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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #131
One of your models is trained using data provided by a third-party data broker. The data broker does not reliably notify you of formatting changes in the data. You want to make your model training pip
The correct answer is A. Use TensorFlow Data Validation to detect and flag schema anomalies.. TensorFlow Data Validation (TFDV) is a library that can help you detect and flag anomalies in your dataset, such as changes in the schema or data types.
Submitted by diego_uy· Apr 18, 2026Data processing and feature engineering
Question
One of your models is trained using data provided by a third-party data broker. The data broker does not reliably notify you of formatting changes in the data. You want to make your model training pipeline more robust to issues like this. What should you do?
Options
- AUse TensorFlow Data Validation to detect and flag schema anomalies.
- BUse TensorFlow Transform to create a preprocessing component that will normalize data to the
- CUse tf.math to analyze the data, compute summary statistics, and flag statistical anomalies.
- DUse custom TensorFlow functions at the start of your model training to detect and flag known
How the community answered
(32 responses)- A72% (23)
- B16% (5)
- C9% (3)
- D3% (1)
Explanation
TensorFlow Data Validation (TFDV) is a library that can help you detect and flag anomalies in your dataset, such as changes in the schema or data types.
Topics
#Data validation#Schema anomalies#ML pipeline robustness#TensorFlow Data Validation
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