Quick Answer
The answer is Amazon Lex and Amazon Bedrock. This combination works because Lex provides the conversational interface and natural language understanding to interpret user intents and manage dialog flow, while Bedrock supplies access to foundation models for advanced natural language generation and richer responses. When a user utterance is received, Lex can route it to a Bedrock foundation model via a Lambda function or direct integration, allowing the chatbot to leverage a pre-trained FM rather than relying solely on static Lex intents. On the AWS Certified AI Practitioner AIF-C01 exam, this question tests your understanding of how to extend Lex’s built-in NLU with a generative AI layer; a common trap is selecting Amazon Comprehend or Amazon Polly, which handle sentiment analysis or speech synthesis, not conversational AI with foundation models. Remember the pairing: Lex handles the “talk” and dialog management, Bedrock provides the “brain” for generative responses—think “Lex for the chat, Bedrock for the smarts.”
This AIF-C01 practice question tests your understanding of applications of foundation models. 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.
Which TWO AWS services can be used together to build a chatbot that leverages a foundation model for natural language understanding?
Amazon Rekognition
Why wrong: Rekognition is for image/video analysis.
Amazon Lex
Lex handles dialog management and intent recognition.
Amazon Polly
Why wrong: Polly converts text to speech, not NLU.
AWS Glue
Why wrong: Glue is an ETL service, not relevant for chatbots.
Amazon Bedrock
Bedrock provides the underlying FM for NLU.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Amazon Lex
Amazon Lex provides the conversational interface and natural language understanding (NLU) to interpret user intents and manage dialog, while Amazon Bedrock gives access to foundation models (FMs) for advanced natural language generation and understanding. Together, Lex can route utterances to a Bedrock FM via a Lambda function or direct integration, enabling a chatbot that leverages a pre-trained FM for richer responses.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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 is for image/video analysis.
Amazon Lex
Why this is correct
Lex handles dialog management and intent recognition.
Related concept
Read the scenario before looking for a memorised answer.
Amazon Polly
Why it's wrong here
Polly converts text to speech, not NLU.
AWS Glue
Why it's wrong here
Glue is an ETL service, not relevant for chatbots.
Amazon Bedrock
Why this is correct
Bedrock provides the underlying FM for NLU.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
AWS often tests the distinction between services that handle conversational interfaces (Lex) versus those that provide generative AI models (Bedrock), tempting candidates to pick Polly (speech output) or Rekognition (vision) as part of a chatbot, when they are not core to NLU or FM integration.
Detailed technical explanation
Under the hood, Amazon Lex uses a slot-filling and intent classification engine based on automatic speech recognition (ASR) and NLU, but it lacks the generative capabilities of large language models. By integrating with Amazon Bedrock, Lex can invoke an FM (e.g., Anthropic Claude or Amazon Titan) via the AWS SDK, allowing the chatbot to handle open-ended queries, generate context-aware responses, and perform complex reasoning beyond predefined intents. This pattern is commonly used in enterprise chatbots for customer support or knowledge retrieval, where Lex handles the dialog state and Bedrock provides the generative AI layer.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
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FAQ
Applications of Foundation Models — This question tests Applications of Foundation Models — Read the scenario before looking for a memorised answer..
The correct answer is: Amazon Lex — Amazon Lex provides the conversational interface and natural language understanding (NLU) to interpret user intents and manage dialog, while Amazon Bedrock gives access to foundation models (FMs) for advanced natural language generation and understanding. Together, Lex can route utterances to a Bedrock FM via a Lambda function or direct integration, enabling a chatbot that leverages a pre-trained FM for richer responses.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
This AIF-C01 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 AIF-C01 exam.
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