KodeKloud

AWS AI Practitioner (AIF-C01): Exam Walkthrough & Mock Exam

KodeKloud

AWS AI Practitioner (AIF-C01): Exam Walkthrough & Mock Exam

Mumshad Mannambeth

Instructor: Mumshad Mannambeth

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

5 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

5 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Apply exam reasoning to AWS AI scenarios covering Amazon Bedrock, RAG, PII redaction, and the AI shared responsibility model.

  • Select appropriate performance metrics (Recall, Precision) and apply SageMaker Ground Truth for production ML lifecycle questions.

  • Evaluate and compare Foundation Models by cost, latency, and accuracy to make sound architectural decisions on AWS.

  • Determine when to use RAG vs. fine-tuning and implement IDP workflows with Amazon Textract in exam-style scenarios.

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Recently updated!

July 2026

Assessments

7 assignments¹

AI Graded see disclaimer
Taught in English

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There are 3 modules in this course

This module introduces the core concepts of Generative AI and the architectural patterns used to deploy them on AWS. You will explore how Amazon Bedrock serves as a foundation for building secure applications, how to protect sensitive data with PII redaction, and how to mitigate common AI risks like hallucinations using Retrieval-Augmented Generation (RAG).

What's included

4 videos2 assignments

Moving beyond basic implementation, this module focuses on the "Care and Feeding" of machine learning models in a production environment. You will learn to navigate the ML lifecycle by labeling data at scale, monitoring for performance drift, and tuning hyperparameters like temperature to ensure consistent, high-quality model outputs.

What's included

2 videos2 assignments

The final module prepares you to make high-level architectural decisions by comparing different AI strategies and tools. You will learn the technical criteria for choosing between RAG and Fine-tuning, how to conduct side-by-side model evaluations on Bedrock, and how to automate complex data extraction from structured and unstructured documents.

What's included

2 videos2 readings3 assignments

Instructor

Mumshad Mannambeth
KodeKloud
39 Courses40,930 learners

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¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.