Q: 13
[Modeling]
A company that runs an online library is implementing a chatbot using Amazon Lex to provide book
recommendations based on category. This intent is fulfilled by an AWS Lambda function that queries
an Amazon DynamoDB table for a list of book titles, given a particular category. For testing, there are
only three categories implemented as the custom slot types: "comedy," "adventure,” and
"documentary.”
A machine learning (ML) specialist notices that sometimes the request cannot be fulfilled because
Amazon Lex cannot understand the category spoken by users with utterances such as "funny," "fun,"
and "humor." The ML specialist needs to fix the problem without changing the Lambda code or data
in DynamoDB.
How should the ML specialist fix the problem?
Options
Discussion
D makes sense, but what if users say something unexpected that's not a direct synonym? Sometimes Lex can't generalize unless synonyms are mapped specifically. In similar exam questions, AWS is picky about using synonyms over creating extra slot types or adding to enumeration values. I think D is right, but it really depends on how you anticipate user input. Someone disagree?
D imo. Adding those words as synonyms in the custom slot type lets Lex map "funny", "humor" etc to "comedy" without touching code or DynamoDB. Pretty confident that's what AWS recommends for these cases.
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