MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A data science team wants to automate the retraining of a model when data drift is detected. Which TWO AWS services should they use in combination to achieve this? (Choose TWO)
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
✓
SageMaker Model Monitor
SageMaker Model Monitor (C) is the correct service for detecting data drift: it continuously monitors a deployed model's endpoint, compares incoming inference data against a baseline, and can emit CloudWatch metrics/alerts when drift (or data quality, bias, or feature attribution issues) is detected. AWS Lambda (D) is the correct companion service because it can be triggered by those CloudWatch alarms/events to run the retraining logic — for example, invoking a SageMaker training job or pipeline to retrain and redeploy the model automatically. Together they form the detect-then-retrain automation loop the team needs. AWS Cloud9 (A) is only a cloud IDE and provides no monitoring or automation capability. Amazon DynamoDB (B) is a NoSQL database and does not detect drift or orchestrate retraining. Amazon Kinesis Data Analytics (E) is for real-time stream processing with SQL/Flink, not for model drift detection or triggering retraining workflows.
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 Cloud9
Why it's wrong here
Cloud9 is a browser-based IDE for writing and debugging code; it neither monitors drift nor orchestrates retraining workflows. It is tempting because engineers do write and test model code there, and it would be the right choice when a team needs a cloud development environment with terminal access.
- ✗
Amazon DynamoDB
Why it's wrong here
DynamoDB stores application data as a key-value or document database; it cannot detect statistical drift or trigger retraining pipelines. It is tempting because it can persist model metadata or inference records, and would be the right choice when a low-latency store for predictions or feature lookups is required.
- ✓
SageMaker Model Monitor
Why this is correct
SageMaker Model Monitor evaluates endpoint data against baselines and emits CloudWatch metrics when drift is detected, providing the detection half of the automation. Pairing it with an action service such as Lambda completes the retraining trigger.
- ✓
AWS Lambda
Why this is correct
Lambda provides the compute that reacts to a drift event and programmatically starts the retraining job or pipeline. Paired with an event source such as EventBridge or CloudWatch Alarms, it satisfies the requirement to automate retraining when data drift is detected, without provisioning persistent infrastructure.
- ✗
Amazon Kinesis Data Analytics
Why it's wrong here
Kinesis Data Analytics performs SQL or Apache Flink stream processing, not drift detection or pipeline orchestration. It is tempting because it handles real-time streaming data, and would be correct when continuous transformations or windowed aggregations over incoming event streams are needed before storage.
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.