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AIF-C01 Practice Question: A retail company wants to forecast product demand…
A retail company wants to forecast product demand at the SKU level for the next 12 weeks. Which AWS service is purpose-built for this task?
⚠ Common exam trap
It's easy for candidates to confuse Amazon Personalize (which also uses ML for predictions) with forecasting, but Personalize is for recommendation systems, not time-series demand prediction.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Amazon Forecast
Amazon Forecast is a fully managed service purpose-built for time-series forecasting, using machine learning to analyze historical data and predict future demand. It is specifically designed for tasks like SKU-level product demand forecasting over a defined time horizon, such as 12 weeks, without requiring deep ML expertise.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon Personalize
Why it's wrong here
Amazon Personalize generates individualised recommendations from user interaction data, not future demand values. It is tempting because it is a managed ML service for retail, but it outputs ranked item suggestions per user, whereas SKU-level forecasting requires a time-series forecasting service.
- ✓
Amazon Forecast
Why this is correct
Amazon Forecast is purpose-built for time-series forecasting, automatically handling seasonality, trends and related datasets to produce SKU-level demand predictions. It removes the need to build and tune custom models, directly meeting the 12-week SKU-level forecasting requirement.
- ✗
Amazon SageMaker
Why it's wrong here
SageMaker is a general-purpose machine learning platform requiring you to build, train and tune forecasting models yourself; it lacks the purpose-built time-series algorithms and SKU-level dataset handling of Amazon Forecast. SageMaker is tempting because it hosts every ML workload, and would be correct for custom model development rather than demand forecasting.
- ✗
Amazon Kendra
Why it's wrong here
Amazon Kendra is an intelligent enterprise search service that indexes and retrieves documents; it performs no time-series forecasting. It is tempting because it uses machine learning, but its purpose is natural-language querying over unstructured content, not predicting SKU-level demand.
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Written by Johnson Ajibi, MSc IT Security
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