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AIF-C01 Practice Question: A team is planning to build an ML model that…
A team is planning to build an ML model that recommends products to users based on their purchase history. Which AWS service is MOST suitable?
⚠ Common exam trap
Many exam-takers confuse Amazon Personalize with Amazon SageMaker, thinking SageMaker is the only ML service, but Personalize is purpose-built for recommendations and requires far less custom coding and infrastructure management.
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 Personalize
Amazon Personalize is the correct choice because it is a fully managed service specifically designed to build real-time recommendation systems using user-item interaction data, such as purchase history. It uses deep learning algorithms like HRNN (Hierarchical Recurrent Neural Network) to personalize product recommendations for each user, making it the most suitable for this use case.
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 SageMaker
Why it's wrong here
SageMaker is a general model-building platform requiring you to select algorithms, engineer features and train recommenders yourself, whereas Amazon Personalize provides purpose-built recommendation recipes. SageMaker is tempting because it hosts any custom ML workload, and would be correct if the team needed bespoke model code or full training control.
- ✗
Amazon Forecast
Why it's wrong here
Forecast produces time-series predictions such as demand or revenue, not ranked item recommendations from user purchase histories. It is tempting because it consumes historical purchase data, but it would be correct for predicting future numeric values like inventory demand, not for generating personalised product suggestions.
- ✗
Amazon Rekognition
Why it's wrong here
Rekognition performs image and video analysis such as object, face and label detection, and cannot generate product recommendations from purchase histories. It is tempting because it is a ready-made AI service, but it would be correct for scenarios involving analysing images or video content, not behavioural recommendation.
- ✓
Amazon Personalize
Why this is correct
Amazon Personalize is purpose-built for recommendation systems, ingesting purchase history to train custom models that surface product suggestions via real-time inference. It satisfies the stem's requirement for purchase-history-driven recommendations without manual algorithm development, unlike general-purpose ML platforms such as SageMaker, which would demand substantial custom engineering.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.