Quick Answer
The answer is reducing the number of time series in the training set, as this action is least likely to reduce overfitting in DeepAR time series models. DeepAR relies on learning shared patterns across multiple related time series to generalize effectively; removing series reduces the diversity of training data and the model’s statistical strength, making it more prone to memorizing noise in the remaining series. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding that DeepAR is designed for grouped time series forecasting, where more data across series improves regularization. A common trap is assuming that less data always reduces overfitting, but here the opposite is true—fewer series weaken the model’s ability to capture common temporal dynamics. Memory tip: think “more series, more sharing, less overfitting”—DeepAR thrives on cross-series learning.
This MLS-C01 practice question tests your understanding of modeling. 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 company is using Amazon SageMaker to train a time series forecasting model using the DeepAR algorithm. The training data contains multiple time series. The model is overfitting. Which action is LEAST likely to reduce overfitting?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "least"
Why it matters: You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
Decrease the number of layers in the neural network.
Why wrong: Reducing model complexity reduces overfitting.
Increase the dropout rate.
Why wrong: Dropout is a regularization technique.
Decrease the context length.
Why wrong: Shorter context reduces model capacity.
Reduce the number of time series in the training set.
Less data may 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
Reduce the number of time series in the training set.
Option D is correct because reducing the number of time series in the training set reduces the diversity of training data, which typically increases overfitting rather than reducing it. DeepAR relies on learning patterns across multiple related time series to generalize well; fewer time series mean less shared statistical strength, making the model more likely to memorize noise in the remaining series.
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.
Decrease the number of layers in the neural network.
Why it's wrong here
Reducing model complexity reduces overfitting.
Increase the dropout rate.
Why it's wrong here
Dropout is a regularization technique.
Decrease the context length.
Why it's wrong here
Shorter context reduces model capacity.
Reduce the number of time series in the training set.
Why this is correct
Less data may worsen overfitting.
Clue confirmation
The clue word "least" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates mistakenly think reducing training data always reduces overfitting, but in time series forecasting with DeepAR, fewer time series actually weaken the cross-series learning that regularizes the model, making overfitting worse.
Detailed technical explanation
DeepAR uses a recurrent neural network (RNN) with a negative binomial likelihood loss function; the context length determines how many past time steps the encoder sees. Reducing context length limits the model's temporal memory, which is a form of implicit regularization. In practice, if the context length is too short, the model may underfit by missing seasonality, but it will not overfit as severely as with a long context.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Reduce the number of time series in the training set. — Option D is correct because reducing the number of time series in the training set reduces the diversity of training data, which typically increases overfitting rather than reducing it. DeepAR relies on learning patterns across multiple related time series to generalize well; fewer time series mean less shared statistical strength, making the model more likely to memorize noise in the remaining series.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "least". You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This MLS-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 MLS-C01 exam.
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