- A
Data ingestion: Collecting and importing raw data from various sources.
Correct: Data ingestion is the process of collecting and importing raw data.
- B
Data validation: Checking data quality, integrity, and schema compliance.
Correct: Data validation checks data quality and schema.
- C
Feature engineering: Transforming raw data into features that improve model performance.
Correct: Feature engineering transforms raw data to improve models.
- D
Model training: The process of assessing model performance using metrics.
Why wrong: Incorrect: This describes model evaluation, not training.
- E
Model evaluation: The process of deploying a model into production.
Why wrong: Incorrect: This describes model deployment, not evaluation.
- F
Model deployment: The process of transforming raw data into features.
Why wrong: Incorrect: This describes feature engineering, not deployment.
Vertex AI Pipeline Components — Definitions Matching | Google PMLE Explained
This PMLE practice question tests your understanding of data ingestion. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: data ingestion. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Match each ML pipeline component to its description.
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
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).
Key principle: Data ingestion
Answer analysis
Option-by-option breakdown
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.
Related concept
Data ingestion
- ✓
Data validation: Checking data quality, integrity, and schema compliance.
Why this is correct
Correct: Data validation checks data quality and schema.
Related concept
Data ingestion
- ✓
Feature engineering: Transforming raw data into features that improve model performance.
Why this is correct
Correct: Feature engineering transforms raw data to improve models.
Related concept
Data ingestion
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common trap is confusing model training with model evaluation. Training is the process of learning model parameters, while evaluation measures performance using metrics.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Data ingestion
- Data validation
- Feature engineering
- Model evaluation
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Data ingestion
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Data ingestion Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Review data ingestion, then practise related PMLE questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this PMLE question test?
Data ingestion
What is the correct answer to this question?
The correct answer is: 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).
What should I do if I get this PMLE question wrong?
Review data ingestion, then practise related PMLE questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Data ingestion
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Last reviewed: Jun 11, 2026
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.
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