Match each ML pipeline component to its description.
Drag a concept onto its matching description — or click a concept then click the description.
Production ML pipeline framework by Google
ML toolkit for Kubernetes-based workflows
Unified stream and batch data processing service
Managed Apache Airflow workflow orchestration
Serverless ML pipeline orchestration on Vertex AI
⚠ Common exam trap
A common trap is confusing model training with model evaluation. Training is the process of learning model parameters, while evaluation measures performance using metrics.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Data ingestion: Collecting and importing raw data from various sources.
The correct matches are: Data ingestion → Collecting and importing raw data from various sources. Data validation → Checking data quality, integrity, and schema compliance. Feature engineering → Transforming raw data into features that improve model performance. The remaining options are incorrect: Model training is not about assessing model performance (that's evaluation); Model evaluation is not about deploying a model (that's deployment); Model deployment is not about transforming raw data into features (that's feature engineering).
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Data ingestion: Collecting and importing raw data from various sources.
Why this is correct
Correct: Data ingestion is the process of collecting and importing raw data.
Data validation: Checking data quality, integrity, and schema compliance.
Why this is correct
Correct: Data validation checks data quality and schema.
Feature engineering: Transforming raw data into features that improve model performance.
Why this is correct
Correct: Feature engineering transforms raw data to improve models.
Model training: The process of assessing model performance using metrics.
Why it's wrong here
Incorrect: This describes model evaluation, not training.
Model evaluation: The process of deploying a model into production.
Why it's wrong here
Incorrect: This describes model deployment, not evaluation.
Model deployment: The process of transforming raw data into features.
Why it's wrong here
Incorrect: This describes feature engineering, not deployment.
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About these practice questions
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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.