The answer is Amazon Textract, the AWS AI service designed for document text extraction from images and scanned documents. This service uses machine learning to go beyond basic optical character recognition (OCR), intelligently identifying and extracting printed text, handwriting, tables, and key-value pairs from images, which makes it the precise tool for detecting credit card numbers in user-uploaded photos for compliance blurring. On the AWS Certified Cloud Practitioner CLF-C02 exam, this question tests your ability to match specific AI workloads to the correct service, often appearing alongside traps like Amazon Rekognition (which analyzes images for objects and faces) or Amazon Comprehend (which processes natural language). A common mistake is confusing text extraction with image analysis, so remember that Textract is purpose-built for pulling text out of documents and images, not for visual recognition. For a quick memory tip, think of "Textract" as "Text Extract" — it literally extracts the text from the image for you.
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's application receives images from users and needs to detect and blur any credit card numbers visible in the images for compliance. Which AWS AI service provides document text extraction from images?
Why wrong: Rekognition detects objects, faces, and scenes in images — while it can detect text, Textract is purpose-built for document text extraction with form and table understanding.
B
Amazon Textract
Textract automatically extracts text, forms, and tables from documents and images using ML, providing structured data extraction that goes beyond basic OCR.
C
Amazon Comprehend
Why wrong: Comprehend analyzes text for entities, sentiment, and language — it processes text input, not images.
D
Amazon Macie
Why wrong: Macie discovers sensitive data in S3 storage — it doesn't process image content for text extraction.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Amazon Textract
Amazon Textract is specifically designed to extract text, including handwritten and printed text, from scanned documents and images. It goes beyond simple optical character recognition (OCR) by using machine learning to identify and extract key-value pairs, tables, and form data, making it the correct choice for extracting credit card numbers from images for compliance processing.
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 Rekognition
Why it's wrong here
Rekognition detects objects, faces, and scenes in images — while it can detect text, Textract is purpose-built for document text extraction with form and table understanding.
✓
Amazon Textract
Why this is correct
Textract automatically extracts text, forms, and tables from documents and images using ML, providing structured data extraction that goes beyond basic OCR.
Related concept
Read the scenario before looking for a memorised answer.
✗
Amazon Comprehend
Why it's wrong here
Comprehend analyzes text for entities, sentiment, and language — it processes text input, not images.
✗
Amazon Macie
Why it's wrong here
Macie discovers sensitive data in S3 storage — it doesn't process image content for text extraction.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse Amazon Rekognition's ability to detect text in images (via its DetectText API) with Amazon Textract's specialized document text extraction, but Rekognition is optimized for general scene text (e.g., signs, labels) and lacks the layout analysis and key-value extraction capabilities needed for compliance-grade document processing.
Detailed technical explanation
How to think about this question
Amazon Textract uses a deep learning-based OCR engine that can handle complex layouts, including tables and forms, and can extract text from both scanned PDFs and image files (JPEG, PNG). It supports synchronous (single-page) and asynchronous (multi-page) processing, and its output includes bounding box coordinates for each detected word and line, which is critical for downstream compliance workflows like blurring specific text regions. A subtle behavior is that Textract can detect handwriting, but accuracy may vary with cursive or highly stylized text, requiring post-processing validation for sensitive data like credit card numbers.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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 Textract — Amazon Textract is specifically designed to extract text, including handwritten and printed text, from scanned documents and images. It goes beyond simple optical character recognition (OCR) by using machine learning to identify and extract key-value pairs, tables, and form data, making it the correct choice for extracting credit card numbers from images for compliance processing.
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.
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Question Discussion
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