Match each data format to its typical use in AWS ML.
Drag a concept onto its matching description — or click a concept then click the description.
Tabular data for SageMaker built-in algorithms
Efficient binary format for SageMaker
Columnar storage for analytics
Semi-structured data, e.g., for Lambda
TensorFlow training data format
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
CSV: Tabular data for built-in algorithms
Common data formats in AWS ML: CSV for tabular data, JSON for semi-structured inference I/O, Parquet for columnar analytics, and TFRecord for TensorFlow training. Distractors confuse CSV with Parquet and JSON with binary formats.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
CSV: Tabular data for built-in algorithms
Why this is correct
CSV (Comma-Separated Values) is a common format for tabular data, and AWS ML built-in algorithms like Linear Learner and XGBoost expect CSV input.
JSON: Semi-structured data for real-time inference
Why this is correct
JSON (JavaScript Object Notation) is used for semi-structured data, often in real-time inference requests to SageMaker endpoints.
Parquet: Columnar storage for analytics
Why this is correct
Parquet is a columnar storage format optimized for analytical queries, commonly used with AWS Athena and Redshift Spectrum.
TFRecord: TensorFlow training data
Why this is correct
TFRecord is a binary format used by TensorFlow for efficient data loading during training on SageMaker.
CSV: Columnar storage for analytics
Why it's wrong here
Incorrect — CSV is row-oriented, not columnar. Columnar storage is a characteristic of Parquet, not CSV.
JSON: Binary format for efficient I/O
Why it's wrong here
Incorrect — JSON is a text-based format, not binary. Binary formats like RecordIO or TFRecord are used for efficient I/O.
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