AWS Certified AI Practitioner AIF-C01 Practice Question
A team is building a text-classification model. Before starting training, they want to confirm that the collected dataset meets basic quality standards. Which action will best help validate data quality during this preparation phase?
Randomly duplicate existing records until each class has the same number of examples.
Remove all records containing rare words so the model trains on common vocabulary only.
Wait until after model training to split the data into training and test sets.
Generate a report that flags null values, duplicate rows, and out-of-range values in the dataset.
Validating a dataset's quality typically starts with exploratory checks that reveal obvious problems. Scanning the data for missing values, duplicate records, or out-of-range entries surfaces issues that can distort learning and should be fixed before training. Simply duplicating data, deleting minority or rare examples, or waiting until after training to create a test split does not assess or improve the underlying data quality and may even introduce new biases or leakage.
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AWS Certified AI Practitioner AIF-C01
Guidelines for Responsible AI
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