Question 326 of 1,000
PMLE Architecting Low-Code ML Solutions Practice Question
This PMLE practice question tests your understanding of architecting low-code ml solutions. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. 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.
A data scientist wants to evaluate the performance of a BigQuery ML classification model on a test dataset. Which function should they use?
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
✓
ML.EVALUATE
ML.EVALUATE is the correct function because it computes classification metrics (e.g., precision, recall, accuracy, F1 score, ROC AUC) directly on a trained BigQuery ML model using a provided test dataset or evaluation input. This is the dedicated function for assessing model performance after training, aligning with the task of evaluating a classification model on held-out test data.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the distinction between prediction (ML.PREDICT) and evaluation (ML.EVALUATE), trapping candidates who confuse generating outputs with measuring performance, especially when the question mentions 'evaluate performance' but the candidate fixates on 'predict' as the primary ML function.
Detailed technical explanation
How to think about this question
Under the hood, ML.EVALUATE applies the trained model to the specified input table and computes metrics using BigQuery's distributed processing, returning a row of values such as precision, recall, accuracy, f1_score, log_loss, and roc_auc (for binary classifiers). A subtle behavior is that for multiclass classification, ML.EVALUATE returns per-class metrics in separate rows, which can be aggregated for overall performance. In a real-world scenario, a data scientist might use ML.EVALUATE after hyperparameter tuning to compare model versions on a consistent test split, ensuring the chosen model generalizes well before deployment.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
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
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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FAQ
Questions learners often ask
What does this PMLE question test?
Architecting Low-Code ML Solutions — This question tests Architecting Low-Code ML Solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: ML.EVALUATE — ML.EVALUATE is the correct function because it computes classification metrics (e.g., precision, recall, accuracy, F1 score, ROC AUC) directly on a trained BigQuery ML model using a provided test dataset or evaluation input. This is the dedicated function for assessing model performance after training, aligning with the task of evaluating a classification model on held-out test data.
What should I do if I get this PMLE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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