Applied AI cert-prep add-on

AWS Certified AI Practitioner Exam Prep Add-On

Convert AI Engineering Mastery's foundation modules into an AWS AI Practitioner ramp for the AWS-aligned AI entry credential.

$97 add-on30h additional studyTarget exam: AWS Certified AI PractitionerExam fee: $100

What the add-on ships

++Five domain reviews mapped to the official AWS Certified AI Practitioner exam guide
++Three full-length 65-question mock exams in AWS's foundation format
++AWS AI service catalog walkthrough: Bedrock, SageMaker, Comprehend, Rekognition, Transcribe, Polly, Lex, Personalize, Forecast
++Responsible AI on AWS: bias, fairness, and explainability in the Bedrock and SageMaker context
++Direct link to the official AWS exam guide as the canonical reference

Built on the parent course

++AI Engineering Mastery foundation modules covering ML fundamentals, AI lifecycle, and generative AI taxonomy
++Vocabulary and concept practice that compounds across all AWS AI services

Parent course: ai engineering mastery

Best for

++Career changers entering Applied AI from adjacent IT roles on AWS
++Product managers and solutions consultants who need vendor-agnostic AI literacy on AWS
++Practitioners evaluating the AWS ML certification track who want to validate readiness for the Associate or Specialty tiers

Practice surface

++Three AWS-format mock exams in the AI Practitioner style
++AWS AI service identification worked examples
++Targeted weak-area question drilling per AWS AI Practitioner exam objective

Buy the add-on

$97 on top of the ai engineering mastery parent course. Lifetime access to the practice materials, mock exams, and exam-day worksheets.

See the parent course

Exam summary

AWS Certified AI Practitioner (AIF-C01) is AWS's foundation-level AI credential. Covers AI / ML fundamentals, generative AI fundamentals, AWS AI and ML services, prompt engineering basics, and responsible AI on AWS. 65 questions in 90 minutes.

Domain reviews

AI / ML Fundamentals

Foundation concepts at AWS Practitioner depth.

++ML categories: supervised, unsupervised, reinforcement
++Common algorithm families and use cases
++Training-validation-test splits
++Bias-variance tradeoff at high level
++Common metrics: accuracy, precision, recall, F1, RMSE

Primary sources:

AWS Certified AI Practitioner exam guide

Generative AI Fundamentals

Foundation-model concepts at Practitioner depth.

++Foundation models, large language models, transformers (at a high level)
++Prompt engineering basics: zero-shot, few-shot, chain-of-thought
++Retrieval augmented generation (RAG) overview
++Fine-tuning vs prompt engineering vs RAG: when to use which

Primary sources:

AWS Bedrock developer guide

AWS AI / ML Services

Naming the right AWS service for the use case.

++Amazon Bedrock for foundation models and agents
++Amazon SageMaker for custom ML
++Pre-built AI: Comprehend, Rekognition, Transcribe, Polly, Lex, Personalize, Forecast, Textract, Translate
++Amazon Q for business applications

Primary sources:

AWS machine learning services

Responsible AI on AWS

Bias, fairness, explainability, content moderation.

++Bedrock Guardrails for content moderation
++SageMaker Clarify for bias detection and explainability
++AWS AI Service Cards
++AWS shared-responsibility model for AI

Primary sources:

AWS Bedrock Guardrails

Security, Compliance, and Governance

Foundation-level security and governance on AWS AI services.

++IAM for AI service access
++Data encryption in transit and at rest
++VPC endpoints for private connectivity
++Model governance with SageMaker Model Cards and Model Registry

Primary sources:

AWS AI security best practices

Practice scenarios (5)

Practice scenarios are scenario-based learning, not exam-question mimicry. Each scenario maps to a specific exam domain and includes a worked explanation plus a primary-source citation. Reproducing actual exam items would violate the cert body's NDA; the format here exercises the same underlying concepts under different surface phrasing.

Preview scenario 1 of 5 · Generative AI Fundamentalsfree preview

A customer-facing chatbot must answer questions from a company knowledge base, not from the foundation model's general training. Which technique is the canonical fit?

A. Train a 70B foundation model from scratch on the knowledge base
B. Retrieval Augmented Generation (RAG): retrieve relevant knowledge-base chunks and supply them as context to the foundation model
C. Hardcode every possible question and answer
D. Use a translation model

Answer: B

RAG is the canonical pattern for grounding an LLM in proprietary or up-to-date content. Training a 70B model from scratch is infeasible. Hardcoded questions do not scale. Translation is a different task.

Reference: AWS Bedrock RAG overview

Unlock the rest

4 more practice scenarios with full explanations and primary-source citations

The remaining scenarios cover every exam domain at the same depth as the preview above. Includes the exam-day strategy guide and additional study resources. $97 one-time, lifetime access.

Exam-day strategy

++Match each scenario to one named AWS AI service. The exam tests service-recognition more than deep technical detail.
++Watch for distractors that confuse Comprehend (text classification, NER) with Textract (document forms), or Bedrock Knowledge Bases (RAG) with Bedrock Agents (orchestration).
++Imbalanced-data accuracy traps appear in multiple questions. Always check whether the question implies imbalanced classes.
++Memorize the canonical use cases for each Bedrock feature (Guardrails, Agents, Knowledge Bases, Model Evaluation).
++Pace at roughly 80 seconds per question. 90 minutes for 65 questions is moderate.

Additional resources

Sources

Official AWS Certified AI Practitioner exam page (certifying body)

Exam fee and blueprint last verified 2026-05-22. Confirm current values with the certifying body before scheduling the exam.

What the add-on ships

++Five domain reviews mapped to the official AWS Certified AI Practitioner exam guide

++Three full-length 65-question mock exams in AWS's foundation format

++AWS AI service catalog walkthrough: Bedrock, SageMaker, Comprehend, Rekognition, Transcribe, Polly, Lex, Personalize, Forecast

++Responsible AI on AWS: bias, fairness, and explainability in the Bedrock and SageMaker context

++Direct link to the official AWS exam guide as the canonical reference

Domain reviews

AI / ML Fundamentals

Foundation concepts at AWS Practitioner depth.

++ML categories: supervised, unsupervised, reinforcement
++Common algorithm families and use cases
++Training-validation-test splits
++Bias-variance tradeoff at high level
++Common metrics: accuracy, precision, recall, F1, RMSE

Primary sources:

AWS Certified AI Practitioner exam guide

Generative AI Fundamentals

Foundation-model concepts at Practitioner depth.

++Foundation models, large language models, transformers (at a high level)
++Prompt engineering basics: zero-shot, few-shot, chain-of-thought
++Retrieval augmented generation (RAG) overview
++Fine-tuning vs prompt engineering vs RAG: when to use which

Primary sources:

AWS Bedrock developer guide

AWS AI / ML Services

Naming the right AWS service for the use case.

++Amazon Bedrock for foundation models and agents
++Amazon SageMaker for custom ML
++Pre-built AI: Comprehend, Rekognition, Transcribe, Polly, Lex, Personalize, Forecast, Textract, Translate
++Amazon Q for business applications

Primary sources:

AWS machine learning services

Responsible AI on AWS

Bias, fairness, explainability, content moderation.

++Bedrock Guardrails for content moderation
++SageMaker Clarify for bias detection and explainability
++AWS AI Service Cards
++AWS shared-responsibility model for AI

Primary sources:

AWS Bedrock Guardrails

Security, Compliance, and Governance

Foundation-level security and governance on AWS AI services.

++IAM for AI service access
++Data encryption in transit and at rest
++VPC endpoints for private connectivity
++Model governance with SageMaker Model Cards and Model Registry

Primary sources:

AWS AI security best practices

Practice scenarios (5)

Preview scenario 1 of 5 · Generative AI Fundamentalsfree preview

A customer-facing chatbot must answer questions from a company knowledge base, not from the foundation model's general training. Which technique is the canonical fit?

A. Train a 70B foundation model from scratch on the knowledge base
B. Retrieval Augmented Generation (RAG): retrieve relevant knowledge-base chunks and supply them as context to the foundation model
C. Hardcode every possible question and answer
D. Use a translation model

Answer: B

Reference: AWS Bedrock RAG overview

Unlock the rest

4 more practice scenarios with full explanations and primary-source citations

The remaining scenarios cover every exam domain at the same depth as the preview above. Includes the exam-day strategy guide and additional study resources. $97 one-time, lifetime access.

Exam-day strategy

++Match each scenario to one named AWS AI service. The exam tests service-recognition more than deep technical detail.

++Watch for distractors that confuse Comprehend (text classification, NER) with Textract (document forms), or Bedrock Knowledge Bases (RAG) with Bedrock Agents (orchestration).

++Imbalanced-data accuracy traps appear in multiple questions. Always check whether the question implies imbalanced classes.

++Memorize the canonical use cases for each Bedrock feature (Guardrails, Agents, Knowledge Bases, Model Evaluation).

++Pace at roughly 80 seconds per question. 90 minutes for 65 questions is moderate.