MLA-C02 deployment and orchestration
Deployment and Orchestration of ML and AI Workflows is worth 24% of MLA-C02. AWS frames it as choosing deployment infrastructure and endpoints, provisioning compute, configuring auto scaling, setting up CI/CD to automate workflows, and building agentic workflows.
This is the domain where the exam’s cost instinct is strongest. Nearly every question offers a technically workable answer that costs more than necessary.
Choosing an inference approach
The most tested decision in the domain. Match the pattern to the requirement:
| Approach | Use when | Cost shape |
|---|---|---|
| Real-time endpoint | Low latency, continuous traffic | Pay while provisioned |
| Serverless inference | Intermittent, unpredictable traffic | Pay per request, cold starts |
| Asynchronous inference | Large payloads, long processing, latency tolerant | Queued, scales to zero |
| Batch inference | Large volumes on a schedule, latency irrelevant | Cheapest per record |
The decision rule the exam expects: if latency does not matter, do not pay for a persistent endpoint. A nightly scoring job is batch. A sporadic internal tool is serverless. A customer-facing API is real time.
Provisioning and auto scaling
- Right-sizing instances, and when a GPU is actually justified.
- Auto scaling on a meaningful metric, so capacity follows demand.
- Scaling to zero where the pattern allows it.
- Multi-model or multi-container hosting when many small models would each waste an endpoint.
CI/CD for ML
ML pipelines are software pipelines with extra stages:
- Source control for code and for data and model versions.
- Automated build, test and deploy.
- Infrastructure as code so environments are reproducible.
- Deployment strategies that reduce risk: staged rollout, canary, shadow traffic, and the ability to roll back.
- Automated retraining triggered by a schedule or by drift detection.
The distinctive ML addition: your pipeline must handle a model artefact as a deployable thing with its own versioning and approval, not just code.
Agentic workflows
Explicit in C02’s task list and new relative to its predecessor. At this level:
- An agent uses tools and takes multi-step actions rather than returning a single response.
- Orchestration means coordinating those steps, handling failures and managing state.
- Because agents act, consequential actions need approval controls — a human in the loop for anything irreversible.
- Observability matters more, not less: you need to see which steps ran and why.
Sample questions
Question 1. An internal tool calls a model perhaps 30 times a day, at unpredictable intervals. The team wants to minimise cost and does not object to occasional slow first responses. Which deployment fits?
- A. A real-time endpoint with provisioned capacity
- B. Serverless inference
- C. A nightly batch inference job
- D. An always-on GPU endpoint
Show answer
Answer: B
Serverless inference bills per request and scales to zero between calls, which suits low and unpredictable volume where cold starts are acceptable. A provisioned real-time endpoint bills continuously for idle capacity, batch does not suit interactive use, and an always-on GPU endpoint is the most expensive option.
Question 2. A team wants to release a new model version while limiting exposure if it behaves badly, with the ability to revert quickly. Which approach is most appropriate?
- A. Replace the existing endpoint with the new version directly
- B. Deploy to a larger instance type to absorb any problems
- C. Test thoroughly in development and then deploy to production
- D. A canary rollout sending a small share of traffic to the new version, with rollback
Show answer
Answer: D
A canary or staged rollout sends a small share of traffic to the new version first and allows a fast revert, which is exactly what limiting exposure with quick rollback requires. Replacing the endpoint outright removes the safety net, a larger instance is unrelated, and testing only in development does not limit production exposure.
Question 3. An agentic workflow can issue refunds as one of its tool actions. What control should the deployment include?
- A. A human approval step before the refund action executes
- B. Logging every refund to CloudWatch for later review
- C. Automatic retry if the refund action fails
- D. A more capable foundation model behind the agent
Show answer
Answer: A
An irreversible financial action requires a human approval step before execution, which is the standard control for consequential agent actions. Logging records what happened after the fact, retries repeat the action, and a larger model does not make the action safe.
What to practise
Deploy the same model three ways — real-time, serverless and batch — and look at what each costs when idle. That comparison makes the inference-selection questions immediate, and they are the most common question shape in this domain.