MLA-C01 / Domain 3 / 22%

Deployment and Orchestration of ML Workflows

Workflow orchestration, deployment patterns, inference, and pipelines.

Official Task Statements

TaskWhat to prove
MLA-3.1Select deployment infrastructure based on existing architecture and requirements.
MLA-3.2Create and script infrastructure based on existing architecture and requirements.
MLA-3.3Use automated orchestration tools to set up CI/CD pipelines.

Concepts You Need to Understand

  • Deployment infrastructure, endpoints, batch transform, pipelines, CI/CD, model registry, canary deployments, and rollback.

AWS services involved

  • SageMaker Endpoints
  • Batch Transform
  • SageMaker Pipelines
  • Model Registry
  • CodePipeline
  • ECR

Important configurations

  • Endpoint variant weights.
  • Auto scaling.
  • Model package approval.
  • Container image security.

Exam Decision Patterns

Least operational overhead

Prefer managed and serverless services when they satisfy the requirement. Exceptions appear when the scenario needs host control, unsupported runtimes, specialized network behavior, or exact migration compatibility.

Highly available

Identify the failure boundary. One instance is not HA. Multiple instances in one AZ help capacity but not AZ failure. Multi-AZ handles regional AZ faults. Multi-Region handles regional events but adds complexity and cost.

Durable

Durability is about preserving data. Use replication, versioning, backups, point-in-time recovery, and tested restore plans. A durable backup does not guarantee a low RTO.

Decouple the application

Use SQS for buffering work, SNS for fanout, EventBridge for event routing, and Step Functions for visible workflow state. Add retries, DLQs, and idempotent consumers.

Least privilege

Prefer roles and temporary credentials, scope actions/resources/conditions, watch explicit denies, and remember that resource policies may also be required.

Most cost-effective

Read usage pattern, duration, access frequency, scaling behavior, data transfer, and operations. Cheapest unit price is not always lowest total cost.

Lowest latency

Move content or compute closer to users, cache aggressively, choose the right database access pattern, and avoid unnecessary cross-Region or NAT paths.

Private connectivity

Use private subnets, VPC endpoints, PrivateLink, VPN, Direct Connect, Transit Gateway, and tight DNS/routing design instead of public exposure.

Minimum downtime

Separate deployment downtime, failure recovery, and data restore time. Use blue/green, canary, Multi-AZ, replication, and tested rollback where appropriate.

Automatic remediation

Pair a reliable signal with EventBridge or CloudWatch, a scoped Systems Manager Automation or Lambda action, and a validation step.

Common Mistakes

  • Using real-time endpoints for offline batch scoring.
  • Promoting a model without artifact and metric traceability.

Example Architecture

ML Training and Deployment Pipeline Data is prepared, a model is trained and approved, then deployed and monitored for drift and performance. ML Training and Deployment Pipeline S3 DataSageMaker ProcessingTraining JobModel RegistryEndpointModel Monitor
Data is prepared, a model is trained and approved, then deployed and monitored for drift and performance.

Hands-On Activity

Sketch a pipeline from data validation through approval and deployment.

For an AWS-account lab, use one of the linked mini labs and keep cleanup steps visible before you start.

Task-by-Task Study Notes

MLA-3.1 - Select deployment infrastructure based on existing architecture and requirements.

This task statement is asking whether you can turn a scenario into a decision. Start by identifying the workload requirement, the control or service family involved, and the tradeoff AWS is testing in this domain.

  • Translate the wording into requirements: security, operations, cost, availability, latency, governance, or data behavior.
  • Choose the service or configuration that directly satisfies those requirements with the least unnecessary complexity.
  • Reject options that are technically possible but miss the domain goal or increase risk without a requirement.

Practice MLA-3.1 style questions in this domain

MLA-3.2 - Create and script infrastructure based on existing architecture and requirements.

This task statement is asking whether you can turn a scenario into a decision. Start by identifying the workload requirement, the control or service family involved, and the tradeoff AWS is testing in this domain.

  • Translate the wording into requirements: security, operations, cost, availability, latency, governance, or data behavior.
  • Choose the service or configuration that directly satisfies those requirements with the least unnecessary complexity.
  • Reject options that are technically possible but miss the domain goal or increase risk without a requirement.

Practice MLA-3.2 style questions in this domain

MLA-3.3 - Use automated orchestration tools to set up CI/CD pipelines.

This task statement is asking whether you can turn a scenario into a decision. Start by identifying the workload requirement, the control or service family involved, and the tradeoff AWS is testing in this domain.

  • Translate the wording into requirements: security, operations, cost, availability, latency, governance, or data behavior.
  • Choose the service or configuration that directly satisfies those requirements with the least unnecessary complexity.
  • Reject options that are technically possible but miss the domain goal or increase risk without a requirement.

Practice MLA-3.3 style questions in this domain

Review Checklist

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