MLS-C01 Modeling Practice Question
Network Topology
A data scientist ran an XGBoost training job on Amazon SageMaker using a CSV dataset. The training job failed with the error shown. What is the most likely cause of this failure?
⚠ Common exam trap
It's easy for candidates to confuse a column count mismatch with missing values or delimiter issues, but the error message specifically points to inconsistent row lengths, not data quality or formatting problems.
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
✓
One of the rows in the CSV has an extra column
XGBoost on SageMaker expects the CSV input to have a consistent number of columns across all rows. If one row contains an extra column, the parser will fail because it cannot map the additional field to the feature schema defined by the training job. This mismatch causes the 'Error: Number of columns does not match' or similar parsing error.
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 dataset includes a header row
Why it's wrong here
A header row would cause the first row to have the same number of fields as the data; the error would not be about field count mismatch.
- ✓
One of the rows in the CSV has an extra column
Why this is correct
The error message indicates that line 1 has 11 fields instead of the expected 10, meaning an extra column in that row.
- ✗
The delimiter used in the CSV is not a comma
Why it's wrong here
If the delimiter were different, all rows would likely have the same number of fields; the inconsistency suggests a specific row issue.
- ✗
The dataset contains missing values
Why it's wrong here
Missing values would appear as empty fields but would not change the field count; the number of delimiters per row would remain the same.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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