Correct Answer: To detect 'data drift' where the real-world data starts to differ from the training data, impacting accuracy
Model monitoring ensures that your ML models remain accurate over time by detecting changes in data distributions....
Correct Answer
To detect 'data drift' where the real-world data starts to differ from the training data, impacting accuracy
Why This Answer Is Correct
Model monitoring ensures that your ML models remain accurate over time by detecting changes in data distributions.
This question evaluates your knowledge of Amazon SageMaker — a core concept tested in AWS Certified Solutions Architect – Associate (SAA-C03). Understanding Amazon SageMaker is essential for designing scalable, secure, and cost-effective cloud architectures. Mastery of this topic will help you answer scenario-based questions confidently on exam day.
Your data science team needs a fully managed environment to build, train, and deploy custom machine learning models using TensorFlow. Which platform provides these end-to-end MLOps capabilities?
A data science team needs a fully managed service to build, train, and deploy machine learning models at scale. Which service is designed for this?
What is 'Amazon SageMaker Ground Truth' used for?
A team wants to quickly start experimenting with machine learning without configuring their own Jupyter notebooks. Which SageMaker feature provides a web-based IDE for ML?
To reduce costs for training large machine learning models, which EC2 instance feature should a solutions architect recommend using with SageMaker?