AWS Machine Learning Engineer Associate Certification Exam Syllabus

• Data formats and ingestion mechanisms (for example, validated and non-validated formats, Apache Parquet, JSON, CSV, Apache ORC, Apache Avro, RecordIO)
• How to use the core AWS data sources (for example, Amazon S3, Amazon Elastic File System [Amazon EFS], Amazon FSx for NetApp ONTAP)
• How to use AWS streaming data sources to ingest data (for example, Amazon Kinesis, Apache Flink, Apache Kafka)
• AWS storage options, including use cases and tradeoffs

- Skills in:

• Extracting data from storage (for example, Amazon S3, Amazon Elastic Block Store [Amazon EBS], Amazon EFS, Amazon RDS, Amazon DynamoDB) by using relevant AWS service options (for example, Amazon S3 Transfer Acceleration, Amazon EBS Provisioned IOPS)
• Choosing appropriate data formats (for example, Parquet, JSON, CSV, ORC) based on data access patterns
• Ingesting data into Amazon SageMaker Data Wrangler and SageMaker Feature Store
• Merging data from multiple sources (for example, by using programming techniques, AWS Glue, Apache Spark)
• Troubleshooting and debugging data ingestion and storage issues that involve capacity and scalability
• Making initial storage decisions based on cost, performance, and data structure

• Data cleaning and transformation techniques (for example, detecting and treating outliers, imputing missing data, combining, deduplication)
• Feature engineering techniques (for example, data scaling and standardization, feature splitting, binning, log transformation, normalization)
• Encoding techniques (for example, one-hot encoding, binary encoding, label encoding, tokenization)
• Tools to explore, visualize, or transform data and features (for example, SageMaker Data Wrangler, AWS Glue, AWS Glue DataBrew)
• Services that transform streaming data (for example, AWS Lambda, Spark)
• Data annotation and labeling services that create high-quality labeled datasets

- Skills in:

• Transforming data by using AWS tools (for example, AWS Glue, AWS Glue DataBrew, Spark running on Amazon EMR, SageMaker Data Wrangler)
• Creating and managing features by using AWS tools (for example, SageMaker Feature Store)
• Validating and labeling data by using AWS services (for example, SageMaker Ground Truth, Amazon Mechanical Turk)

• Pre-training bias metrics for numeric, text, and image data (for example, class imbalance [CI], difference in proportions of labels [DPL])
• Strategies to address CI in numeric, text, and image datasets (for example, synthetic data generation, resampling)
• Techniques to encrypt data
• Data classification, anonymization, and masking
• Implications of compliance requirements (for example, personally identifiable information [PII], protected health information [PHI], data residency)

- Skills in:

• Validating data quality (for example, by using AWS Glue DataBrew and AWS Glue Data Quality)
• Identifying and mitigating sources of bias in data (for example, selection bias, measurement bias) by using AWS tools (for example, SageMaker Clarify)
• Preparing data to reduce prediction bias (for example, by using dataset splitting, shuffling, and augmentation)
• Configuring data to load into the model training resource (for example, Amazon EFS, Amazon FSx)

• Capabilities and appropriate uses of ML algorithms to solve business problems
• How to use AWS artificial intelligence (AI) services (for example, Amazon Translate, Amazon Transcribe, Amazon Rekognition, Amazon Bedrock) to solve specific business problems
• How to consider interpretability during model selection or algorithm selection
• SageMaker built-in algorithms and when to apply them

- Skills in:

• Assessing available data and problem complexity to determine the feasibility of an ML solution
• Comparing and selecting appropriate ML models or algorithms to solve specific problems
• Choosing built-in algorithms, foundation models, and solution templates (for example, in SageMaker JumpStart and Amazon Bedrock)
• Selecting models or algorithms based on costs
• Selecting AI services to solve common business needs

• Elements in the training process (for example, epoch, steps, batch size)
• Methods to reduce model training time (for example, early stopping, distributed training)
• Factors that influence model size
• Methods to improve model performance
• Benefits of regularization techniques (for example, dropout, weight decay, L1 and L2)
• Hyperparameter tuning techniques (for example, random search, Bayesian optimization)
• Model hyperparameters and their effects on model performance (for example, number of trees in a tree-based model, number of layers in a neural network)
• Methods to integrate models that were built outside SageMaker into SageMaker

- Skills in:

• Using SageMaker built-in algorithms and common ML libraries to develop ML models
• Using SageMaker script mode with SageMaker supported frameworks to train models (for example, TensorFlow, PyTorch)
• Using custom datasets to fine-tune pre-trained models (for example, Amazon Bedrock, SageMaker JumpStart)
• Performing hyperparameter tuning (for example, by using SageMaker automatic model tuning [AMT])
• Integrating automated hyperparameter optimization capabilities
• Preventing model overfitting, underfitting, and catastrophic forgetting (for example, by using regularization techniques, feature selection)
• Combining multiple training models to improve performance (for example, ensembling, stacking, boosting)
• Reducing model size (for example, by altering data types, pruning, updating feature selection, compression)
• Managing model versions for repeatability and audits (for example, by using the SageMaker Model Registry)

• Model evaluation techniques and metrics (for example, confusion matrix, heat maps, F1 score, accuracy, precision, recall, Root Mean Square Error [RMSE], receiver operating characteristic [ROC], Area Under the ROC Curve [AUC])
• Methods to create performance baselines
• Methods to identify model overfitting and underfitting
• Metrics available in SageMaker Clarify to gain insights into ML training data and models
• Convergence issues

- Skills in:

• Selecting and interpreting evaluation metrics and detecting model bias
• Assessing tradeoffs between model performance, training time, and cost
• Performing reproducible experiments by using AWS services
• Comparing the performance of a shadow variant to the performance of a production variant
• Using SageMaker Clarify to interpret model outputs
• Using SageMaker Model Debugger to debug model convergence

• Deployment best practices (for example, versioning, rollback strategies)
• AWS deployment services (for example, SageMaker)
• Methods to serve ML models in real time and in batches
• How to provision compute resources in production environments and test environments (for example, CPU, GPU)
• Model and endpoint requirements for deployment endpoints (for example, serverless endpoints, real-time endpoints, asynchronous endpoints, batch inference)
• How to choose appropriate containers (for example, provided or customized)
• Methods to optimize models on edge devices (for example, SageMaker Neo)

- Skills in:

• Evaluating performance, cost, and latency tradeoffs
• Choosing the appropriate compute environment for training and inference based on requirements (for example, GPU or CPU specifications, processor family, networking bandwidth)
• Selecting the correct deployment orchestrator (for example, Apache Airflow, SageMaker Pipelines)
• Selecting multi-model or multi-container deployments
• Selecting the correct deployment target (for example, SageMaker endpoints, Kubernetes, Amazon Elastic Container Service [Amazon ECS], Amazon Elastic Kubernetes Service [Amazon EKS], Lambda)
• Choosing model deployment strategies (for example, real time, batch)

• Difference between on-demand and provisioned resources
• How to compare scaling policies
• Tradeoffs and use cases of infrastructure as code (IaC) options (for example, AWS CloudFormation, AWS Cloud Development Kit [AWS CDK])
• Containerization concepts and AWS container services
• How to use SageMaker endpoint auto scaling policies to meet scalability requirements (for example, based on demand, time)

- Skills in:

• Applying best practices to enable maintainable, scalable, and cost-effective ML solutions (for example, automatic scaling on SageMaker endpoints, dynamically adding Spot Instances, by using Amazon EC2 instances, by using Lambda behind the endpoints)
• Automating the provisioning of compute resources, including communication between stacks (for example, by using CloudFormation, AWS CDK)
• Building and maintaining containers (for example, Amazon Elastic Container Registry [Amazon ECR], Amazon EKS, Amazon ECS, by using bring your own container [BYOC] with SageMaker)
• Configuring SageMaker endpoints within the VPC network
• Deploying and hosting models by using the SageMaker SDK
• Choosing specific metrics for auto scaling (for example, model latency, CPU utilization, invocations per instance)

• Capabilities and quotas for AWS CodePipeline, AWS CodeBuild, and AWS CodeDeploy
• Automation and integration of data ingestion with orchestration services
• Version control systems and basic usage (for example, Git)
• CI/CD principles and how they fit into ML workflows
• Deployment strategies and rollback actions (for example, blue/green, canary, linear)
• How code repositories and pipelines work together

- Skills in:

• Configuring and troubleshooting CodeBuild, CodeDeploy, and CodePipeline, including stages
• Applying continuous deployment flow structures to invoke pipelines (for example, Gitflow, GitHub Flow)
• Using AWS services to automate orchestration (for example, to deploy ML models, automate model building)
• Configuring training and inference jobs (for example, by using Amazon EventBridge rules, SageMaker Pipelines, CodePipeline)
• Creating automated tests in CI/CD pipelines (for example, integration tests, unit tests, end-to-end tests)
• Building and integrating mechanisms to retrain models

• Drift in ML models
• Techniques to monitor data quality and model performance
• Design principles for ML lenses relevant to monitoring

- Skills in:

• Monitoring models in production (for example, by using SageMaker Model Monitor)
• Monitoring workflows to detect anomalies or errors in data processing or model inference
• Detecting changes in the distribution of data that can affect model performance (for example, by using SageMaker Clarify)
• Monitoring model performance in production by using A/B testing

• Key performance metrics for ML infrastructure (for example, utilization, throughput, availability, scalability, fault tolerance)
• Monitoring and observability tools to troubleshoot latency and performance issues (for example, AWS X-Ray, Amazon CloudWatch Lambda Insights, Amazon CloudWatch Logs Insights)
• How to use AWS CloudTrail to log, monitor, and invoke re-training activities
• Differences between instance types and how they affect performance (for example, memory optimized, compute optimized, general purpose, inference optimized)
• Capabilities of cost analysis tools (for example, AWS Cost Explorer, AWS Billing and Cost Management, AWS Trusted Advisor)
• Cost tracking and allocation techniques (for example, resource tagging)

- Skills in:

• Configuring and using tools to troubleshoot and analyze resources (for example, CloudWatch Logs, CloudWatch alarms)
• Creating CloudTrail trails
• Setting up dashboards to monitor performance metrics (for example, by using Amazon QuickSight, CloudWatch dashboards)
• Monitoring infrastructure (for example, by using EventBridge events)
• Rightsizing instance families and sizes (for example, by using SageMaker Inference Recommender and AWS Compute Optimizer)
• Monitoring and resolving latency and scaling issues
• Preparing infrastructure for cost monitoring (for example, by applying a tagging strategy)
• Troubleshooting capacity concerns that involve cost and performance (for example, provisioned concurrency, service quotas, auto scaling)
• Optimizing costs and setting cost quotas by using appropriate cost management tools (for example, AWS Cost Explorer, AWS Trusted Advisor, AWS Budgets)
• Optimizing infrastructure costs by selecting purchasing options (for example, Spot Instances, On-Demand Instances, Reserved Instances, SageMaker Savings Plans)

• IAM roles, policies, and groups that control access to AWS services (for example, AWS Identity and Access Management [IAM], bucket policies, SageMaker Role Manager)
• SageMaker security and compliance features
• Controls for network access to ML resources
• Security best practices for CI/CD pipelines

- Skills in:

• Configuring least privilege access to ML artifacts
• Configuring IAM policies and roles for users and applications that interact with ML systems
• Monitoring, auditing, and logging ML systems to ensure continued security and compliance
• Troubleshooting and debugging security issues
• Building VPCs, subnets, and security groups to securely isolate ML systems