Machine Learning Operations (MLOps) Flashcards
7 cards from real AIF-C01 practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Machine Learning Operations (MLOps) flashcards as text
What is the key difference between a shadow deployment and a canary deployment?
Answer: Shadow deployment sends real traffic to the new model but discards its predictions
In shadow deployment, real production requests are mirrored to the new model, but only the existing model's responses are served to users, allowing safe comparison without risk.
Amazon SageMaker Feature Store serves what primary purpose in an MLOps workflow?
Answer: Providing a centralized repository for storing, sharing, and reusing ML features
SageMaker Feature Store provides a centralized, reusable repository of ML features that ensures consistency between training and inference, and enables feature sharing across teams.
What is concept drift in the context of ML model monitoring?
Answer: A change in the underlying relationship between input features and the target variable
Concept drift occurs when the statistical relationship between inputs and outputs changes over time, meaning the patterns the model learned are no longer valid.
In Amazon SageMaker, what is a 'production variant' used for?
Answer: Splitting traffic between multiple model versions on a single endpoint
A production variant allows you to deploy multiple model versions behind a single SageMaker endpoint and control what percentage of traffic each variant receives.
Which SageMaker capability automatically detects bias in training data and model predictions?
Answer: SageMaker Clarify
SageMaker Clarify provides bias detection for both pre-training data and post-training model predictions, as well as feature importance and model explainability reports.
What triggers are commonly used to initiate automatic model retraining in an MLOps pipeline?
Answer: New data availability, performance degradation, or scheduled time intervals
Automated retraining is typically triggered by data drift alerts, model performance falling below a threshold, scheduled time-based intervals, or new labeled data becoming available.
What is the role of SageMaker Experiments in an MLOps workflow?
Answer: Tracking, organizing, and comparing multiple ML training runs and their metrics
SageMaker Experiments records the inputs, parameters, configurations, and results of every training run, enabling teams to reproduce and compare experiments systematically.