The answer is Amazon Comprehend Medical. This AWS service is purpose-built to extract structured medical information such as diagnoses, medications, and test results from unstructured clinical text using natural language processing and machine learning, making it the correct choice for parsing clinical notes and medical documents. On the AWS Certified Cloud Practitioner CLF-C02 exam, this question tests your ability to match a specialized healthcare use case with the appropriate service, often appearing alongside traps like Amazon Comprehend (which handles general text but lacks medical-specific entity recognition) or Amazon Textract (which extracts text from documents but does not interpret medical meaning). A reliable memory tip is to think of the word “Medical” in the service name as your clue—if the scenario involves clinical notes, diagnoses, or prescriptions, Amazon Comprehend Medical is the only service designed for that domain.
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 healthcare company needs to extract structured medical data from clinical notes and medical documents. Which AWS service provides ML-powered extraction of medical information from unstructured text?
Why wrong: Textract extracts text and structured data (forms, tables) from scanned documents — it doesn't understand medical terminology or relationships between medical concepts.
B
Amazon Comprehend
Why wrong: Standard Comprehend provides general NLP (sentiment, entities, key phrases) but lacks the medical domain models for clinical terminology and PHI detection.
C
Amazon Comprehend Medical
Comprehend Medical uses pre-trained models for medical text understanding, extracting medications, conditions, dosages, and PHI from clinical notes and medical documents.
D
Amazon Rekognition
Why wrong: Rekognition analyzes images and videos — it doesn't process medical text documents.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Amazon Comprehend Medical
Amazon Comprehend Medical is specifically designed to extract structured medical information such as diagnoses, medications, and test results from unstructured clinical text using natural language processing (NLP) and machine learning. It is the only AWS service purpose-built for healthcare use cases like parsing clinical notes and medical documents.
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 Textract
Why it's wrong here
Textract extracts text and structured data (forms, tables) from scanned documents — it doesn't understand medical terminology or relationships between medical concepts.
✗
Amazon Comprehend
Why it's wrong here
Standard Comprehend provides general NLP (sentiment, entities, key phrases) but lacks the medical domain models for clinical terminology and PHI detection.
✓
Amazon Comprehend Medical
Why this is correct
Comprehend Medical uses pre-trained models for medical text understanding, extracting medications, conditions, dosages, and PHI from clinical notes and medical documents.
Related concept
Read the scenario before looking for a memorised answer.
✗
Amazon Rekognition
Why it's wrong here
Rekognition analyzes images and videos — it doesn't process medical text documents.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse Amazon Comprehend (general NLP) with Amazon Comprehend Medical (healthcare-specific), assuming the general service can handle medical text without the specialized medical ontology and compliance features.
Trap categories for this question
Keyword trap
Standard Comprehend provides general NLP (sentiment, entities, key phrases) but lacks the medical domain models for clinical terminology and PHI detection.
Detailed technical explanation
How to think about this question
Amazon Comprehend Medical uses a pre-trained deep learning model trained on medical literature and clinical notes to identify entities like ICD-10-CM codes, RxNorm medication names, and anatomical terms. It also supports Protected Health Information (PHI) detection and can be integrated with AWS HealthLake for longitudinal patient data analysis. A subtle behavior is that it can detect negation (e.g., 'no evidence of cancer') to avoid false positives in clinical decision support.
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 Comprehend Medical — Amazon Comprehend Medical is specifically designed to extract structured medical information such as diagnoses, medications, and test results from unstructured clinical text using natural language processing (NLP) and machine learning. It is the only AWS service purpose-built for healthcare use cases like parsing clinical notes and medical documents.
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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