The correct answer is that the training data contains missing or infinite values. This is because the ValueError specifically flags the presence of NaN (Not a Number) or infinite values in the dataset, which disrupt the mathematical operations during gradient descent or loss calculation, causing the model to fail. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this error tests your ability to diagnose data quality issues before tuning hyperparameters or scaling infrastructure—a common trap is assuming the error stems from model architecture or memory limits. To avoid this, always run a quick data validation check using functions like `df.isnull().sum()` or `np.isinf()` before training. Remember the mnemonic: “NaN means data, not parameters—clean your inputs first.”
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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.
Exhibit
Refer to the exhibit.
CloudWatch Logs snippet:
2023-07-01T10:00:00 ERROR: Model training failed: ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
Traceback:
File "train.py", line 45, in <module>
model.fit(X_train, y_train)
File "sklearn/linear_model/_logistic.py", line 1523, in fit
...
A data scientist receives the above error during model training. What is the most likely cause?
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.
Refer to the exhibit.
CloudWatch Logs snippet:
2023-07-01T10:00:00 ERROR: Model training failed: ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
Traceback:
File "train.py", line 45, in <module>
model.fit(X_train, y_train)
File "sklearn/linear_model/_logistic.py", line 1523, in fit
...
A
The training data contains missing or infinite values.
The error explicitly states 'Input contains NaN, infinity or a value too large'.
B
The learning rate is too high.
Why wrong: Learning rate issues cause convergence problems, not NaN in input.
C
The data format is incorrect; expected CSV but received JSON.
Why wrong: Format errors would be parsing errors.
D
The instance type lacks sufficient memory.
Why wrong: Memory errors would show OutOfMemory, not NaN.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The training data contains missing or infinite values.
Option B is correct. The error indicates NaN or infinite values in the input data. Option A is wrong because the error is about data, not hyperparameters. Option C is wrong because the error is not about memory. Option D is wrong because the error is not about data format.
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.
✓
The training data contains missing or infinite values.
Why this is correct
The error explicitly states 'Input contains NaN, infinity or a value too large'.
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 learning rate is too high.
Why it's wrong here
Learning rate issues cause convergence problems, not NaN in input.
✗
The data format is incorrect; expected CSV but received JSON.
Why it's wrong here
Format errors would be parsing errors.
✗
The instance type lacks sufficient memory.
Why it's wrong here
Memory errors would show OutOfMemory, not NaN.
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.
Trap categories for this question
Command / output trap
Memory errors would show OutOfMemory, not NaN.
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.
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The training data contains missing or infinite values. — Option B is correct. The error indicates NaN or infinite values in the input data. Option A is wrong because the error is about data, not hyperparameters. Option C is wrong because the error is not about memory. Option D is wrong because the error is not about data format.
What should I do if I get this MLS-C01 question wrong?
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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