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MLA-C01
MLA-C01 Amazon Web Services

Machine Learning Engineer Associate

The course teaches AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam content, covering data preparation, model development, deployment, orchestration, monitoring, maintenance, and security using AWS services.

170
Minutes
65
Questions
720/1000
Passing Score
$150
Exam Cost
4
Languages

Who Should Take This

It is designed for software engineers, data scientists, or ML engineers with at least one year of hands‑on experience building and deploying models on AWS. They seek to validate their ability to design production‑ready ML pipelines, implement AWS services, and ensure operational excellence and security.

What's Covered

1 Ingest, transform, and validate data for ML workloads using AWS data services, and perform feature engineering and feature store operations.
2 Choose appropriate modeling approaches, train and tune ML models using SageMaker, and evaluate model performance with appropriate metrics.
3 Deploy models to SageMaker endpoints, orchestrate ML pipelines, and implement CI/CD for ML workflows.
4 Monitor model performance and data quality in production, implement retraining strategies, and secure ML infrastructure and data.

Exam Structure

Question Types

  • Multiple Choice
  • Multiple Response

Scoring Method

Scaled scoring from 100 to 1000, minimum passing score of 720

Delivery Method

Pearson VUE testing center or online proctored

Recertification

Recertify every 3 years by passing the current exam or earning a higher-level AWS certification.

What's Included in AccelaStudy® AI

Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats

Course Outline

71 learning goals
1 Domain 1: Data Preparation for ML
4 topics

Ingest and store data for ML workloads

  • Identify AWS data ingestion services and explain how S3, Kinesis Data Streams, Kinesis Data Firehose, Glue crawlers, and AWS Data Exchange support batch and streaming data collection for ML pipelines.
  • Implement S3 storage strategies for ML datasets including bucket organization, object lifecycle policies, storage classes, versioning, and cross-region replication for training data durability.
  • Implement streaming data ingestion using Kinesis Data Streams and Kinesis Data Firehose with delivery to S3, including partition key design, shard management, and buffering configuration for ML data pipelines.
  • Implement data cataloging and schema discovery using AWS Glue crawlers, Glue Data Catalog, and Athena to create queryable metadata for ML dataset inventories.
  • Analyze data partitioning, versioning, and storage format tradeoffs (Parquet, CSV, JSON, RecordIO) to optimize cost, query performance, and training data loading efficiency.

Transform data and perform feature engineering

  • Identify AWS data transformation services and explain the capabilities of SageMaker Data Wrangler, SageMaker Processing, Glue ETL jobs, Glue DataBrew, and EMR for data preparation workflows.
  • Implement data transformation pipelines using SageMaker Processing jobs with scikit-learn, Spark, or custom containers for cleaning, normalization, encoding, and imputation of ML training data.
  • Implement visual data preparation workflows using SageMaker Data Wrangler with data flow transformations, custom transforms, and export to SageMaker Processing or Pipelines.
  • Implement AWS Glue ETL jobs with PySpark transformations, job bookmarks, and crawler-based schema evolution for scalable data preparation pipelines.
  • Analyze feature engineering tradeoffs affecting model signal quality, data leakage risk, and operational maintainability when selecting between one-hot encoding, target encoding, embedding-based representations, and binning strategies.

Manage features with SageMaker Feature Store

  • Identify SageMaker Feature Store concepts and explain the differences between online and offline stores, feature groups, record identifiers, event time, and feature ingestion patterns.
  • Implement SageMaker Feature Store feature groups with online and offline configurations, define feature definitions, and ingest features using batch and streaming ingestion APIs.
  • Analyze feature reuse, point-in-time correctness, and training-serving skew prevention strategies when designing Feature Store feature groups for production ML workflows.

Ensure data quality and prepare data for modeling

  • Identify data quality dimensions and explain how completeness, consistency, accuracy, timeliness, and class balance affect ML model training outcomes.
  • Implement data quality validation checks using SageMaker Data Wrangler data insights, Glue Data Quality rules, and custom validation scripts to detect anomalies, missing values, and distribution drift before training.
  • Implement dataset splitting strategies for training, validation, and test sets with stratification, time-based splits, and group-aware splits to prevent data leakage and ensure valid evaluation.
  • Implement data labeling workflows using SageMaker Ground Truth with labeling workforces, annotation consolidation, and active learning to produce high-quality labeled datasets.
  • Analyze data integrity failures and determine corrective remediation strategies including imputation methods, resampling techniques, and augmentation approaches to preserve model validity.
2 Domain 2: ML Model Development
4 topics

Choose a modeling approach

  • Identify ML problem types and explain when to use classification, regression, clustering, ranking, time-series forecasting, anomaly detection, and recommendation algorithms for given business objectives.
  • Identify SageMaker built-in algorithms and explain the capabilities of XGBoost, Linear Learner, K-Nearest Neighbors, Random Cut Forest, BlazingText, Image Classification, Object Detection, and Seq2Seq for common ML tasks.
  • Identify Amazon Bedrock foundation model capabilities and explain when to use Bedrock for text generation, summarization, embedding, and image generation versus training custom models with SageMaker.
  • Select modeling approaches aligned to problem type, data characteristics, latency constraints, explainability requirements, and available training data volume using SageMaker Autopilot for automated model selection.
  • Analyze algorithm choice tradeoffs among model complexity, training cost, inference latency, interpretability, and maintainability to recommend the optimal approach for a given ML scenario.

Train and tune models with SageMaker

  • Identify SageMaker training concepts and explain training job configuration including instance types, input data channels, output locations, training images, and managed spot training for cost reduction.
  • Implement SageMaker training jobs using built-in algorithms and custom training scripts with the SageMaker Python SDK, including framework estimators for TensorFlow, PyTorch, and scikit-learn.
  • Implement SageMaker Automatic Model Tuning (hyperparameter optimization) with Bayesian, random, and hyperband strategies, defining hyperparameter ranges and objective metrics.
  • Implement distributed training strategies using SageMaker data parallelism and model parallelism libraries to scale training across multiple instances and GPUs.
  • Implement experiment tracking and training diagnostics using SageMaker Experiments and SageMaker Debugger to capture metrics, detect training anomalies, and compare model variants.
  • Analyze training outcomes and refine model configurations using error analysis, learning curve diagnostics, and ablation experiments to improve generalization and reduce overfitting.

Evaluate and analyze model performance

  • Identify ML evaluation metrics and explain when to use accuracy, precision, recall, F1, AUC-ROC, RMSE, MAE, and MAPE for classification, regression, and ranking model assessment.
  • Implement model evaluation workflows using SageMaker Processing jobs and SageMaker Clarify to generate evaluation reports, confusion matrices, and feature importance analysis.
  • Implement bias detection and explainability analysis using SageMaker Clarify to identify pre-training data bias, post-training model bias, and generate SHAP-based feature attribution explanations.
  • Analyze evaluation outputs to identify overfitting, underfitting, class imbalance effects, bias patterns, and threshold optimization opportunities across model variants.

Manage models with SageMaker Model Registry

  • Identify SageMaker Model Registry concepts and explain model groups, model versions, approval statuses, model packages, and lineage tracking for production model governance.
  • Implement model versioning and approval workflows using SageMaker Model Registry with model groups, version metadata, approval status transitions, and cross-account model sharing.
  • Analyze model governance requirements and determine registry organization strategies that support reproducibility, auditability, and safe promotion across development, staging, and production accounts.
3 Domain 3: Deployment and Orchestration of ML Workflows
4 topics

Select and configure deployment infrastructure

  • Identify SageMaker inference options and explain the differences between real-time endpoints, serverless inference, asynchronous inference, and batch transform for model serving patterns.
  • Implement SageMaker real-time endpoints with instance type selection, auto-scaling policies, multi-model endpoints, and multi-container endpoints for production model serving.
  • Implement SageMaker batch transform jobs and asynchronous inference endpoints for large-scale offline prediction workloads with input/output configuration and concurrency management.
  • Implement model containerization using custom inference containers with ECR, including model loading, request deserialization, prediction, and response serialization for SageMaker endpoints.
  • Analyze deployment architecture tradeoffs across latency, throughput, cost, scaling behavior, and operational complexity to select optimal inference patterns for given workload requirements.

Create and manage ML infrastructure as code

  • Identify infrastructure-as-code tools for ML and explain how CloudFormation, CDK, and SageMaker Project templates automate provisioning of ML training, endpoint, and pipeline resources.
  • Implement CloudFormation templates for reproducible ML environments including SageMaker domains, training job configurations, endpoint deployments, and associated IAM roles and VPC resources.
  • Analyze infrastructure automation for reliability, security posture, environment parity, and drift resilience across development, staging, and production ML environments.

Orchestrate ML workflows with SageMaker Pipelines

  • Identify SageMaker Pipelines concepts and explain pipeline steps (Processing, Training, Tuning, Transform, Model, Condition, Callback), step dependencies, pipeline parameters, and execution semantics.
  • Implement SageMaker Pipelines with multi-step workflows including data processing, model training, evaluation, conditional registration, and batch transform steps with parameterized execution.
  • Implement Step Functions-based ML orchestration with SageMaker integration actions for training, transform, and endpoint operations combined with Lambda tasks, parallel branches, and error handling.
  • Analyze orchestration pipeline designs and determine improvements for retry logic, failure handling, execution caching, and governance compliance in end-to-end ML workflows.

Implement CI/CD for ML model deployment

  • Identify MLOps CI/CD patterns and explain how CodePipeline, CodeBuild, SageMaker Projects, and EventBridge compose automated model retraining, validation, and deployment promotion workflows.
  • Implement CI/CD pipelines for ML models using SageMaker Projects with CodePipeline integration, automated model building, quality gates, approval actions, and endpoint deployment promotion.
  • Implement safe deployment strategies for ML models using blue/green deployments, canary traffic shifting, shadow testing, and A/B testing with SageMaker endpoint production variants.
  • Analyze CI/CD pipeline robustness and determine improvements for rollback safety, model validation gate effectiveness, deployment blast radius minimization, and governance auditability.
4 Domain 4: ML Solution Monitoring, Maintenance, and Security
5 topics

Monitor model inference and data quality

  • Identify SageMaker Model Monitor capabilities and explain how data quality monitoring, model quality monitoring, bias drift monitoring, and feature attribution drift monitoring detect production degradation.
  • Implement SageMaker Model Monitor schedules with baseline constraints, data capture configuration, and monitoring job definitions to detect data drift, model quality degradation, and bias drift in real-time endpoints.
  • Implement CloudWatch alarms and dashboards for ML endpoint metrics including invocation count, latency percentiles, error rates, model quality metrics, and auto-scaling triggers.
  • Analyze monitoring alerts and Model Monitor violation reports to determine whether retraining, rollback, threshold recalibration, or data pipeline remediation is the appropriate corrective action.

Monitor and optimize ML infrastructure and costs

  • Identify AWS cost management services and explain how Cost Explorer, Budgets, compute instance pricing models (on-demand, reserved, spot, Savings Plans), and SageMaker managed spot training reduce ML infrastructure costs.
  • Implement infrastructure monitoring using CloudWatch metrics and logs for SageMaker training jobs, processing jobs, and endpoints to track GPU/CPU utilization, memory usage, and I/O throughput.
  • Implement endpoint auto-scaling policies with target tracking, step scaling, and scheduled scaling based on invocation metrics and utilization patterns for cost-efficient model serving.
  • Analyze cost and performance telemetry to determine rightsizing opportunities, spot instance eligibility, serverless inference migration candidates, and Savings Plans commitment optimization for ML workloads.

Secure ML resources with IAM and network controls

  • Identify AWS security services for ML and explain how IAM roles, resource-based policies, VPC configurations, security groups, and SageMaker execution roles control access to ML training, data, and inference resources.
  • Implement least-privilege IAM policies for SageMaker roles including training job execution roles, pipeline execution roles, endpoint invocation policies, and cross-account model sharing permissions.
  • Implement VPC-based network isolation for SageMaker resources including training in VPC mode, VPC endpoints for SageMaker API and S3, and private subnet configurations for endpoint hosting.
  • Analyze ML workflow security risks and determine remediation strategies for unauthorized data access, model artifact exposure, inference endpoint abuse, and privilege escalation in multi-account ML environments.

Implement encryption and data protection for ML

  • Identify AWS encryption options for ML and explain how KMS customer managed keys, S3 server-side encryption, SageMaker volume encryption, and inter-container traffic encryption protect ML data at rest and in transit.
  • Implement KMS-based encryption for SageMaker training volumes, S3 training data, model artifacts, endpoint storage, and notebook instance EBS volumes with customer managed key policies.
  • Implement secrets management for ML workflows using Secrets Manager and Systems Manager Parameter Store for database credentials, API keys, and sensitive hyperparameters in training and inference code.
  • Analyze data protection strategies and determine encryption, tokenization, and access logging configurations that satisfy compliance requirements (HIPAA, PCI-DSS, GDPR) for ML systems processing sensitive data.

Implement logging, auditing, and governance for ML

  • Identify AWS logging and auditing services for ML and explain how CloudTrail, CloudWatch Logs, SageMaker lineage tracking, and S3 access logging provide auditability for ML pipeline operations.
  • Implement CloudTrail logging for SageMaker API calls, S3 data access events, and IAM authentication events to create audit trails for ML model development and deployment activities.
  • Analyze governance gaps in ML workflows and determine logging, tagging, and access control improvements needed for regulatory compliance, model lineage traceability, and incident forensics readiness.

Hands-On Labs

20 labs ~460 min total Console Simulator

Practice in a simulated cloud console or Python code sandbox — no account needed. Each lab runs entirely in your browser.

Certification Benefits

Salary Impact

$146,000
Average Salary

Related Job Roles

ML Engineer Data Scientist AI Engineer ML Operations Engineer Applied Scientist

Industry Recognition

The AWS ML Engineer Associate certification validates practical machine learning engineering skills on AWS, the successor to the retiring ML Specialty. As AI/ML adoption accelerates across industries, certified ML engineers command premium compensation and are critical hires for organizations building production ML systems.

Scope

Included Topics

  • All domains and task statements in the AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam guide: Domain 1 Data Preparation for ML (28%), Domain 2 ML Model Development (26%), Domain 3 Deployment and Orchestration of ML Workflows (22%), and Domain 4 ML Solution Monitoring, Maintenance, and Security (24%).
  • Associate-level machine learning engineering responsibilities on AWS including data ingestion, transformation, feature engineering, model training, hyperparameter tuning, deployment architecture, CI/CD orchestration, monitoring, and security operations.
  • Key AWS services for ML engineers: SageMaker (Studio, Training, Processing, Endpoints, Pipelines, Feature Store, Model Registry, Clarify, Data Wrangler, Autopilot, Debugger, Model Monitor, Canvas), Bedrock, S3, Glue, Glue DataBrew, Athena, EMR, Kinesis, Step Functions, Lambda, EventBridge, CloudWatch, CloudTrail, IAM, KMS, VPC, ECR, CodePipeline, CodeBuild, CloudFormation, and Secrets Manager.
  • Practical workflow design decisions involving performance, reliability, scalability, maintainability, cost optimization, and compliance constraints for production ML systems on AWS.

Not Covered

  • Research-focused deep learning theory and advanced mathematical derivations not required by MLA-C01 objectives.
  • Specialty-level model architecture optimization techniques and advanced distributed training internals beyond associate exam scope.
  • Non-AWS platform tooling and provider-specific patterns that do not map to AWS machine learning engineering workflows.
  • Rapidly changing exact service pricing values and temporary commercial offers that are not stable for domain knowledge synthesis.
  • AWS CLI command-level syntax memorization and SDK version-specific API signatures.

Official Exam Page

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