← MLS-C01·Amazon Web Services

Question 564 of 1,755

Modeling →hardMultiple ChoiceObjective-mapped

Improve Time Series Model Generalization with Expanding Window Cross-Validation

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 data scientist is training a time series forecasting model using Amazon SageMaker's DeepAR algorithm. The dataset contains daily sales data for 10,000 products over 2 years. The scientist splits the data chronologically: training on the first 18 months, validation on the next 3 months, and test on the last 3 months. The model performs well on validation but poorly on test. The data scientist suspects the model is overfitting to the validation period. Which action should the scientist take to improve test performance?

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.

A
Use time series cross-validation with an expanding window
Correct. Expanding window cross-validation uses multiple validation periods, reducing overfitting to a single validation window and improving generalization to the test period.
B
Reduce the context length to 30 days
Why wrong: Incorrect. Reducing context length to 30 days may lose long-term patterns and does not directly address overfitting to the validation period.
C
Add more exogenous features like holidays and promotions
Why wrong: Incorrect. Adding more exogenous features could introduce noise and may worsen overfitting; it does not specifically address overfitting to the validation period.
D
Use the entire dataset for training and ignore validation
Why wrong: Incorrect. Using the entire dataset for training and ignoring validation removes the ability to detect overfitting and may still result in poor test performance.

Full breakdown with real-world context →

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

✓

Use time series cross-validation with an expanding window

The correct answer is A. Time series cross-validation with an expanding window evaluates the model on multiple validation periods, ensuring robustness and reducing overfitting to a single validation window. Option B reduces context length and may lose long-term dependencies. Option C may introduce irrelevant features and does not address overfitting to validation. Option D removes validation entirely, which would not help diagnose or reduce overfitting.

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.

✓
Use time series cross-validation with an expanding window
Why this is correct
Correct. Expanding window cross-validation uses multiple validation periods, reducing overfitting to a single validation window and improving generalization to the test period.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Reduce the context length to 30 days
Why it's wrong here
Incorrect. Reducing context length to 30 days may lose long-term patterns and does not directly address overfitting to the validation period.
✗
Add more exogenous features like holidays and promotions
Why it's wrong here
Incorrect. Adding more exogenous features could introduce noise and may worsen overfitting; it does not specifically address overfitting to the validation period.
✗
Use the entire dataset for training and ignore validation
Why it's wrong here
Incorrect. Using the entire dataset for training and ignoring validation removes the ability to detect overfitting and may still result in poor test performance.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.

Detailed technical explanation

How to think about this question

This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.

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.
Use explanations to understand the rule behind the answer.

TExam Day Tips

Underline the problem statement mentally.
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 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

Got this wrong? Here's your next step.

Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

Related MLS-C01 practice-question pages

Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.

Data Engineering practice questions

Practise MLS-C01 questions linked to Data Engineering.

Machine Learning Implementation and Operations practice questions

Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.

Modeling practice questions

Practise MLS-C01 questions linked to Modeling.

Exploratory Data Analysis practice questions

Practise MLS-C01 questions linked to Exploratory Data Analysis.

MLS-C01 fundamentals practice questions

Practise MLS-C01 questions linked to MLS-C01 fundamentals.

MLS-C01 scenario practice questions

Practise MLS-C01 questions linked to MLS-C01 scenario.

MLS-C01 troubleshooting practice questions

Practise MLS-C01 questions linked to MLS-C01 troubleshooting.

Practice this exam

Start a free MLS-C01 practice session

Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.

10 questions 20 questions 30 questions 50 questions Timed 30

MLS-C01 practice-test guide →Study guide →Browse all practice tests

FAQ

Questions learners often ask

What does this MLS-C01 question test?

Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Use time series cross-validation with an expanding window — The correct answer is A. Time series cross-validation with an expanding window evaluates the model on multiple validation periods, ensuring robustness and reducing overfitting to a single validation window. Option B reduces context length and may lose long-term dependencies. Option C may introduce irrelevant features and does not address overfitting to validation. Option D removes validation entirely, which would not help diagnose or reduce overfitting.

What should I do if I get this MLS-C01 question wrong?

Are there clue words in this question I should notice?

Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

About these practice questions

Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →

How Courseiva writes practice questions · Editorial policy

Keep practising

Question Discussion

Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.

Loading comments…

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.