MLS-C01 Modeling Practice Question
A company uses SageMaker to train a time-series forecasting model using Amazon Forecast. The dataset contains historical sales data for 10,000 products over 2 years. Which data format is required for the target time series?
⚠ Common exam trap
Many candidates assume Amazon Forecast supports flexible data formats like JSON or Parquet for all dataset types, but the target time series is strictly restricted to CSV to ensure consistent parsing and algorithm compatibility.
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
✓
A CSV file with columns: timestamp, target_value, item_id
Amazon Forecast requires the target time series data to be in a CSV format with specific columns: timestamp, target_value, and item_id. This structured format allows the service to correctly identify the time series for each product and the target metric to forecast. The CSV format is the standard input for Forecast's built-in algorithms and ensures compatibility with the dataset import process.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A single JSON file with nested arrays
Why it's wrong here
Forecast uses CSV for input datasets.
- ✓
A CSV file with columns: timestamp, target_value, item_id
Why this is correct
This is the required format for target time series in Forecast.
- ✗
A text file with one value per line
Why it's wrong here
Forecast requires structured format with timestamps and identifiers.
- ✗
A Parquet file partitioned by date
Why it's wrong here
Forecast supports CSV and Parquet, but the schema must include the required columns.
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