Free AIF-C01: AWS Certified AI Practitioner Exam Questions and Answers
95 verified practice questions for AIF-C01.
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Last updated: September 19, 2026
- Exam code
- AIF-C01
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- Amazon
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Question #1
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to know how much information can fit into one prompt. Which consideration will inform the company's decision?
Please select an optionIncorrectCorrect answer: B
The context window defines the maximum number of tokens a model can accept in a single prompt, so it determines how much information fits in one prompt. Temperature, batch size and model size do not set that limit.
Was this answer correct?Question #2
A company is building a large language model (LLM) question answering chatbot. The company wants to decrease the number of actions call center employees need to take to respond to customer questions. Which business objective should the company use to evaluate the effect of the LLM chatbot?
Please select an optionIncorrectCorrect answer: B
Average call duration directly measures whether agents need fewer actions and less time to resolve customer questions, so it is the right business metric for the chatbot's effect.
Was this answer correct?Question #3
A law firm wants to build an AI application by using large language models (LLMs). The application will read legal documents and extract key points from the documents. Which solution meets these requirements?
Please select an optionIncorrectCorrect answer: C
A summarization chatbot is ideal for extracting key points from legal documents. Large language models (LLMs) can be used to summarize complex texts, such as legal documents, making them more accessible and understandable. • Option C (Correct): "Develop a summarization chatbot": This is the correct answer because a summarization chatbot uses LLMs to condense and extract key information from text, which is precisely the requirement for reading and summarizing legal documents. • Option A: "Build an automatic named entity recognition system" is incorrect because it focuses on identifying specific entities, not summarizing documents. • Option B: "Create a recommendation engine" is incorrect as it is used to suggest products or content, not summarize text. • Option D: "Develop a multi-language translation system" is incorrect because translation is unrelated to summarizing text. AWS AI Practitioner References: • Using LLMs for Text Summarization on AWS: AWS supports developing summarization tools using its AI services, including Amazon Bedrock.
Was this answer correct?Question #4
A company has developed an ML model for image classification. The company wants to deploy the model to production so that a web application can use the model. The company needs to implement a solution to host the model and serve predictions without managing any of the underlying infrastructure. Which solution will meet these requirements?
Please select an optionIncorrectCorrect answer: A
SageMaker Serverless Inference hosts the model and serves predictions while AWS provisions and scales the compute automatically, so no infrastructure is managed. CloudFront, API Gateway and AWS Batch do not host ML models for inference.
Was this answer correct?Question #5
A company has documents that are missing some words because of a database error. The company wants to build an ML model that can suggest potential words to fill in the missing text. Which type of model meets this requirement?
Please select an optionIncorrectCorrect answer: D
BERT-based models are trained with a masked language modeling objective and use bidirectional context to predict missing words, which is exactly the fill-in-the-blank task described.
Was this answer correct?Question #6
A medical company deployed a disease detection model on Amazon Bedrock. To comply with privacy policies, the company wants to prevent the model from including personal patient information in its responses. The company also wants to receive notification when policy violations occur. Which solution meets these requirements?
Please select an optionIncorrectCorrect answer: C
Guardrails for Amazon Bedrock provide mechanisms to filter and control the content generated by models to comply with privacy and policy requirements. Using guardrails ensures that sensitive or personal information is not included in the model's responses. Additionally, integrating Amazon CloudWatch alarms allows for real-time notification when a policy violation occurs. • Option C (Correct): "Use Guardrails for Amazon Bedrock to filter content. Set up Amazon CloudWatch alarms for notification of policy violations": This is the correct answer because it directly addresses both the prevention of policy violations and the requirement to receive notifications when such violations occur. • Option A: "Use Amazon Macie to scan the model's output for sensitive data" is incorrect because Amazon Macie is designed to monitor data in S3, not to filter real-time model outputs. • Option B: "Configure AWS CloudTrail to monitor the model's responses" is incorrect because CloudTrail tracks API activity and is not suited for content moderation. • Option D: "Implement Amazon SageMaker Model Monitor to detect data drift" is incorrect because data drift detection does not address content moderation or privacy compliance. AWS AI Practitioner References: • Guardrails in Amazon Bedrock: AWS provides guardrails to ensure AI models comply with content policies, and using CloudWatch for alerting integrates monitoring capabilities.
Was this answer correct?Question #7
How can companies use large language models (LLMs) securely on Amazon Bedrock?
Please select an optionIncorrectCorrect answer: A
To securely use large language models (LLMs) on Amazon Bedrock, companies should design clear and specific prompts to avoid unintended outputs and ensure proper configuration of AWS Identity and Access Management (IAM) roles and policies with the principle of least privilege. This approach limits access to sensitive resources and minimizes the potential impact of security incidents. • Option A (Correct): "Design clear and specific prompts. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access": This is the correct answer as it directly addresses both security practices in prompt design and access management. • Option B: "Enable AWS Audit Manager for automatic model evaluation jobs" is incorrect because Audit Manager is for compliance and auditing, not directly related to secure LLM usage. • Option C: "Enable Amazon Bedrock automatic model evaluation jobs" is incorrect because Bedrock does not provide automatic model evaluation jobs specifically for security purposes. • Option D: "Use Amazon CloudWatch Logs to make models explainable and to monitor for bias" is incorrect because CloudWatch Logs are used for monitoring and not directly for making models explainable or secure. AWS AI Practitioner References: • Secure AI Practices on AWS: AWS recommends configuring IAM roles and using least privilege access to ensure secure usage of AI models.
Was this answer correct?Question #8
A company wants to classify human genes into 20 categories based on gene characteristics. The company needs an ML algorithm to document how the inner mechanism of the model affects the output. Which ML algorithm meets these requirements?
Please select an optionIncorrectCorrect answer: A
Decision trees are an interpretable machine learning algorithm that clearly documents the decision-making process by showing how each input feature affects the output. This transparency is particularly useful when explaining how the model arrives at a certain decision, making it suitable for classifying genes into categories. • Option A (Correct): "Decision trees": This is the correct answer because decision trees provide a clear and interpretable representation of how input features influence the model's output, making it ideal for understanding the inner mechanisms affecting predictions. • Option B: "Linear regression" is incorrect because it is used for regression tasks, not classification. • Option C: "Logistic regression" is incorrect as it does not provide the same level of interpretability in documenting decision-making processes. • Option D: "Neural networks" is incorrect because they are often considered "black boxes" and do not easily explain how they arrive at their outputs. AWS AI Practitioner References: • Interpretable Machine Learning Models on AWS: AWS supports using interpretable models, such as decision trees, for tasks that require clear documentation of how input data affects output decisions.
Was this answer correct?Question #9
A company wants to use large language models (LLMs) with Amazon Bedrock to develop a chat interface for the company's product manuals. The manuals are stored as PDF files. Which solution meets these requirements MOST cost-effectively?
Please select an optionIncorrectCorrect answer: D
An Amazon Bedrock knowledge base indexes the PDF manuals and retrieves only the relevant passages for each prompt (RAG), minimizing tokens per request without fine-tuning costs. Adding whole PDFs to every prompt or fine-tuning a model is more expensive.
Was this answer correct?Question #10
A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality. Which action must the company take to use the custom model through Amazon Bedrock?
Please select an optionIncorrectCorrect answer: A
Amazon Bedrock custom models cannot be used on demand; the company must purchase Provisioned Throughput for the custom model before invoking it. SageMaker endpoints and Model Registry are outside Bedrock, and access grants apply to base models.
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Other Amazon certifications
- AWS Certified Cloud Practitioner CLF-C02 (opens in a new tab)
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- AWS Certified Security - Specialty (opens in a new tab)
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