The answer is to use Amazon SageMaker Pipelines with a scheduled Lambda function to trigger retraining daily. This is the most efficient approach because SageMaker Pipelines provides a fully managed orchestration service that natively sequences steps for data processing, training, and model registration, while a scheduled Lambda function acts as the cron trigger to initiate the pipeline on a daily cadence without any manual intervention. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of MLOps automation patterns, specifically how to combine serverless triggers with SageMaker’s native pipeline orchestration to handle time-series models that require frequent updates. A common trap is choosing a manual retraining script or a simple SageMaker training job without orchestration, which lacks artifact tracking, step dependencies, and model registry integration. Memory tip: think “Lambda launches the pipeline, Pipelines handles the process” to remember the separation of trigger and orchestration responsibilities.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. 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.
A machine learning team is deploying a time-series forecasting model using Amazon SageMaker. The model is trained on historical data and needs to be updated daily with new data. The team wants to automate the retraining pipeline and avoid manual intervention. Which approach is the most efficient?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Amazon SageMaker Pipelines with a scheduled Lambda function to trigger retraining daily.
Option D is correct because Amazon SageMaker Pipelines provides a fully managed, end-to-end orchestration service for building, training, and deploying machine learning models. By combining it with a scheduled AWS Lambda function, the team can automate daily retraining without manual intervention, leveraging SageMaker's native integration for step sequencing, artifact tracking, and model registry updates.
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 AWS Step Functions to orchestrate retraining, but require a manual approval step.
Why it's wrong here
The manual approval step defeats the purpose of full automation.
✗
Use SageMaker training jobs manually triggered by the team each day.
Why it's wrong here
Manual triggering is not automated and requires human intervention.
✗
Use a cron job on an EC2 instance to run a training script.
Why it's wrong here
Requires managing EC2 instances and does not leverage SageMaker's managed training.
✓
Use Amazon SageMaker Pipelines with a scheduled Lambda function to trigger retraining daily.
Why this is correct
Combines SageMaker Pipelines for automated ML workflows with Lambda for scheduling, providing a fully automated solution.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates might choose Option C (cron job on EC2) because it seems simpler, but they overlook the operational burden of managing EC2 and the lack of native SageMaker integration for model lineage and automated deployment.
Detailed technical explanation
How to think about this question
SageMaker Pipelines uses a directed acyclic graph (DAG) of steps (e.g., processing, training, evaluation, registration) that can be parameterized and triggered via the AWS SDK or Lambda. The Lambda function can invoke the pipeline execution using the `start_pipeline_execution` API, and the pipeline can automatically fetch the latest daily data from an S3 prefix or a Glue/Athena query. Under the hood, SageMaker Pipelines manages retry logic, parallel execution, and artifact lineage in Amazon S3, which is critical for auditability in regulated industries.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Amazon SageMaker Pipelines with a scheduled Lambda function to trigger retraining daily. — Option D is correct because Amazon SageMaker Pipelines provides a fully managed, end-to-end orchestration service for building, training, and deploying machine learning models. By combining it with a scheduled AWS Lambda function, the team can automate daily retraining without manual intervention, leveraging SageMaker's native integration for step sequencing, artifact tracking, and model registry updates.
What should I do if I get this MLS-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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This MLS-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 MLS-C01 exam.
Question Discussion
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