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aws AIF C01
aws AIF C01
Official doc
Official doc
Domain review transcripts
Domain review transcripts
Domain 1: Fundamentals of AI and ML
Task Statement 1.1: Explain basic AI concepts and terminologies
Task Statement 1.2: Identify practical use cases for AI
Task Statement 1.3: Describe the ML development lifecycle
Task Statement 2.1: Explain the basic concepts of generative AI
Task Statement 2.2: Understand the capabilities & limits of GenAI for solving business problems
Task Statement 2.3: Describe AWS infrastructure & technologies for building GenAI applications
Task Statement 3.1: Describe design considerations for applications that use foundation models
Task Statement 3.2: Choose effective prompt engineering techniques
Task Statement 3.3: Describe the training and fine-tuning process for foundation models
Task Statement 3.4: Describe methods to evaluate foundation model performance
Task Statement 4.1: Explain the development of AI systems that are responsible
Task Statement 4.2: Recognize the importance of transparent and explainable models
Task Statement 5.1: Explain methods to secure AI systems
Task Statement 5.2: Recognize governance and compliance regulations for AI systems
Exam syllabus
Exam syllabus
Content Domain 1: Fundamentals of AI and ML
Content Domain 2: Fundamentals of GenAI
Content Domain 3: Applications of Foundation Models
Content Domain 4: Guidelines for Responsible AI
Content Domain 5: Security, Compliance, and Governance for AI Solutions
AWS Certified AI Practitioner (AIF-C01)
In-Scope AWS Services
Out-of-Scope AWS Services
Official questions
Official questions
Sample questions
Study notes
Study notes
Cheatsheets
Cheatsheets
AWS Certified AI Practitioner (AIF-C01) Study Notes
Analytics
AWS Certified AI Practitioner (AIF-C01) Master Exam Cheat Sheet
AWS Certified AI Practitioner (AIF-C01) — 6-Layer AI System Architecture
Notes by objectives
Notes by objectives
Task Statement 1.1: Explain Basic AI Concepts and Terminologies
Task Statement 1.2: Identify Practical Use Cases for AI
Task Statement 1.3: Describe the AI/ML Development Lifecycle
Task Statement 2.1: Explain the Basic Concepts of Generative AI (GenAI)
Task Statement 2.2: Understand the Capabilities and Limitations of GenAI for Solving Business Problems
Task Statement 2.3: Describe AWS Infrastructure and Technologies for Building GenAI Applications
AIF-C01 Task 3.1: Design Considerations for Applications That Use Foundation Models
AIF-C01 Task 3.2: Effective Prompt Engineering Techniques
AIF-C01 Task 3.3: Training and Fine-Tuning Foundation Models
AIF-C01 Task 3.4: Methods to Evaluate Foundation Model Performance
AIF-C01 Content Domain 4: Guidelines for Responsible AI
AIF-C01 Study Notes: Transparent and Explainable Models
AIF-C01 Study Notes: Task Statement 5.1 — Explain Methods to Secure AI Systems
AIF-C01 Task 5.2: Governance and Compliance Regulations for AI Systems
Notes by objectives official scripts
Notes by objectives official scripts
Domain 1: Fundamentals of AI and ML
AWS Certified AI Practitioner — Task Statement 1.1 Study Notes
AWS Certified AI Practitioner — Task Statement 1.2 Study Notes
AWS Certified AI Practitioner — Task Statement 1.3 Study Notes
AWS AI Practitioner Exam Notes
AWS AI Practitioner Exam Notes
Task Statement 2.3 Study Notes
AWS AI Practitioner Exam Notes
AWS AI Practitioner Exam Notes
AWS AI Practitioner Exam Notes
AWS AI Practitioner Exam Notes
AWS Certified AI Practitioner – Task Statement 4.1 Study Notes
AWS AI Practitioner Exam Notes
AWS AI Practitioner Exam Notes
AWS AI Practitioner Exam Notes
Notes by topics
Notes by topics
Amazon SageMaker — AIF-C01 Exam Preparation Notes
Foundation Model Fine-Tuning: Supervised (SFT) vs. Reinforcement Fine-Tuning (RFT)
Foundation Model Automated Evaluation Metrics (AWS AIF-C01)
Amazon Nova Model Family Overview
Prompt Engineering & Performance Optimization (AIF-C01)
Amazon Q & AWS Generative AI Ecosystem (AIF-C01)
Machine Learning & Generative AI Model Architectures (AIF-C01)
Reinforcement Learning from Human Feedback (RLHF) & Alignment (AIF-C01)
Model Performance, Metrics & Evaluation (AIF-C01)
Complete AWS Machine Learning & GenAI Guide (AIF-C01)
aws CLF C02
aws CLF C02
Official doc
Official doc
Domain review transcripts
Domain review transcripts
Content Domain 1: Cloud Concepts
Content Domain 1: Cloud Concepts
Content Domain 1: Cloud Concepts
Content Domain 1: Cloud Concepts
Content Domain 1: Cloud Concepts
Content Domain 2: Security and Compliance
Content Domain 2: Security and Compliance
Content Domain 2: Security and Compliance
Content Domain 2: Security and Compliance
Content Domain 3: Cloud Technology and Services
Content Domain 3: Cloud Technology and Services
Content Domain 3: Cloud Technology and Services
Content Domain 3: Cloud Technology and Services
Content Domain 3: Cloud Technology and Services
Content Domain 3: Cloud Technology and Services
Content Domain 3: Cloud Technology and Services
Content Domain 3: Cloud Technology and Services
Content Domain 4: Billing, Pricing, and Support
Content Domain 4: Billing, Pricing, and Support
Content Domain 4: Billing, Pricing, and Support
Exam syllabus
Exam syllabus
In-Scope AWS Services
Out-of-Scope AWS Services
Technologies and Concepts
Content Domain 1: Cloud Concepts
Content Domain 2: Security and Compliance
Content Domain 3: Cloud Technology and Services
Content Domain 4: Billing, Pricing, and Support
AWS Certified Cloud Practitioner (CLF-C02)
Study notes
Study notes
Exam questions
Exam questions
1.1
1.2
1.3
1.4
2.1
2.2
2.3
2.4
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
4.1
4.2
4.3
Exam questions grok
Exam questions grok
CLF-C02 Exam Notes — Task Statement 1.1
CLF-C02 Exam Notes — Task Statement 1.2
1.3
AWS CLF-C02 Exam Notes – Task Statement 1.4
AWS CLF-C02 Exam Notes
AWS CLF-C02 Exam Notes
2.3
AWS Certified Cloud Practitioner (CLF-C02) Exam Notes
AWS CLF-C02 Exam Questions
3.2
3.3
3.4
3.5
3.6
3.7
3.8
4.1
4.2
4.3
Notes by domain review transcripts
Notes by domain review transcripts
AWS Certified Cloud Practitioner (CLF-C02) Study Notes
CLF-C02 Study Notes: Task Statement 1.1 - Define the Benefits of the AWS Cloud
CLF-C02 Study Notes: AWS Cloud Design Principles
CLF-C02 Study Notes: Migration to AWS Cloud
CLF-C02 Study Notes: Cloud Economics
CLF-C02 Study Notes: AWS Shared Responsibility Model
CLF-C02 Study Notes: Task Statement 2.2
CLF-C02 Study Notes: Identify AWS Access Management Capabilities
CLF-C02 Study Notes: Task Statement 2.4
CLF-C02 Study Notes: Task Statement 3.1
CLF-C02 Study Notes: Define the AWS Global Infrastructure
CLF-C02 Study Notes: Identify AWS Compute Services
CLF-C02 Study Notes: Task Statement 3.4 — Identify AWS Database Services
CLF-C02 Study Notes: Task Statement 3.5 — Identify AWS Network Services
CLF-C02 Study Notes: Task Statement 3.6 — Identify AWS Storage Services
CLF-C02 Study Notes: AWS AI/ML and Analytics Services
CLF-C02 Study Notes: Task Statement 3.8 — Identify Other AWS Services
CLF-C02 Study Notes: Task Statement 4.1 - Compare AWS Pricing Models
CLF-C02 Study Notes: Task Statement 4.2 - Understand resources for billing, budget, and cost management
CLF-C02 Study Notes: AWS Technical Resources and Support Options
Short notes by domain transcripts
Short notes by domain transcripts
Grok
AWS Certified Cloud Practitioner (CLF-C02) Complete Study Notes
Special topics
Special topics
AWS Pricing Model Decision Guide
AWS Site-to-Site VPN vs. AWS Direct Connect (Exam & Architectural Guide)
AWS Exam Guide: EC2 Self-Hosted vs. Amazon RDS vs. Amazon Aurora (MySQL)
AWS Well-Architected Framework: 6 Pillars Cheatsheet & Exam Guide
AWS Cloud Migration: CAF, 7 Rs & Migration Services Cheatsheet
AWS Security, Governance & Compliance Cheatsheet (Task Statement 2.2)
AWS Security Components & Resources Cheatsheet (Task Statement 2.4)
aws MLA C02
aws MLA C02
Official doc
Official doc
Domain review assignments
Domain review assignments
Bonus Assignment: Prepare a support ticket dataset
Bonus Assignment: Choose and defend a modeling approach
Bonus Assignment: Deploy and automate one small model
Bonus Assignment: Take an assistant into operations
Domain review notes
Domain review notes
Domain 1 overview
Task 1.1 - Collect and store data
Task 1.2 - Perform data transformation, feature engineering, and pre-processing
Task 1.3 - Validate data quality and manage bias
Domain 2 Review
Task 2.1 - Choose appropriate modeling approaches for ML and AI solutions
Task 2.2 - Train, fine-tune, and customize models for ML and AI solutions
Task 2.3 - Analyze and evaluate the performance of ML and AI systems
Domain 3 Review
Task 3.1 - Manage deployment infrastructure for ML and AI model types
Task 3.2 - Provision and configure resources for ML and AI workloads
Task 3.3 - Implement automated orchestration and continuous integration and continuous delivery (CI/CD) pipelines
Domain 4 Review
Task 4.1 - Monitor ML and AI model inference and performance
Task 4.2 - Optimize and manage ML and AI infrastructure costs and performance
Task 4.3 - Secure ML and AI workloads and model endpoints
Exam syllabus
Exam syllabus
AWS Certified Machine Learning Engineer - Associate (MLA-C02)
Content Domain 1: Data Preparation for ML and AI
Content Domain 2: ML Model and Foundation Model (FM) Development
Content Domain 3: Deployment and Orchestration of ML and AI Workflows
Content Domain 4: Operating, Monitoring, and Securing ML and AI Solutions
Comparison of MLA-C01 and MLA-C02
In-scope AWS services and features
Out-of-scope AWS services and features
Mentions of AWS services on the exam
Study notes
Study notes
MLA-C02 Final Cheatsheet
Keyword matching
Keyword matching
Domain 1: Data Preparation for ML and AI
Domain 2: ML Model and Foundation Model (FM) Development
Domain 3: Deployment and Orchestration of ML and AI Workflows
Domain 4: Operating, Monitoring, and Securing ML and AI Solutions
Notes by skills
Notes by skills
Task 1.1: Data Preparation for ML and AI - Collect and store data
Task 1.1: Data Preparation for ML and AI - Collect and store data
Task 1.1: Data Preparation for ML and AI - Collect and store data
Task 1.1: Data Preparation for ML and AI - Collect and store data
Task 1.1: Data Preparation for ML and AI - Collect and store data.
Task 1.1: Data Preparation for ML and AI - Collect and store data
Task 1.1: Data Preparation for ML and AI - Collect and store data.
Task 1.1: Data Preparation for ML and AI - Collect and store data.
Task 1.1: Data Preparation for ML and AI - Collect and store data
Task 1.2: Data Preparation for ML and AI - Perform data transformation, feature engineering, and pre-processing
Task 1.2: Data Preparation for ML and AI - Perform data transformation, feature engineering, and pre-processing
Task 1.2: Data Preparation for ML and AI - Perform data transformation, feature engineering, and pre-processing
Task 1.2: Data Preparation for ML and AI - Perform data transformation, feature engineering, and pre-processing
Task 1.2: Data Preparation for ML and AI - Perform data transformation, feature engineering, and pre-processing.
Task 1.2: Data Preparation for ML and AI - Perform data transformation, feature engineering, and pre-processing
Task 1.2: Data Preparation for ML and AI - Perform Data Transformation, Feature Engineering, and Pre-processing
Task 1.2: Data Preparation for ML and AI - Perform data transformation, feature engineering, and pre-processing
Task 1.2: Data Preparation for ML and AI - Perform data transformation, feature engineering, and pre-processing
Task 1.3: Data Preparation for ML and AI - Validate data quality and manage bias
Task 1.3: Data Preparation for ML and AI - Validate data quality and manage bias.
Task 1.3: Data Preparation for ML and AI - Validate data quality and manage bias
Task 1.3: Data Preparation for ML and AI - Validate data quality and manage bias
Task 1.3: Data Preparation for ML and AI - Validate data quality and manage bias
Task 1.3: Data Preparation for ML and AI - Validate data quality and manage bias
Task 1.3: Data Preparation for ML and AI - Validate data quality and manage bias
Task 2.1: ML Model and Foundation Model (FM) Development - Choose Appropriate Modeling Approaches for ML and AI Solutions
Task 2.1: ML Model and Foundation Model (FM) Development - Choose appropriate modeling approaches for ML and AI solutions.
Task 2.1: ML Model and Foundation Model (FM) Development - Choose appropriate modeling approaches for ML and AI solutions
Task 2.1: ML Model and Foundation Model (FM) Development - Choose appropriate modeling approaches for ML and AI solutions
Task 2.1: ML Model and Foundation Model (FM) Development - Choose appropriate modeling approaches for ML and AI solutions
Task 2.1: ML Model and Foundation Model (FM) Development - Choose appropriate modeling approaches for ML and AI solutions
Task 2.1: ML Model and Foundation Model (FM) Development - Choose appropriate modeling approaches for ML and AI solutions
Task 2.1: ML Model and Foundation Model (FM) Development - Choose appropriate modeling approaches for ML and AI solutions
Task 2.2: ML Model and Foundation Model (FM) Development - Train, fine-tune, and customize models for ML and AI solutions
Task 2.2: ML Model and Foundation Model (FM) Development - Train, fine-tune, and customize models for ML and AI solutions
Task 2.2: ML Model and Foundation Model (FM) Development - Train, fine-tune, and customize models for ML and AI solutions
Task 2.2: Train, fine-tune, and customize models for ML and AI solutions
Task 2.2: ML Model and Foundation Model (FM) Development - Train, fine-tune, and customize models for ML and AI solutions
Task 2.2: ML Model and Foundation Model (FM) Development - Train, fine-tune, and customize models for ML and AI solutions
Task 2.2: ML Model and Foundation Model (FM) Development - Train, fine-tune, and customize models for ML and AI solutions
Task 2.2: ML Model and Foundation Model (FM) Development - Train, fine-tune, and customize models for ML and AI solutions
Task 1.2: ML Model and Foundation Model (FM) Development - Train, fine-tune, and customize models for ML and AI solutions
Task 2.3: ML Model and Foundation Model (FM) Development - Analyze and evaluate the performance of ML and AI systems
Task 2.3: Analyze and evaluate the performance of ML and AI systems
Task 2.3: ML Model and Foundation Model (FM) Development - Analyze and evaluate the performance of ML and AI systems
Task 2.3: Analyze and evaluate the performance of ML and AI systems
Task 2.3: Analyze and evaluate the performance of ML and AI systems
Task 2.3: ML Model and Foundation Model (FM) Development - Analyze and evaluate the performance of ML and AI systems
Task 2.3: ML Model and Foundation Model (FM) Development - Analyze and evaluate the performance of ML and AI systems
Task 2.3: ML Model and Foundation Model (FM) Development - Analyze and evaluate the performance of ML and AI systems
Task 2.3: ML Model and Foundation Model (FM) Development - Analyze and evaluate the performance of ML and AI systems
Task 2.3: ML Model and Foundation Model (FM) Development - Analyze and evaluate the performance of ML and AI systems
Task 3.1: Deployment and Orchestration of ML and AI Workflows - Manage deployment infrastructure for ML and AI model types
Task 3.1: Deployment and Orchestration of ML and AI Workflows - Manage Deployment Infrastructure
Task 3.1: Deployment and Orchestration of ML and AI Workflows - Manage deployment infrastructure for ML and AI model types
Task 3.1: Deployment and Orchestration of ML and AI Workflows - Manage deployment infrastructure for ML and AI model types
Task 3.1: Deployment and Orchestration of ML and AI Workflows - Manage deployment infrastructure for ML and AI model types
Task 3.1: Deployment and Orchestration of ML and AI Workflows - Manage deployment infrastructure for ML and AI model types
Task 3.1: Deployment and Orchestration of ML and AI Workflows - Manage deployment infrastructure for ML and AI model types
Task 3.1: Deployment and Orchestration of ML and AI Workflows - Manage deployment infrastructure for ML and AI model types
Task 3.2: Deployment and Orchestration of ML and AI Workflows - Provision and configure resources for ML and AI workloads based on existing architecture and requirements.
Task 3.2: Provision and configure resources for ML and AI workloads based on existing architecture and requirements.
Task 3.2: Provision and configure resources for ML and AI workloads based on existing architecture and requirements
Task 3.2: Deployment and Orchestration of ML and AI Workflows - Provision and configure resources for ML and AI workloads based on existing architecture and requirements.
Task 3.2: Deployment and Orchestration of ML and AI Workflows - Provision and configure resources for ML and AI workloads based on existing architecture and requirements
Task 3.2: Deployment and Orchestration of ML and AI Workflows - Provision and configure resources for ML and AI workloads based on existing architecture and requirements
Task 3.2: Deployment and Orchestration of ML and AI Workflows - Provision and configure resources for ML and AI workloads based on existing architecture and requirements
Task 3.2: Deployment and Orchestration of ML and AI Workflows - Provision and configure resources for ML and AI workloads based on existing architecture and requirements.
Task 3.2: Deployment and Orchestration of ML and AI Workflows - Provision and configure resources for ML and AI workloads based on existing architecture and requirements.
Task 3.2: Deployment and Orchestration of ML and AI Workflows - Provision and configure resources for ML and AI workloads based on existing architecture and requirements.
Task 3.2: Provision and configure resources for ML and AI workloads based on existing architecture and requirements
Task 3.3: Deployment and Orchestration of ML and AI Workflows - Implement automated orchestration and continuous integration and continuous delivery (CI/CD) pipelines for MLOps and AI workloads
Task 3.3: Deployment and Orchestration of ML and AI Workflows - Implement automated orchestration and continuous integration and continuous delivery (CI/CD) pipelines for MLOps and AI workloads
Task 3.3: Deployment and Orchestration of ML and AI Workflows - Implement automated orchestration and continuous integration and continuous delivery (CI/CD) pipelines for MLOps and AI workloads
Task 3.3: Deployment and Orchestration of ML and AI Workflows - Implement automated orchestration and continuous integration and continuous delivery (CI/CD) pipelines for MLOps and AI workloads
Task 3.3: Deployment and Orchestration of ML and AI Workflows - Implement automated orchestration and continuous integration and continuous delivery (CI/CD) pipelines for MLOps and AI workloads
Task 3.3: Deployment and Orchestration of ML and AI Workflows - Implement automated orchestration and continuous integration and continuous delivery (CI/CD) pipelines for MLOps and AI workloads.
Task 3.3: Deployment and Orchestration of ML and AI Workflows - Implement automated orchestration and continuous integration and continuous delivery (CI/CD) pipelines for MLOps and AI workloads
Task 3.3: Deployment and Orchestration of ML and AI Workflows - Implement automated orchestration and continuous integration and continuous delivery (CI/CD) pipelines for MLOps and AI workloads
Task 3.3: Deployment and Orchestration of ML and AI Workflows - Implement Automated Orchestration and Continuous Integration and Continuous Delivery (CI/CD) Pipelines for MLOps and AI Workloads
Task 3.3: Deployment and Orchestration of ML and AI Workflows - Implement automated orchestration and continuous integration and continuous delivery (CI/CD) pipelines for MLOps and AI workloads
Task 3.3: Deployment and Orchestration of ML and AI Workflows - Implement automated orchestration and continuous integration and continuous delivery (CI/CD) pipelines for MLOps and AI workloads
Task 4.1: Operating, Monitoring, and Securing ML and AI Solutions - Monitor ML and AI model inference and performance
Task 4.1: Operating, Monitoring, and Securing ML and AI Solutions - Monitor ML and AI model inference and performance.
Task 4.1: Operating, Monitoring, and Securing ML and AI Solutions - Monitor ML and AI model inference and performance
Task 4.1: Operating, Monitoring, and Securing ML and AI Solutions - Monitor ML and AI model inference and performance
Task 4.1: Operating, Monitoring, and Securing ML and AI Solutions - Monitor ML and AI model inference and performance
Task 4.1: Operating, Monitoring, and Securing ML and AI Solutions - Monitor ML and AI model inference and performance
Task 4.2: Operating, Monitoring, and Securing ML and AI Solutions - Optimize and manage ML and AI infrastructure costs and performance
Task 4.2: Optimize and manage ML and AI infrastructure costs and performance
Task 4.2: Operating, Monitoring, and Securing ML and AI Solutions - Optimize and manage ML and AI infrastructure costs and performance
Task 4.2: Operating, Monitoring, and Securing ML and AI Solutions - Optimize and manage ML and AI infrastructure costs and performance
Task 4.2: Operating, Monitoring, and Securing ML and AI Solutions - Optimize and manage ML and AI infrastructure costs and performance
Task 4.2: Operating, Monitoring, and Securing ML and AI Solutions - Optimize and manage ML and AI infrastructure costs and performance
Task 4.2: Operating, Monitoring, and Securing ML and AI Solutions - Optimize and manage ML and AI infrastructure costs and performance
Task 4.2: Operating, Monitoring, and Securing ML and AI Solutions - Optimize and manage ML and AI infrastructure costs and performance
Task 4.2: Operating, Monitoring, and Securing ML and AI Solutions - Optimize and manage ML and AI infrastructure costs and performance.
Task 4.2: Operating, Monitoring, and Securing ML and AI Solutions - Optimize and manage ML and AI infrastructure costs and performance
Task 4.3: Operating, Monitoring, and Securing ML and AI Solutions - Secure ML and AI workloads and model endpoints.
Task 4.3: Operating, Monitoring, and Securing ML and AI Solutions - Secure ML and AI workloads and model endpoints
Task 4.3: Secure ML and AI workloads and model endpoints
Task 4.3: Operating, Monitoring, and Securing ML and AI Solutions - Secure ML and AI workloads and model endpoints
Task 4.3: Secure ML and AI workloads and model endpoints
Task 4.3: Operating, Monitoring, and Securing ML and AI Solutions - Secure ML and AI workloads and model endpoints
Task 4.3: Operating, Monitoring, and Securing ML and AI Solutions - Secure ML and AI workloads and model endpoints
Task 4.3: Operating, Monitoring, and Securing ML and AI Solutions - Secure ML and AI workloads and model endpoints
Task 4.3: Operating, Monitoring, and Securing ML and AI Solutions - Secure ML and AI workloads and model endpoints
Practice questions
Practice questions
AWS Certified Machine Learning Engineer - Associate (MLA-C02)
Udemy Full-Length Practice Exam (65 Questions)
Practice questions ds
Practice questions ds
Skill 1.1.1: Extract data from data sources
Skill 1.1.2: Make storage decisions and configure storage services based on cost, performance, data structure, and data compliance
Skill 1.1.3: Troubleshoot and debug data ingestion and storage issues that involve capacity and scalability.
Skill 1.1.4: Use AWS streaming data sources to ingest data (for example, by using Amazon Kinesis, Apache Flink, Apache Kafka)
Skill 1.1.1: Extract data from data sources
Skill 1.1.6: Merge data from multiple sources (for example, by using programming techniques, AWS Glue, Apache Spark)
Skill 1.1.1: Configure Scalable Vector Databases for AI Applications
Skill 1.1.1: Extract data from data sources
Skill 1.1.1: Extract data from data sources
Skill 1.2.1: Transform data by using AWS tools (for example, AWS Glue, AWS Glue DataBrew, Spark on Amazon EMR, SageMaker Data Wrangler)
Skill 1.1.1: Extract data from data sources
Skill 1.2.3: Transform streaming data (for example, by using AWS Lambda, Spark)
Skill 1.2.4: Perform feature engineering (for example, scaling, standardization, feature splitting, binning, log transformation, normalization)
Skill 1.2.5: Configure and use embedding models to transform text and image data into numerical representations
Skill 1.1.1: Extract data from data sources
Skill 1.1.1: Extract data from data sources
Skill 1.2.8: Mask, redact, and anonymize data
Skill 1.2.9: Prepare data for FM fine-tuning, continuous pre-training, and model distillation
Skill 1.3.1: Validate data quality (for example, by using DataBrew, AWS Glue Data Quality)
Skill 1.3.2: Label and annotate data
Skill 1.3: Validate data quality and manage bias
Skill 1.3: Validate data quality and manage bias
Skill 1.1.1: Extract data from data sources
Skill 1.1.1: Extract data from data sources
Skill 1.3: Validate data quality and manage bias
Skill 1.1.1: Extract data from data sources
Skill 2.1.2: Identify fine-tuning strategies for pre-trained FMs to meet business needs
Skill 1.1.1: Extract data from data sources
Skill 2.1.4: Evaluate tradeoffs between custom solutions, managed services, pre-trained models, and FMs to meet business needs
Skill 2.1.5: Select Retrieval Augmented Generation (RAG) architecture patterns based on use case requirements
Skill 1.1.1: Extract data from data sources
Skill 1.1.1: Extract data from data sources
Skill 1.1.1: Extract data from data sources
Skill 2.2.1: Apply Amazon SageMaker AI built-in algorithms and common ML libraries
Skill 2.2.2: Configure SageMaker AI script mode with supported frameworks for simplicity and performance
Skill 2.2.3: Implement hyperparameter optimization (for example, SageMaker AI automatic model tuning [AMT])
Skill 1.1.1: Extract data from data sources
Skill 2.2.5: Prevent model overfitting, underfitting, and catastrophic forgetting
Skill 2.2.6: Combine multiple ML models to improve performance or reduce cost
Skill 2.2.7: Adjust fundamental hyperparameters (for example, epoch, steps, batch size)
Skill 2.2.8: Apply customization techniques for AI solutions (for example, task-specific prompt engineering, fine-tuning)
Skill 2.2.9: Optimize retrieval components and embedding models
Skill 2.3.1: Perform reproducible experiments (MLflow on SageMaker AI, Bedrock evaluations, Bedrock Prompt Management)
Skill 2.3.10: Configure RAG system monitoring, including retrieval accuracy assessment
Skill 2.3.2: Create model performance baselines and implement drift detection
Skill 2.3.3: Compare the performance of shadow variants to production variants
Skill 2.3.4: Explain model outputs
Skill 2.3.5: Debug model convergence issues
Skill 1.1.1: Extract data from data sources
Skill 2.3.7: Implement integrated human evaluation frameworks (for example, human-in-the-loop workflows, text generation quality assessment)
Skill 1.1.1: Extract data from data sources
Skill 2.3.9: Perform AI evaluation (for example, model output assessment, content quality validation, bias detection, LLM-as-a-judge frameworks)
Skill 3.1.1: Select appropriate compute environments and deployment targets
Skill 3.1.2: Select deployment orchestrators and multi-model or multi-container deployment strategies
Skill 3.1.3: Select model inference strategies (for example, real-time and batch processing)
Skill 3.1.4: Evaluate and select appropriate foundation model (FM) deployment options
Skill 3.1.5: Deploy models that were built outside of AWS into AWS environments (for example, Amazon SageMaker AI, Amazon Bedrock Custom Model Import)
Skill 3.1.6: Deploy and configure agents for specific tasks, integration with other services and tools, and agent communication protocols
Skill 3.1.7: Configure FM deployment, model hosting, and resource allocation
Skill 3.1.8: Apply Retrieval Augmented Generation (RAG) System Configurations
Skill 3.2.1: Optimize resource provisioning between on-demand and provisioned resources for performance and cost efficiency
Skill 3.2.10: Implement AI-specific resource scaling for GPU workloads
Skill 3.2.11: Deploy agentic workflow infrastructure
Skill 3.2.2: Automate compute resource provisioning with integrated communication between stacks and orchestration services.
Skill 3.2.3: Build and maintain containers for ML and AI workloads
Skill 3.2.4: Configure SageMaker AI endpoints within VPC network environments
Skill 3.2.5: Deploy and host models programmatically (for example, by using the SageMaker AI SDK for Python, AWS CLI, Boto3)
Skill 3.2.6: Select specific metrics for auto scaling implementations
3.2.7
Skill 1.1.1: Extract data from data sources
Skill 1.1.1: Extract data from data sources
Skill 3.3.1: Implement automated deployment strategies and rollback actions
Skill 3.3.10: Configure FM deployment automation with fine-tuned model versioning
Skill 3.3.11: Configure AI-specific pipeline orchestration for RAG system updates and knowledge base refresh cycles
Skill 3.3.2: Configure and troubleshoot AWS CodeBuild, AWS CodeCommit, AWS CodeDeploy, AWS CodePipeline, and AWS CodeConnections
Skill 1.1.1: Extract data from data sources
Skill 1.1.1: Extract data from data sources
Skill 3.3.5: Build and integrate mechanisms to re-train models
Skill 3.3.6: Manage model versions for repeatability and audits (for example, SageMaker Model Registry, MLflow on SageMaker AI)
Skill 1.1.1: Extract data from data sources
Skill 1.1.1: Extract data from data sources
3.3.9
4.1.1
Skill 4.1.2: Monitor workflows to detect anomalies or errors in data processing or model inference
Skill 4.1.3: Detect changes in data distribution that can affect model performance
Skill 4.1.4: Monitor model performance in production by using A/B testing
Skill 4.1.5: Monitor and automate the management of agent performance and coordination
4.1.6
Skill 4.2.1: Select inference instance families to optimize performance and cost
Skill 4.2.10: Monitor AI-specific cost patterns
Skill 4.2.2: Configure and use tools to troubleshoot and analyze resources (for example, Amazon CloudWatch, Amazon Bedrock AgentCore Observability, AWS X-Ray)
Skill 4.2.3: Set up dashboards to monitor performance metrics.
Skill 4.2.4: Optimize capacity for cost, performance, and reliability
Skill 1.1.1: Extract data from data sources - Practice Questions
Skill 4.2.6: Optimize infrastructure costs by selecting purchasing options
Skill 4.2.7: Evaluate cost implications of using FMs for inference in production
Skill 4.2.8: Monitor agent resource consumption patterns
Skill 4.2.9: Manage FM inference costs with usage optimization
Skill 1.1.1: Extract data from data sources
Skill 1.1.1: Extract data from data sources
Skill 4.3.3: Configure IAM policies and roles for users and applications in ML and AI systems
Skill 1.1.1: Extract data from data sources
Skill 4.3.5: Troubleshoot and debug security issues in ML and AI systems
Skill 4.3.6: Create VPCs, subnets, and security groups to securely isolate ML and AI systems
Skill 4.3.7: Identify and mitigate security risks and vulnerabilities in ML and AI systems
Skill 1.1.1: Extract data from data sources
Skill 4.3.9: Implement safeguards and sensitive data protection to meet application requirements and responsible AI policies (for example, by using Amazon Bedrock Guardrails)
Practice questions tables
Practice questions tables
Skill 1.1.1: Extract data from data sources
1.1.2
Skill 1.1.3: Troubleshoot and debug data ingestion and storage issues that involve capacity and scalability
Skill 1.1.4: Use AWS streaming data sources to ingest data
Skill 1.1.5: Ingest from and write by using appropriate data formats (for example, Apache Parquet, JSON, CSV, ORC) based on data access patterns.
Skill 1.1.6: Merge data from multiple sources
1.1.7
Skill 1.1.8: Ingest and store diverse data types (for example, text, images, audio) for AI and ML applications
Skill 1.1.9: Ingest data into SageMaker Feature Store
1.2.1
Skill 1.2.2: Create and manage features by using AWS tools (for example, SageMaker Feature Store)
Skill 1.2.3: Transform streaming data (for example, by using AWS Lambda, Spark)
1.2.4
Skill 1.2.5: Configure and use embedding models to transform text and image data into numerical representations
Skill 1.1.1: Extract data from data sources
1.2.7
Skill 1.2.8: Mask, redact, and anonymize data
1.2.9
Skill 1.3.1: Validate data quality (for example, by using DataBrew, AWS Glue Data Quality)
Skill 1.3.2: Label and annotate data
Skill 1.3.3: Identify and mitigate sources of bias in data by using AWS tools and techniques
Skill 1.3.4: Optimize multimodal data distributions by applying bias metrics across numeric, text, and image assets
1.3.5
Skill 1.1.6: Validate AI training data integrity (for example, prompt-response pair validation, content safety screening)
Skill 1.3.7: Clean data (for example, by detecting outliers, imputing missing data, deduplication)
2.1.1
Skill 2.1.2: Identify fine-tuning strategies for pre-trained FMs to meet business needs
Skill 2.1.3: Compare and select appropriate ML models, generative AI (GenAI) models, algorithms, and solution templates to meet business needs (for example, interpretability, domain-specific performance, latency)
Skill 2.1.4: Evaluate tradeoffs between custom solutions, managed services, pre-trained models, and FMs to meet business needs
Skill 2.1.5: Select Retrieval Augmented Generation (RAG) architecture patterns based on use case requirements
Skill 2.1.6: Assess tradeoffs between ML model performance, training time, and cost
Skill 2.1.7: Assess tradeoffs between AI model performance, latency, and cost
Skill 2.1.8: Apply AWS AI services to solve specific business problems (for example, Amazon Textract, Amazon Rekognition, Amazon Comprehend, Amazon Transcribe)
2.2.1
Skill 2.2.2: Configure SageMaker AI script mode with supported frameworks for simplicity and performance
2.2.3
Skill 2.2.4: Implement training time reduction techniques (for example, early stopping, distributed training)
2.2.5
2.2.6
Skill 2.2.7: Adjust fundamental hyperparameters (for example, epoch, steps, batch size)
2.2.8
Skill 2.2.9: Optimize retrieval components and embedding models
Skill 2.3.1: Perform reproducible experiments (for example, by using MLflow on SageMaker AI, Amazon Bedrock evaluations, Amazon Bedrock Prompt Management)
Skill 2.3.10: Configure RAG system monitoring, including retrieval accuracy assessment
Skill 2.3.2: Create model performance baselines and implement drift detection
Skill 2.3.3: Compare the performance of shadow variants to production variants
Skill 2.3.4: Explain model outputs
2.3.5
Skill 2.3.6: Apply comprehensive model evaluation techniques for traditional ML and GenAI models
2.3.7
2.3.8
Skill 2.3.9: Perform AI evaluation (for example, model output assessment, content quality validation, bias detection, LLM-as-a-judge frameworks)
Skill 3.1.1: Select appropriate compute environments and deployment targets
3.1.2
Skill 3.1.3: Select model inference strategies (for example, real-time and batch processing)
Skill 3.1.4: Evaluate and select appropriate foundation model (FM) deployment options
Skill 3.1.5: Deploy models that were built outside of AWS into AWS environments (for example, Amazon SageMaker AI, Amazon Bedrock Custom Model Import)
Skill 3.1.6: Deploy and configure agents for specific tasks, integration with other services and tools, and agent communication protocols.
3.1.7
Skill 3.1.8: Apply Retrieval Augmented Generation (RAG) system configurations (for example, retrieval strategies, reranking)
Skill 3.2.1: Optimize resource provisioning between on-demand and provisioned resources for performance and cost efficiency
Skill 3.2.10: Implement AI-specific resource scaling for GPU workloads
3.2.11
Skill 3.2.2: Automate compute resource provisioning with integrated communication between stacks and orchestration services
Skill 3.2.3: Build and maintain containers for ML and AI workloads
Skill 3.2.4: Configure SageMaker AI endpoints within VPC network environments
Skill 3.2.5: Deploy and host models programmatically (for example, by using the SageMaker AI SDK for Python, AWS CLI, Boto3)
Skill 3.2.6: Select specific metrics for auto scaling implementations
Skill 3.2.7: Create and manage Amazon Bedrock knowledge bases with vector database configurations, document indexing, and retrieval optimization
Skill 3.2.8: Implement retrieval pipelines to meet business needs
3.2.9
Skill 3.3.1: Implement automated deployment strategies and rollback actions
Skill 3.3.10: Configure FM deployment automation with fine-tuned model versioning
Skill 3.3.11: Configure AI-specific pipeline orchestration for RAG system updates and knowledge base refresh cycles
Skill 3.3.2: Configure and troubleshoot AWS CodeBuild, AWS CodeCommit, AWS CodeDeploy, AWS CodePipeline, and AWS CodeConnections
Skill 3.3.3: Configure training and inference jobs
Skill 3.3.4: Configure automated testing strategies within CI/CD pipelines for traditional ML and AI workloads
3.3.5
Skill 3.3.6: Manage model versions for repeatability and audits
Skill 3.3.7: Manage prompts (for example, Amazon Bedrock Prompt Management)
Skill 3.3.8: Implement automated agent deployment pipelines and agent version management
Skill 3.3.9: Implement AI model testing frameworks, including prompt testing
Skill 4.1.1: Monitor model performance in production by using Amazon CloudWatch generative AI observability, Amazon Bedrock Model Evaluation, and drift detection pipelines.
Skill 4.1.2: Monitor workflows to detect anomalies or errors in data processing or model inference
Skill 4.1.3: Detect changes in data distribution that can affect model performance
Skill 4.1.4: Monitor model performance in production by using A/B testing
Skill 4.1.5: Monitor and automate the management of agent performance and coordination (for example, coordination failure detection, truncated streaming, tool failures)
Skill 4.1.6: Configure AI-specific performance monitoring for foundation models (FMs), such as Amazon Bedrock evaluations
4.2.1
Skill 4.2.10: Monitor AI-specific cost patterns (for example, token usage optimization, embedding computation costs, vector database storage optimization)
4.2.2
Skill 4.2.3: Set up dashboards to monitor performance metrics
Skill 4.2.4: Optimize capacity for cost, performance, and reliability
Skill 4.2.5: Optimize costs and set cost quotas by using appropriate cost management tools
4.2.6
Skill 4.2.7: Evaluate cost implications of using FMs for inference in production
Skill 4.2.8: Monitor agent resource consumption patterns
Skill 4.2.9: Manage FM inference costs with usage optimization
Skill 4.3.1: Secure continuous integration and continuous delivery (CI/CD) pipelines by checking for code and image vulnerabilities
Skill 4.3.2: Configure least privilege access to ML and AI artifacts
Skill 4.3.3: Configure IAM policies and roles for users and applications in ML and AI systems
Skill 4.3.4: Configure comprehensive monitoring, auditing, compliance, and logging for ML and AI systems
Skill 4.3.5: Troubleshoot and debug security issues in ML and AI systems
Skill 4.3.6: Create VPCs, subnets, and security groups to securely isolate ML and AI systems
Skill 4.3.7: Identify and mitigate security risks and vulnerabilities in ML and AI systems
Skill 4.3.8: Select the appropriate credential type to access FMs
Skill 4.3.9: Implement safeguards and sensitive data protection to meet application requirements and responsible AI policies
Wrong questions
Wrong questions
Domain 1: Data Preparation for ML and AI
Domain 2: ML Model and Foundation Model (FM) Development
Domain 3: Deployment and Orchestration of ML and AI Workflows
Domain 4: Operating, Monitoring, and Securing ML and AI Solutions
Scripts
Scripts
Aws review notes
Short notes by skills
Short notes by skills
Skill 1.1.1: Extract data from data sources (for example, Amazon S3, Amazon EBS, Amazon EFS, Amazon RDS, Amazon DynamoDB, Amazon OpenSearch Service)
Skill 1.1.2: Make storage decisions and configure storage services based on cost, performance, data structure, and data compliance
Skill 1.1.3: Troubleshoot and debug data ingestion and storage issues that involve capacity and scalability
Skill 1.1.4: Use AWS Streaming Data Sources to Ingest Data
Skill 1.1.5: Ingest from and write by using appropriate data formats (e.g., Apache Parquet, JSON, CSV, ORC) based on data access patterns
Skill 1.1.6: Merge data from multiple sources (for example, by using programming techniques, AWS Glue, Apache Spark)
Skill 1.1.7: Configure scalable vector databases for AI applications
Skill 1.1.8: Ingest and Store Diverse Data Types (Text, Images, Audio) for AI/ML
Skill 1.1.9: Ingest data into SageMaker Feature Store
Skill 1.2.1: Transform data by using AWS tools
Skill 1.2.2: Create and manage features by using AWS tools (for example, SageMaker Feature Store)
Skill 1.2.3: Transform streaming data (for example, by using AWS Lambda, Spark)
Skill 1.2.4: Perform Feature Engineering (Scaling, Standardization, Feature Splitting, Binning, Log Transformation, Normalization)
Skill 1.2.5: Configure and use embedding models to transform text and image data into numerical representations
Skill 1.2.6: Apply advanced text pre-processing techniques (for example, tokenization, domain-specific augmentation)
Skill 1.2.7: Prepare documents for Retrieval Augmented Generation (RAG) applications (chunking strategies, metadata extraction)
Skill 1.2.8: Mask, Redact, and Anonymize Data
Skill 1.2.9: Prepare data for FM fine-tuning, continuous pre-training, and model distillation
Skill 1.3.1: Validate Data Quality (DataBrew, AWS Glue Data Quality)
Skill 1.3.2: Label and annotate data
Skill 1.3.3: Identify and mitigate sources of bias in data by using AWS tools and techniques (for example, dataset splitting, shuffling, augmentation)
Skill 1.3.4: Optimize multimodal data distributions by applying bias metrics across numeric, text, and image assets
Skill 1.3.5: Resolve Class Imbalance in Numeric, Text, and Image Datasets
Skill 1.3.6: Validate AI Training Data Integrity (Prompt-Response Pair Validation, Content Safety Screening)
Skill 1.3.7: Clean data (for example, by detecting outliers, imputing missing data, deduplication)
Skill 2.1.1: Evaluate and select appropriate FMs from Amazon Bedrock based on task requirements and performance criteria
Skill 2.1.2: Identify fine-tuning strategies for pre-trained FMs to meet business needs
Skill 2.1.3: Compare and select appropriate ML models, GenAI models, algorithms, and solution templates to meet business needs (for example, interpretability, domain-specific performance, latency).
Skill 2.1.4: Evaluate tradeoffs between custom solutions, managed services, pre-trained models, and FMs to meet business needs.
Skill 2.1.5: Select Retrieval Augmented Generation (RAG) architecture patterns based on use case requirements
Skill 2.1.6: Assess Tradeoffs Between ML Model Performance, Training Time, and Cost
Skill 2.1.7: Assess tradeoffs between AI model performance, latency, and cost
Skill 2.1.8: Apply AWS AI Services to Solve Specific Business Problems
Skill 2.2.1: Apply Amazon SageMaker AI built-in algorithms and common ML libraries.
Skill 2.2.2: Configure SageMaker AI Script Mode with Supported Frameworks
Skill 2.2.3: Implement hyperparameter optimization (SageMaker AI automatic model tuning [AMT])
Skill 2.2.4: Implement Training Time Reduction Techniques (Early Stopping, Distributed Training)
Skill 2.2.5: Prevent model overfitting, underfitting, and catastrophic forgetting
Skill 2.2.6: Combine Multiple ML Models to Improve Performance or Reduce Cost
Skill 2.2.7: Adjust fundamental hyperparameters (for example, epoch, steps, batch size)
Skill 2.2.8: Apply customization techniques for AI solutions (for example, task-specific prompt engineering, fine-tuning)
Skill 2.2.9: Optimize retrieval components and embedding models
Skill 2.3.1: Perform Reproducible Experiments
Skill 2.3.10: Configure RAG system monitoring, including retrieval accuracy assessment.
Skill 2.3.2: Create Model Performance Baselines and Implement Drift Detection
Skill 2.3.3: Compare the performance of shadow variants to production variants
Skill 2.3.4: Explain Model Outputs
Skill 2.3.5: Debug model convergence issues
Skill 2.3.6: Apply comprehensive model evaluation techniques for traditional ML and GenAI models.
Skill 2.3.7: Implement integrated human evaluation frameworks
Skill 2.3.8: Apply NLP Evaluation Metrics (BLEU, ROUGE, BERTScore, Semantic Similarity)
Skill 2.3.9: Perform AI Evaluation
Skill 3.1.1: Select appropriate compute environments and deployment targets
Skill 3.1.2: Select deployment orchestrators and multi-model or multi-container deployment strategies
Skill 3.1.3: Select model inference strategies (for example, real-time and batch processing)
Skill 3.1.4: Evaluate and select appropriate foundation model (FM) deployment options
Skill 3.1.5: Deploy Models Built Outside AWS into AWS Environments
Skill 3.1.6: Deploy and Configure Agents for Specific Tasks, Integration, and Agent Communication Protocols
Skill 3.1.7: Configure FM deployment, model hosting, and resource allocation
Skill 3.1.8: Apply Retrieval Augmented Generation (RAG) System Configurations
Skill 3.2.1: Optimize resource provisioning between on-demand and provisioned resources for performance and cost efficiency
Skill 3.2.10: Implement AI-specific resource scaling for GPU workloads
Skill 3.2.11: Deploy agentic workflow infrastructure
Skill 3.2.2: Automate compute resource provisioning with integrated communication between stacks and orchestration services
Skill 3.2.3: Build and maintain containers for ML and AI workloads
Skill 3.2.4: Configure SageMaker AI endpoints within VPC network environments
Skill 3.2.5: Deploy and host models programmatically (for example, by using the SageMaker AI SDK for Python, AWS CLI, Boto3)
Skill 3.2.6: Select specific metrics for auto scaling implementations
Skill 3.2.7: Create and manage Amazon Bedrock knowledge bases with vector database configurations, document indexing, and retrieval optimization.
Skill 3.2.8: Implement retrieval pipelines to meet business needs
Skill 3.2.9: Implement Agent State Management Systems
Skill 3.1.1: Implement automated deployment strategies and rollback actions
Skill 3.3.10: Configure FM Deployment Automation with Fine-Tuned Model Versioning
Skill 3.3.11: Configure AI-specific pipeline orchestration for RAG system updates and knowledge base refresh cycles
Skill 3.3.2: Configure and troubleshoot AWS CodeBuild, AWS CodeCommit, AWS CodeDeploy, AWS CodePipeline, and AWS CodeConnections.
Skill 3.3.3: Configure training and inference jobs
Skill 3.3.4: Configure automated testing strategies within CI/CD pipelines for traditional ML and AI workloads.
Skill 3.3.5: Build and integrate mechanisms to re-train models
Skill 3.3.6: Manage Model Versions for Repeatability and Audits
Skill 3.3.7: Manage Prompts (Amazon Bedrock Prompt Management)
Skill 3.8: Implement automated agent deployment pipelines and agent version management.
Skill 3.3.9: Implement AI Model Testing Frameworks, Including Prompt Testing
Skill 4.1.1: Monitor model performance in production by using Amazon CloudWatch generative AI observability, Amazon Bedrock Model Evaluation, and drift detection pipelines
Skill 4.1.2: Monitor workflows to detect anomalies or errors in data processing or model inference
Skill 4.1.3: Detect changes in data distribution that can affect model performance.
Skill 4.1.4: Monitor model performance in production by using A/B testing
Skill 4.1.5: Monitor and automate the management of agent performance and coordination
Skill 4.1.6: Configure AI-specific performance monitoring for foundation models (FMs), such as Amazon Bedrock evaluations
Skill 4.2.1: Select inference instance families to optimize performance and cost
Skill 4.2.10: Monitor AI-specific cost patterns (token usage optimization, embedding computation costs, vector database storage optimization)
Skill 4.2.2: Configure and Use Tools to Troubleshoot and Analyze Resources
Skill 4.2.3: Set up dashboards to monitor performance metrics
Skill 4.4: Optimize capacity for cost, performance, and reliability
Skill 4.2.5: Optimize costs and set cost quotas by using appropriate cost management tools
Skill 4.2.6: Optimize infrastructure costs by selecting purchasing options
Skill 4.2.7: Evaluate cost implications of using FMs for inference in production
Skill 4.2.8: Monitor agent resource consumption patterns
Skill 4.2.9: Manage FM Inference Costs with Usage Optimization
Skill 4.3.1: Secure CI/CD pipelines by checking for code and image vulnerabilities
Skill 4.3.2: Configure least privilege access to ML and AI artifacts
Skill 4.3.3: Configure IAM Policies and Roles for Users and Applications in ML and AI Systems
Skill 4.3.4: Configure comprehensive monitoring, auditing, compliance, and logging for ML and AI systems (for example, AWS CloudTrail, AWS Config).
Skill 4.3.5: Troubleshoot and debug security issues in ML and AI systems
Skill 4.3.6: Create VPCs, subnets, and security groups to securely isolate ML and AI systems
Skill 4.3.7: Identify and Mitigate Security Risks and Vulnerabilities in ML and AI Systems
Skill 4.3.8: Select the appropriate credential type to access FMs (e.g., Amazon Bedrock API keys, IAM credentials)
Skill 4.3.9: Implement Safeguards & Sensitive Data Protection (Amazon Bedrock Guardrails)
Special topics
Special topics
RAG General strategy?
Amazon OpenSearch Service
Interview prep
Interview prep
Practice questions
Practice questions
AI Engineer Job Domain
Tools
Tools
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