AWS Certified AI Practitioner AIF-C01 Practice Question
A startup wants to train a machine learning model that detects customer sentiment in live chat transcripts. Which type of training data should the team collect and prepare for this use case?
Labeled JPEG images showing different facial expressions
Numeric tabular data of customer purchase amounts with column headers
Labeled text documents annotated with sentiment categories
Unlabeled time-series sensor readings collected at regular intervals
Detecting sentiment in chat transcripts is a natural language processing task, so the model must learn from examples of written language. Labeled text documents that include sentiment tags (for example, positive, neutral, or negative) provide the necessary input-output pairs for supervised learning. Time-series sensor data, numeric tabular records, and labeled images do not contain the textual information required to teach a model about language sentiment, making them unsuitable for this scenario.
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Why are other types of data, such as images or tabular data, not suitable for this problem?
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AWS Certified AI Practitioner AIF-C01
Fundamentals of AI and ML
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