducing data would likely increase overfitting.
Apply regularization
Why this is correct
Regularization adds penalty for large weights, helping to reduce overfitting.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Increase learning rate
Why it's wrong here
A higher learning rate may cause unstable training but does not directly address overfitting.
Increase model complexity
Why it's wrong here
Increasing complexity would likely worsen overfitting.
Common exam traps
AWS often tests the misconception that overfitting is solved by increasing data or model complexity, when in fact the first step should be regularization to penalize overly complex models.
Detailed technical explanation
Regularization works by adding a penalty term to the loss function—L2 (Ridge) adds a squared magnitude of weights, while L1 (Lasso) adds absolute values, effectively shrinking less important features to zero. In practice, the regularization strength (lambda) must be tuned via cross-validation; too high a value can cause underfitting. Amazon SageMaker's built-in XGBoost algorithm, for example, exposes `alpha` (L1) and `lambda` (L2) hyperparameters, and in deep learning frameworks like PyTorch or TensorFlow, you can add weight decay directly in the optimizer (e.g., `optimizer = torch.optim.SGD(model.parameters(), lr=0.01, weight_decay=1e-4)`).
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
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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: Apply regularization — The high training accuracy (99%) paired with significantly lower test accuracy (70%) is a classic symptom of overfitting, where the model memorizes the training data instead of learning generalizable patterns. Regularization (Option B) is the first-line technique to combat overfitting by adding a penalty to the loss function (e.g., L1 or L2 regularization), which discourages overly complex decision boundaries. In Amazon SageMaker, this can be implemented via hyperparameters like `l1` or `l2` in built-in algorithms or by adding dropout layers in a custom framework.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Read the scenario before looking for a memorised answer.
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Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A team trained a deep learning model that achieves 99% accuracy on training data but only 70% on validation data. What is the most likely issue?
easyWhy B: The model performs exceptionally well on training data (99% accuracy) but significantly worse on validation data (70% accuracy). This large gap indicates the model has memorized the training data, including noise and irrelevant patterns, rather than learning generalizable features — a classic symptom of overfitting.
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Last reviewed: Jun 30, 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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