- A
Use SageMaker Model Monitor to collect drift metrics, and have a data scientist manually analyze the metrics and trigger retraining via the SageMaker console
Why wrong: Manual process contradicts automatic requirement.
- B
Use SageMaker Model Monitor to trigger an Amazon EventBridge event that starts a SageMaker Pipeline, which retrains the model, registers it in the Model Registry, and then updates the existing endpoint with a new production variant
EventBridge triggers pipeline on drift; pipeline retrains, registers, and uses production variant to shift traffic gradually with no downtime.
- C
Schedule a daily SageMaker Pipeline that retrains the model and deploys it using a new endpoint, then updates the application to point to the new endpoint
Why wrong: Scheduled retraining doesn't react to drift; updating application endpoint may cause downtime if not careful.
- D
Use SageMaker Model Monitor to publish drift metrics to Amazon CloudWatch, and create a CloudWatch alarm that triggers an AWS Lambda function to retrain and deploy the model
Why wrong: Lambda can trigger retraining, but deploying a new model with no downtime requires more orchestration; Lambda may not handle complex pipeline.
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. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
A company has a SageMaker endpoint running a model that provides real-time recommendations. Recently, the model's accuracy has degraded due to data drift. The team wants to automatically retrain the model when a drift metric exceeds a threshold and deploy the new model without downtime. Which architecture should the team implement?
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
Use SageMaker Model Monitor to trigger an Amazon EventBridge event that starts a SageMaker Pipeline, which retrains the model, registers it in the Model Registry, and then updates the existing endpoint with a new production variant
Option B is correct because it uses SageMaker Model Monitor to detect data drift and emit an EventBridge event, which triggers a SageMaker Pipeline to retrain the model, register it in the Model Registry, and then update the existing endpoint with a new production variant. This architecture enables automatic retraining and zero-downtime deployment by leveraging the endpoint's production variants for a blue/green deployment.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use SageMaker Model Monitor to collect drift metrics, and have a data scientist manually analyze the metrics and trigger retraining via the SageMaker console
Why it's wrong here
Manual process contradicts automatic requirement.
- ✓
Use SageMaker Model Monitor to trigger an Amazon EventBridge event that starts a SageMaker Pipeline, which retrains the model, registers it in the Model Registry, and then updates the existing endpoint with a new production variant
Why this is correct
EventBridge triggers pipeline on drift; pipeline retrains, registers, and uses production variant to shift traffic gradually with no downtime.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Schedule a daily SageMaker Pipeline that retrains the model and deploys it using a new endpoint, then updates the application to point to the new endpoint
Why it's wrong here
Scheduled retraining doesn't react to drift; updating application endpoint may cause downtime if not careful.
- ✗
Use SageMaker Model Monitor to publish drift metrics to Amazon CloudWatch, and create a CloudWatch alarm that triggers an AWS Lambda function to retrain and deploy the model
Why it's wrong here
Lambda can trigger retraining, but deploying a new model with no downtime requires more orchestration; Lambda may not handle complex pipeline.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between automatic drift-triggered retraining with zero-downtime deployment (Option B) versus scheduled retraining or manual intervention, and candidates may overlook the need to update the existing endpoint rather than creating a new one.
Detailed technical explanation
How to think about this question
SageMaker Model Monitor captures inference data and computes drift metrics against a baseline; when a metric exceeds a threshold, it publishes an event to Amazon EventBridge. The EventBridge rule triggers a SageMaker Pipeline that retrains the model, registers it in the Model Registry, and then uses the UpdateEndpoint API to add a new production variant. The endpoint shifts traffic gradually using the variant's initial weight, enabling zero-downtime deployment. In a real-world scenario, this pattern is critical for high-availability recommendation systems where even seconds of downtime can impact revenue.
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.
What to study next
Got this wrong? Here's your next step.
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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: Use SageMaker Model Monitor to trigger an Amazon EventBridge event that starts a SageMaker Pipeline, which retrains the model, registers it in the Model Registry, and then updates the existing endpoint with a new production variant — Option B is correct because it uses SageMaker Model Monitor to detect data drift and emit an EventBridge event, which triggers a SageMaker Pipeline to retrain the model, register it in the Model Registry, and then update the existing endpoint with a new production variant. This architecture enables automatic retraining and zero-downtime deployment by leveraging the endpoint's production variants for a blue/green deployment.
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: Jun 30, 2026
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