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AWS AI services: MLA-C02 study notes and practice questions

What to know about AWS AI services for the MLA-C02 exam, from the ML Model and Foundation Model (FM) Development domain (24% of the exam guide), then 1 free question to test it. Part of the MLA-C02 study guide.

Last verified October 2026 against current AWS documentation ·How we write our questions

What to know for the exam

AI service questions describe a task and ask for the managed service that does it with least effort. Learn the one-line purpose of each.

The services

Service	Does
Amazon Textract	Extracts text, forms (key-value pairs), tables, signatures and queries from scanned documents; `AnalyzeDocument` with FORMS and TABLES; AnalyzeExpense and AnalyzeID
Amazon Comprehend	NLP: sentiment, entities, key phrases, language, PII detection, topic modeling; custom classification and custom entities with your labels
Amazon Rekognition	Image and video analysis: labels, faces, text in images, content moderation; Custom Labels for your own objects
Amazon Transcribe	Speech to text, speaker labels, custom vocabularies, PII redaction; Call Analytics
Amazon Translate	Translation, with custom terminology
Amazon Polly	Text to speech
Amazon Lex	Chatbots with intents and slots
Amazon Kendra	Enterprise search over documents; can serve as a RAG retriever
Amazon Personalize	Recommendations from interaction data
Amazon Fraud Detector (closed to new customers)	Fraud scoring; not for new designs
Amazon Bedrock Data Automation	Extracts structured insights from documents, images, audio and video with generative AI

Choosing a service or a custom model

If the task matches a service, the service wins on effort and time.
Use the service’s custom features (Comprehend custom classification, Rekognition Custom Labels) before training from scratch.
Build in SageMaker AI when the task is unusual, you need full control, or the services don’t reach the accuracy you need.

Human review

Amazon Augmented AI (A2I) sends low-confidence Textract or Rekognition results to human reviewers.

Common traps

Comprehend or Rekognition to read form tables. Use Textract.
Training a custom OCR model when Textract handles the documents.
Transcribe to translate speech. Transcribe, then Translate.

Try the 1 practice question

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Question 1

Easier ·Link to this question

A dental chain receives scanned insurance claim forms from patients by mail and fax. Each form has labeled fields such as patient name, member ID and procedure codes, and many forms include tables listing each treatment and its charge.

The chain has no machine learning team. It needs the field values and the tables pulled out as structured data with the least custom development.

Which AWS service should the chain use?

Choose one answer.

A. Amazon Rekognition, using its text detection API to find the words on each page and then pairing labels with values in custom code.
B. Amazon Comprehend, using its entity recognition on the scanned forms after they are saved as text files.
C. Amazon Textract, calling AnalyzeDocument with the FORMS and TABLES feature types to return key-value pairs and table cells.
D. Train a custom convolutional neural network in SageMaker AI on the scanned forms, labeling every field by hand first.

Show the answer and explanation

Answer: C

A. Wrong: Rekognition detects objects, faces and some text in images, but it does not return form key-value pairs or table structure. The chain would have to build the extraction itself.
B. Wrong: Comprehend analyzes text that is already available, and it does not read scanned pages or extract table cells. The forms need a document extraction service first.
C. Right: Correct. Textract reads scanned documents and, with the FORMS and TABLES feature types, returns labeled fields and table cells as structured data. No custom model is needed. Learn more: What is Amazon Textract? (opens in a new tab)
D. Wrong: A custom model needs many labeled forms and ML expertise, which the chain does not have. A managed document service already covers this task with less effort.

Explanation

Amazon Textract extracts text, form fields and tables from scanned documents, and AnalyzeDocument with FORMS and TABLES returns them as structured data. Rekognition and Comprehend do not parse forms and tables, and a custom model is more work than this managed service needs.

Study

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What is Amazon Textract?