Get Your CertAIF-C01PlayCompete
‹ All AIF-C01 questions

AIF-C01 Practice Question 62: Real Time Recommendations

AWS Certified AI Practitioner (AIF-C01) · Test 7 · question 2 of 10

Fundamentals of AI and MLeasy

A streaming service wants a "Recommended for you" row that reflects what a viewer has just watched. It has three years of view-history events plus a catalogue with genre and release metadata, and it wants a managed service that trains on that data and answers real-time requests from the app. Which service should it choose?

Select one answer
Show answer and explanation

Answer:

  • D. Amazon Personalize, trained on the interaction history and item metadata

Amazon Personalize is *a fully managed machine learning service that uses your data to generate item recommendations for your users*. AWS's first documented use case is personalising a video streaming app with rows such as *Top picks for you* and *More like X*. It generates recommendations *primarily based on item interaction data* (historical bulk records plus real-time events as users interact), and also uses item and user attributes such as genre. It ships API operations for real-time personalisation, which is what the app needs to render the row (option D).

- A is wrong because Comprehend performs NLP on text. It could tag a synopsis with entities and key phrases, but it has no notion of *this viewer* and produces no ranked recommendations. - B is wrong because a foundation model has no learned model of how millions of viewers behave relative to each other. Prompting it with one history yields plausible-sounding titles rather than recommendations learned from interaction data, and it leaves the team to build ranking, evaluation and serving themselves. - C is wrong because Rekognition analyses images and video for objects, scenes, faces and activities. It can enrich the catalogue with content tags, but tagging content is not personalising it for an individual viewer.

Reference: What is Amazon Personalize?

Practice Test 7 ›

Study guides for AIF-C01

AI engineer roadmap 2026: skills, certs and the orderAI engineer roadmap for 2026: skills to learn in order, AI engineer vs ML engineer, which certifications fit each stage, and what to skip.
How AI Is Changing Software Jobs: Where to PrepareHow AI is changing software jobs: what history shows, the evidence on junior developers, what Klarna learned, new AI roles and five domains to prepare for.
How AWS Certification Exams Work: Scoring and RenewalHow AWS certification exams work: levels, booking, question types, unscored questions, scaled scores, pass marks, validity and how recertification works.
What Is Amazon Bedrock? Models, Pricing and GuardrailsWhat is Amazon Bedrock? How a request reaches a model, what happens to your data, token pricing, Guardrails, Knowledge Bases, AgentCore and the exam traps.
Google Cloud Generative AI Leader vs AWS AI PractitionerGoogle Cloud Generative AI Leader vs AWS AI Practitioner: format, cost, topics, free preparation and which certification to take first.
AWS certification cost in 2026, and free ways to prepareWhat every AWS certification costs in 2026 in USD and INR, retake and reschedule rules, the 50% voucher, and the free official ways to prepare.
AWS Generative AI Developer Professional (AIP-C01) guideWhat the AWS Certified Generative AI Developer – Professional (AIP-C01) exam covers - format, cost, the five domains, the services, and how to prepare.
Best AI certifications 2026: AWS, Google, Microsoft, ClaudeThe best AI certifications in 2026 from AWS, Google, Microsoft, Claude and Databricks, by level, with price, status and which one to take first.
Which AWS certification should you get? 2026 roadmapWhich AWS certification to get first, by role and experience, with each exam's status in October 2026, what is changing, and prices in USD and INR.
What Is RAG? How Retrieval-Augmented Generation WorksWhat RAG is and how it works: chunking, embeddings, vector search, hybrid search, reranking, GraphRAG and agentic RAG on AWS, Google and Azure.
Prompt engineering vs RAG vs fine-tuningWhen to use prompt engineering, retrieval-augmented generation, fine-tuning or continued pre-training, compared on cost, freshness, hallucination and privacy.