Question 92 of 1,000
Deployment and Orchestration of ML WorkflowsmediumMultiple ChoiceObjective-mapped

MLA-C01 Deployment and Orchestration of ML Workflows Practice Question

This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.

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?

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.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that 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.

Detailed technical explanation

How to think about this question

Step Functions uses Amazon States Language (ASL) to define state machines with tasks, choices, parallel branches, and error handling. For ML workflows, a common pattern is to use a 'Map' state to run parallel hyperparameter tuning jobs, then a 'Choice' state to evaluate metrics before proceeding to deployment. Under the hood, Step Functions integrates with SageMaker via the 'arn:aws:states:::sagemaker:createTrainingJob' resource ARN, allowing direct invocation without Lambda wrappers, and supports service integration patterns like '.sync' for waiting on job completion.

KKey Concepts to Remember

  • Read the scenario before looking for a memorised answer.
  • Find the constraint that changes the correct option.
  • Eliminate answers that are true in general but not in this case.

TExam Day Tips

  • Watch for words such as best, first, most likely and least administrative effort.
  • Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.

Quick reference

Cloud Service Model Comparison

ModelYou ManageProvider ManagesExamples
IaaSOS, runtime, apps, dataHardware, hypervisor, networkingEC2, Azure VMs, GCP Compute Engine
PaaSApps and dataOS, runtime, middleware, hardwareElastic Beanstalk, Azure App Service
SaaSData and settings onlyEverything elseMicrosoft 365, Salesforce, Workday
FaaS / ServerlessFunction code onlyInfra, scaling, runtimeLambda, Azure Functions, Cloud Run
CaaSContainers and appsKubernetes, OS, hardwareEKS, AKS, GKE

What to study next

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FAQ

Questions learners often ask

What does this MLA-C01 question test?

Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: 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.

What should I do if I get this MLA-C01 question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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Last reviewed: Jul 4, 2026

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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.