MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
An ML engineer needs to orchestrate a multi-step workflow that includes data preprocessing on Spark, model training on SageMaker, and deployment to a production endpoint. They require tight integration with other AWS services and the ability to add custom logic. Which AWS service should they use alongside SageMaker?
⚠ Common exam trap
A common mix-up: candidates confuse SageMaker Pipelines (a SageMaker-native orchestrator) with a general-purpose orchestrator, overlooking the requirement for tight integration with non-SageMaker services like Spark and custom logic — Step Functions is the correct choice for heterogeneous, multi-service ML workflows.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
AWS Step Functions
AWS Step Functions is the correct choice because it provides a serverless workflow orchestration service that can coordinate multi-step ML pipelines involving Spark on AWS Glue or EMR, SageMaker training jobs, and endpoint deployments. It offers tight integration with over 200 AWS services via direct SDK integrations, supports custom logic through Lambda functions, and includes built-in error handling, retries, and parallel execution — making it ideal for complex, heterogeneous ML workflows that extend beyond SageMaker's native capabilities.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
AWS Step Functions
Why this is correct
AWS Step Functions orchestrates the workflow as a state machine, invoking Spark preprocessing, SageMaker training jobs and endpoint deployment as discrete steps. Its native AWS service integrations plus Lambda and Activity tasks satisfy the requirement for custom logic, while retries, error handling and visual tracking coordinate the multi-step pipeline that SageMaker alone cannot sequence.
- ✗
AWS CloudFormation
Why it's wrong here
CloudFormation provisions and updates infrastructure declaratively; it has no execution engine for sequencing Spark jobs, training runs and deployments or for branching on runtime outcomes. It is tempting because it does define SageMaker resources and can invoke Lambda-backed custom resources, which suits repeatable infrastructure deployment rather than workflow orchestration.
- ✗
SageMaker Pipelines
Why it's wrong here
SageMaker Pipelines orchestrates ML steps but its native step types and conditional logic are scoped to SageMaker-centric DAGs, so embedding arbitrary Spark processing and custom branching logic across other AWS services is constrained. It is tempting because it is purpose-built for ML workflows, and would be correct for a pipeline composed entirely of SageMaker training, processing and registration steps.
- ✗
Amazon EventBridge
Why it's wrong here
EventBridge routes and filters events between services but provides no state machine, retry semantics or step sequencing, so it cannot coordinate a multi-stage Spark-train-deploy workflow. It is tempting because it triggers SageMaker jobs on schedules or events, which suits event-driven initiation rather than orchestrating dependent steps with custom logic.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.