This MLA-C01 practice question tests your understanding of shape mismatch error. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: shape Mismatch Error. 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
{
"TrainingJobName": "fraud-detection-model-20241015",
"TrainingJobStatus": "Failed",
"FailureReason": "AlgorithmError: Encountered an unexpected error during training: ValueError: Expected 2D array, got 1D array instead. Reshape your data using array.reshape(-1, 1) if your data has a single feature.",
"AlgorithmSpecification": {
"TrainingImage": "382416733822.dkr.ecr.us-west-2.amazonaws.com/sagemaker-scikit-learn:1.0-1-cpu-py3",
"TrainingInputMode": "File"
},
"ResourceConfig": {
"InstanceType": "ml.m5.large",
"InstanceCount": 1
},
"InputDataConfig": [
{
"ChannelName": "training",
"DataSource": {
"S3DataSource": {
"S3DataType": "S3Prefix",
"S3Uri": "s3://my-bucket/train/data.csv",
"S3DataDistributionType": "FullyReplicated"
}
},
"ContentType": "text/csv",
"CompressionType": "None"
}
]
}
Refer to the exhibit. A data scientist used a SageMaker training job with a custom Scikit-learn script. The training job failed with the error shown. What is the most likely cause of this failure?
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.
Exhibit
{
"TrainingJobName": "fraud-detection-model-20241015",
"TrainingJobStatus": "Failed",
"FailureReason": "AlgorithmError: Encountered an unexpected error during training: ValueError: Expected 2D array, got 1D array instead. Reshape your data using array.reshape(-1, 1) if your data has a single feature.",
"AlgorithmSpecification": {
"TrainingImage": "382416733822.dkr.ecr.us-west-2.amazonaws.com/sagemaker-scikit-learn:1.0-1-cpu-py3",
"TrainingInputMode": "File"
},
"ResourceConfig": {
"InstanceType": "ml.m5.large",
"InstanceCount": 1
},
"InputDataConfig": [
{
"ChannelName": "training",
"DataSource": {
"S3DataSource": {
"S3DataType": "S3Prefix",
"S3Uri": "s3://my-bucket/train/data.csv",
"S3DataDistributionType": "FullyReplicated"
}
},
"ContentType": "text/csv",
"CompressionType": "None"
}
]
}
A
The training script is reading the CSV file incorrectly, causing a shape mismatch.
Correct: The error indicates a shape issue, and SageMaker's CSV loading can produce 1D arrays for single-column data, which the script must handle.
B
The InputDataConfig specifies ContentType text/csv but the actual file is not CSV.
Why wrong: Incorrect: A content type mismatch would likely fail earlier with a different error (e.g., parsing error).
C
The SageMaker training image is outdated and does not support Scikit-learn 1.0.
Why wrong: Incorrect: The image does support Scikit-learn 1.0; the error is not about compatibility.
D
The training data contains missing values that need to be imputed.
Why wrong: Incorrect: Missing values typically cause NaN errors, not shape errors.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The training script is reading the CSV file incorrectly, causing a shape mismatch.
Option A is correct because the error 'shape mismatch' typically occurs when the number of columns in the CSV file does not match the number of features expected by the Scikit-learn model. In SageMaker, the training script often loads data using pandas or numpy, and if the CSV has extra columns (e.g., an index column, header row misinterpreted, or trailing delimiter), the feature matrix shape will be inconsistent with the model's input dimensions, causing a ValueError during fit or transform.
Key principle: Shape Mismatch 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 training script is reading the CSV file incorrectly, causing a shape mismatch.
Why this is correct
Correct: The error indicates a shape issue, and SageMaker's CSV loading can produce 1D arrays for single-column data, which the script must handle.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Shape Mismatch Error
✗
The InputDataConfig specifies ContentType text/csv but the actual file is not CSV.
Why it's wrong here
Incorrect: A content type mismatch would likely fail earlier with a different error (e.g., parsing error).
✗
The SageMaker training image is outdated and does not support Scikit-learn 1.0.
Why it's wrong here
Incorrect: The image does support Scikit-learn 1.0; the error is not about compatibility.
✗
The training data contains missing values that need to be imputed.
Why it's wrong here
Incorrect: Missing values typically cause NaN errors, not shape errors.
Common exam traps
Common exam trap: answer the scenario, not the keyword
In AWS SageMaker, a shape mismatch error often occurs when the CSV file contains extra columns (e.g., an index column from pandas during save) that increase the feature count beyond what the model expects. This is different from missing values or content type issues.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker training jobs execute the user-provided script in an isolated container. The shape mismatch error often arises from the default behavior of pandas.read_csv() when the CSV includes an unnamed index column (e.g., from a previous DataFrame.to_csv()), which adds an extra column. In real-world scenarios, this is common when data is exported from databases or notebooks without setting index=False, leading to a silent column count mismatch that only surfaces during model training.
KKey Concepts to Remember
Shape Mismatch Error
SageMaker Training Job with Custom Scikit-learn Script
TExam Day Tips
→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
Shape Mismatch Error
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. Shape Mismatch Error 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.
Review shape Mismatch Error, then practise related MLA-C01 questions on the same topic to reinforce the concept.
The correct answer is: The training script is reading the CSV file incorrectly, causing a shape mismatch. — Option A is correct because the error 'shape mismatch' typically occurs when the number of columns in the CSV file does not match the number of features expected by the Scikit-learn model. In SageMaker, the training script often loads data using pandas or numpy, and if the CSV has extra columns (e.g., an index column, header row misinterpreted, or trailing delimiter), the feature matrix shape will be inconsistent with the model's input dimensions, causing a ValueError during fit or transform.
What should I do if I get this MLA-C01 question wrong?
Review shape Mismatch Error, then practise related MLA-C01 questions on the same topic to reinforce the concept.
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?
Shape Mismatch Error
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