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MLA-C01 Data Preparation for Machine Learning Practice Question

A company is building a time series forecasting model using SageMaker DeepAR. The raw data is a CSV with columns: timestamp, item_id, and value. What is the correct data format required for DeepAR training?

⚠ Common exam trap

Many candidates assume DeepAR can accept raw CSV data like other SageMaker built-in algorithms (e.g., XGBoost), but DeepAR is a specialized time series algorithm that requires a specific JSON Lines structure with 'start' and 'target' fields, not a simple tabular format.

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

JSON Lines files with 'start', 'target', and optional fields per time series

DeepAR requires time series data to be provided in JSON Lines format, where each line represents a single time series with a 'start' timestamp (in ISO 8601 format), a 'target' array of values, and optional fields like 'cat' for categorical features. This structured format allows DeepAR to handle variable-length sequences and missing values natively, which is not possible with simple CSV or wide-format data.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • JSON Lines files with 'start', 'target', and optional fields per time series

    Why this is correct

    DeepAR's training data format is JSON Lines with start timestamp and target array.

  • A wide-format CSV where each column is a different time series

    Why it's wrong here

    DeepAR expects the data in long format with one row per time step.

  • Parquet files with a schema containing timestamp, item_id, and value

    Why it's wrong here

    DeepAR does natively support Parquet for training input; it uses RecordIO or JSON Lines.

  • A single CSV file with columns: timestamp, item_id, value

    Why it's wrong here

    DeepAR requires JSON Lines, not CSV.

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