Question 1 of 507
ML Model DevelopmenthardMultiple ChoiceObjective-mapped

Quick Answer

The correct answer is that the training script is reading the CSV file incorrectly, causing a shape mismatch. This error occurs because when SageMaker ingests a CSV with a single feature column, the default pandas or numpy read operation may return a one-dimensional array, while scikit-learn models like LinearRegression or LogisticRegression explicitly require a 2D array input—even for a single feature. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of how SageMaker training jobs handle data shape expectations versus scikit-learn’s API requirements. A common trap is assuming the error is about data types or missing values, but the root cause is almost always the dimensionality of the input after CSV parsing. To debug a shape mismatch error in SageMaker training scripts, always check that you reshape your data with `.reshape(-1, 1)` or use double brackets when selecting a single column. Memory tip: “One feature, two brackets—reshape to avoid the brackets.”

MLA-C01 ML Model Development Practice Question

This MLA-C01 practice question tests your understanding of ml model development. 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

{
    "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.

Question 1hardmultiple choice
Full question →

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"
        }
    ]
}

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

The training script is reading the CSV file incorrectly, causing a shape mismatch.

The error 'Expected 2D array, got 1D array' indicates the input data is being interpreted as a single-dimensional array. In SageMaker, when reading a CSV file with one column, the default behavior may produce a 1D array. The script likely expects a 2D array. Option A is correct because the script is incorrectly reading or processing the CSV, causing a shape mismatch.

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 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

    Read the scenario before looking for a memorised answer.

  • 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

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 MLA-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

Questions learners often ask

What does this MLA-C01 question test?

ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: The training script is reading the CSV file incorrectly, causing a shape mismatch. — The error 'Expected 2D array, got 1D array' indicates the input data is being interpreted as a single-dimensional array. In SageMaker, when reading a CSV file with one column, the default behavior may produce a 1D array. The script likely expects a 2D array. Option A is correct because the script is incorrectly reading or processing the CSV, causing a shape mismatch.

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

Identify which MLA-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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Last reviewed: Jun 23, 2026

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This MLA-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 MLA-C01 exam.