The answer is Amazon Rekognition. This is the correct choice because it is a fully managed, pre-built computer vision service that uses deep learning to automatically detect and label objects, scenes, and faces in images without requiring any custom model development. For the scenario of identifying whether a photo contains a person, car, or outdoor scene, Rekognition’s pre-trained APIs directly analyze the image and return relevant labels, matching the exact need for pre-built computer vision object detection. On the AWS Certified Cloud Practitioner CLF-C02 exam, this question tests your understanding of managed AI services versus custom ML solutions—a common trap is confusing Rekognition with Amazon SageMaker, which is for building your own models, not using pre-built capabilities. Remember the memory tip: “Rekognition recognizes things right out of the box,” so if the task says “without building a model,” think Rekognition.
CLF-C02 Cloud Technology and Services Practice Question
This CLF-C02 practice question tests your understanding of cloud technology and services. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company wants to automatically detect and label objects in photos uploaded by users — such as identifying if a photo contains a person, a car, or an outdoor scene — without building their own machine learning model. Which AWS service provides this pre-built computer vision capability?
Why wrong: SageMaker is for building, training, and deploying custom ML models. For pre-built object and scene detection without training a model, Rekognition is the correct service.
B
Amazon Comprehend
Why wrong: Comprehend is a natural language processing service for text analysis (sentiment, entities, key phrases). It does not analyse images.
C
Amazon Rekognition
Rekognition provides pre-trained computer vision via an API. It detects objects, scenes, people, text, and faces in images and video without requiring any ML model training.
D
Amazon Polly
Why wrong: Polly is a text-to-speech service that converts text into natural-sounding speech. It does not analyse images.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Amazon Rekognition
Amazon Rekognition is the correct choice because it is a fully managed, pre-trained computer vision service that can automatically detect and label objects, scenes, and faces in images without requiring any custom machine learning model development. It provides APIs for image analysis, including object and scene detection, which directly matches the requirement to identify if a photo contains a person, car, or outdoor scene.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 for building, training, and deploying custom ML models. For pre-built object and scene detection without training a model, Rekognition is the correct service.
✗
Amazon Comprehend
Why it's wrong here
Comprehend is a natural language processing service for text analysis (sentiment, entities, key phrases). It does not analyse images.
✓
Amazon Rekognition
Why this is correct
Rekognition provides pre-trained computer vision via an API. It detects objects, scenes, people, text, and faces in images and video without requiring any ML model training.
Related concept
Read the scenario before looking for a memorised answer.
✗
Amazon Polly
Why it's wrong here
Polly is a text-to-speech service that converts text into natural-sounding speech. It does not analyse images.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Amazon Rekognition with Amazon SageMaker, assuming SageMaker is the go-to for all AI/ML tasks, but SageMaker requires custom model building, whereas Rekognition provides pre-built computer vision capabilities out of the box.
Trap categories for this question
Keyword trap
Comprehend is a natural language processing service for text analysis (sentiment, entities, key phrases). It does not analyse images.
Detailed technical explanation
How to think about this question
Amazon Rekognition uses deep learning neural networks trained on vast datasets to perform tasks like object detection, facial analysis, and content moderation. Under the hood, it leverages convolutional neural networks (CNNs) to extract features from images and classify them into predefined categories such as 'Person', 'Car', or 'Outdoor'. A subtle behavior is that Rekognition can return confidence scores for each label, allowing developers to filter results based on a threshold (e.g., only accept labels with >90% confidence) to reduce false positives in production applications.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this CLF-C02 question in full detail.
Cloud Technology and Services — This question tests Cloud Technology and Services — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Amazon Rekognition — Amazon Rekognition is the correct choice because it is a fully managed, pre-trained computer vision service that can automatically detect and label objects, scenes, and faces in images without requiring any custom machine learning model development. It provides APIs for image analysis, including object and scene detection, which directly matches the requirement to identify if a photo contains a person, car, or outdoor scene.
What should I do if I get this CLF-C02 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company wants to use machine learning to automatically identify objects, scenes, and activities in images uploaded by users. Which AWS service should they use?
easy
A.Amazon Textract
B.Amazon SageMaker
✓ C.Amazon Rekognition
D.Amazon Comprehend
Why C: Amazon Rekognition is the correct service because it is specifically designed to analyze images and videos to identify objects, scenes, activities, faces, and text. It provides pre-trained machine learning models that can automatically detect these elements without requiring custom model training, making it ideal for the use case described.
Last reviewed: Jun 11, 2026
Question Discussion
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This CLF-C02 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 CLF-C02 exam.
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