AWS Certified AI Practitioner AIF-C01 Practice Question
A data scientist is selecting an AI technique and needs a dataset that is definitively time-series in nature. Which dataset best fits the definition of time-series data, where each observation is ordered by an explicit timestamp and temporal patterns are expected to matter?
Customer support chat transcripts stored as separate conversation documents
A relational table listing current product IDs, descriptions, and prices
Daily closing prices of a company's stock recorded for each trading day over five years
Thousands of labeled photographs of different animal species
Time-series data consist of observations collected at regular or known points in time, where the order carries meaningful information that models must preserve. A record of a stock's daily closing price provides one value per trading day, indexed by date, so the sequence and interval between points are intrinsic to the dataset. The other options do not consist of numerical observations tied to a consistent timeline: labeled photos are image data, chat transcripts are text documents that may include time stamps but are typically analyzed as NLP data per conversation rather than as one continuous numeric series, and a single relational product table captures a static snapshot without a temporal dimension.
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What is time-series data?
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Why is temporal order important in time-series data?
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How are time-series datasets different from other types of data?
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What makes time-series data unique compared to other types of data?
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How does AI or machine learning utilize time-series data in modeling?
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Why wouldn't labeled photographs or chat transcripts qualify as time-series data?
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AWS Certified AI Practitioner AIF-C01
Fundamentals of AI and ML
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