Validate your understanding of artificial intelligence, machine learning, and generative AI concepts on AWS. This foundational exam covers AI/ML fundamentals, Amazon Bedrock, SageMaker, responsible AI, and AI security governance.
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Model evaluation metrics, prompt engineering patterns, Amazon Bedrock, SageMaker AI, responsible AI guidelines, and every in-scope AWS service for the AIF-C01 exam
The AWS Certified AI Practitioner (AIF-C01) is AWS's foundational AI/ML certification, launched in 2024. It validates your understanding of artificial intelligence, machine learning, and generative AI concepts within the AWS ecosystem, including services like Amazon Bedrock, SageMaker, and Rekognition.
This certification is aimed at anyone who wants to demonstrate foundational AI/ML knowledge in an AWS context. It's ideal for business analysts, project managers, and early-career technologists who work alongside AI/ML teams. You don't need hands-on coding experience — conceptual understanding is what matters here.
The AIF-C01 exam has 65 questions (50 scored, 15 unscored) in multiple-choice and multiple-response format. You have 90 minutes, and the passing score is 700/1000 (approximately 70%). Available at Pearson VUE and online. The cost is $100 USD.
Recommended experience: 6 months of exposure to AI/ML concepts and AWS AI services. Hands-on experience is helpful but not required.
Written in the style of the real exam. Answer each one, then open the answer to check.
Question 1 · Applications of Foundation Models
A company is building a generative AI assistant that answers questions from its internal engineering manuals. The company converts the manuals into vector embeddings and needs a place to store the embeddings so the assistant can run similarity searches against them. Which AWS services can serve as the vector store for this solution? (Select TWO.)
Answers: C, E
Options C and E are correct because Amazon OpenSearch Service (including OpenSearch Serverless vector search collections) and Amazon Aurora PostgreSQL with the pgvector extension can both store embeddings and run similarity searches, and both are supported vector stores for Amazon Bedrock Knowledge Bases. Option A runs SQL queries over data in Amazon S3 but is not a vector database. Option B ingests real-time streaming data and does not index vectors for search. Option D is a natural language processing service that extracts insights such as entities and sentiment from text; it does not store embeddings.
Sources: AWS documentation 1, AWS documentation 2
Question 2 · Fundamentals of GenAI
A company uses a text generation model in Amazon Bedrock with on-demand pricing to summarize customer emails. The finance team wants to forecast the monthly cost of the application. Which factor MOST directly determines the cost of each request?
Answer: D
Option D is correct because Amazon Bedrock on-demand pricing for text models charges per input token processed and per output token generated, so longer emails and longer summaries cost more. Option A describes instance-hour billing for a dedicated endpoint, which on-demand Bedrock inference does not use. Option B is wrong because IAM users and permissions carry no charge. Option C is wrong because the provider's pre-training cost is not billed to the customer per request.
Sources: AWS documentation
Question 3 · Fundamentals of AI and ML
A bank deployed a fraud detection model to an Amazon SageMaker AI endpoint. Several months later, the model's accuracy has dropped because customer spending patterns have changed since the model was trained. The bank wants to be alerted automatically when production data starts to differ from the training data. Which solution meets this requirement?
Answer: A
Option A is correct because SageMaker Model Monitor compares live endpoint data against a baseline created from the training data, detects data quality and model quality drift, and can raise alerts through Amazon CloudWatch. Option B creates labeled training datasets with human labelers, which does not watch a deployed model. Option C prepares and transforms data before training but does not monitor production traffic. Option D stores and shares features for training and inference, but it does not detect drift.
Sources: AWS documentation 1, AWS documentation 2
These sample questions are not part of the AIF-C01 practice exam, so you won't meet them again when you take it.
This practice exam has 65 questions, and exam mode gives you 90 minutes to answer them. The real AIF-C01 exam has 65 questions (50 scored and 15 unscored) in 90 minutes, so a full attempt here is the same length. Practice mode has no timer.
In format, yes: questions are multiple choice (one correct answer) or multiple response (two or more), spread across the 5 exam domains in close to the weights the official exam guide gives them. Scoring differs: AWS reports a scaled score from 100 to 1,000 and you pass at 700, while this practice exam shows the raw percentage you got right. For how hard the exam is, whether it is worth it and how long to study, read our AWS AI Practitioner certification guide.
Aim for 80% or more, consistently, on full-length attempts in exam mode, not just once. That leaves a margin over the 700 out of 1,000 you need on the real AIF-C01 exam, whose questions will all be new to you. Repeat attempts here show the same questions, so judge yourself by first attempts and by whether you can explain each answer. No practice score guarantees a pass.
No. The AIF-C01 practice exam is free and needs no account. Your progress and score history are saved in this browser, so they stay on this device and are lost if you clear your browser data.
Start in practice mode, which tells you after each question whether you were right and explains the answer. When you score well there, switch to exam mode: 90 minutes and no feedback until the end, like test day. Your results break the score down by domain, so you can see which domains to study before your next attempt.
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