AWS Certified AI Practitioner AIF-C01 Practice Question
A logistics company spends several hours each morning manually sequencing 200 delivery trucks using past traffic data and parcel priorities. Which approach illustrates how an AI/ML solution could add value and reduce planning effort in this scenario?
Create IAM policies that restrict route-spreadsheet edits to managers only.
Train a model that predicts delivery times and generates optimized driver routes automatically.
Configure an Amazon CloudWatch Events rule to email planners a daily reminder to finish routing by 7 a.m.
Migrate the current route spreadsheet to Amazon S3 so all planners can access one central file.
Training a machine learning model to predict delivery times and automatically output optimized routes applies AI/ML to the company's planning challenge. The model can process large historical traffic and package datasets faster than people, recommending efficient delivery sequences and freeing planners for higher-value work. Simply moving spreadsheets to Amazon S3, tightening IAM permissions, or sending CloudWatch reminders do not use AI/ML and therefore provide little help with route optimization.
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What type of data is typically needed to train the model for route optimization?
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What are the advantages of using AI/ML for route optimization over manual planning?
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Which AWS services can be used to implement this AI/ML solution for route optimization?
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How does AI/ML predict delivery times?
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What is route optimization using AI?
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Why is storing data in Amazon S3 insufficient for route optimization?
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AWS Certified AI Practitioner AIF-C01
Fundamentals of AI and ML
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