Match each MLOps practice to its description.
Drag a concept onto its matching description — or click a concept then click the description.
Continuous integration and deployment for ML pipelines
Track and manage different model iterations
Monitor for changes in data or model performance over time
Schedule or trigger model retraining based on conditions
Compare model versions in production with traffic splitting
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
CI/CD: Automates the processes of building, testing, and deploying ML models.
This matching question requires associating each MLOps practice (CI/CD, Model Monitoring, Data Versioning) with its correct description. The correct matches are: CI/CD automates building, testing, and deploying ML models (option A); Model Monitoring continuously observes model performance and detects data drift (option C); Data Versioning manages and tracks changes to datasets over time (option E). Common mistakes include confusing CI/CD with monitoring or versioning, or assigning model monitoring to data versioning tasks. The wrong options (B, D, F) are incorrect because they swap responsibilities: B wrongly attributes monitoring to CI/CD, D assigns versioning to monitoring, and F assigns CI/CD to versioning.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
CI/CD: Automates the processes of building, testing, and deploying ML models.
Why this is correct
CI/CD (Continuous Integration and Continuous Delivery) automates the pipeline from code changes to deployment, ensuring reliable model releases.
CI/CD: Continuously observes model performance and detects data drift.
Why it's wrong here
Incorrect — this describes Model Monitoring, which tracks real-world performance and drift over time.
Model Monitoring: Continuously observes model performance and detects data drift.
Why this is correct
Model Monitoring involves tracking metrics like accuracy and detecting drift to maintain model reliability.
Model Monitoring: Manages and tracks changes to datasets over time.
Why it's wrong here
Incorrect — this describes Data Versioning, which tracks dataset changes for reproducibility.
Data Versioning: Manages and tracks changes to datasets over time.
Why this is correct
Data Versioning ensures reproducibility by snapshotting and tracking dataset modifications.
Data Versioning: Automates the processes of building, testing, and deploying ML models.
Why it's wrong here
Incorrect — this describes CI/CD, which focuses on the deployment pipeline rather than data tracking.
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About these practice questions
This PMLE question is part of Courseiva's 775-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PMLE practice question is part of Courseiva's free Google Cloud 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 PMLE exam.