This MLS-C01 practice question tests your understanding of modeling. 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.
Refer to the exhibit. A SageMaker training job failed with the error shown. What is the most likely cause of this error?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
The input data contains missing or invalid values
NaN or infinity in data cause this error.
The training algorithm is not compatible with the data type
Why wrong: The algorithm can handle float64, but the data contains invalid values.
The training instance type is not powerful enough
Why wrong: Instance type does not cause NaN errors.
The training script has a syntax error
Why wrong: A syntax error would produce a different error, not a ValueError about data.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
The input data contains missing or invalid values
The error indicates that the input data contains NaN or infinite values. This is a data quality issue. The algorithm expects clean numeric values. The algorithm itself is fine; the training script may have a bug but the error specifically points to input data.
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.
The input data contains missing or invalid values
Why this is correct
NaN or infinity in data cause this error.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
The training algorithm is not compatible with the data type
Why it's wrong here
The algorithm can handle float64, but the data contains invalid values.
The training instance type is not powerful enough
Why it's wrong here
Instance type does not cause NaN errors.
The training script has a syntax error
Why it's wrong here
A syntax error would produce a different error, not a ValueError about data.
Common exam traps
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
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.
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 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.
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: The input data contains missing or invalid values — The error indicates that the input data contains NaN or infinite values. This is a data quality issue. The algorithm expects clean numeric values. The algorithm itself is fine; the training script may have a bug but the error specifically points to input data.
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.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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. Refer to the exhibit. A training job failed with the error shown. What is the most likely cause?
hardWhy B: The error message explicitly states that the input contains NaN or infinity, which indicates missing values or outliers in the training data. This causes numerical instability during training. Option A is incorrect because the error is about input data, not model architecture. Option C is incorrect because the error is from the training script, not insufficient memory. Option D is incorrect because the error is about input values, not runtime limits.
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Last reviewed: Jun 20, 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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