- A
Retrain the model with adjusted class weights in AutoML Tables to increase recall, then deploy using Vertex AI Prediction with autoscaling enabled.
AutoML Tables supports class weights to handle imbalance, improving recall. Vertex AI Prediction with autoscaling dynamically adjusts resources to maintain latency during spikes and control costs.
- B
Use BigQuery ML to create a logistic regression model with class weights, then deploy it on Cloud Run with maximum concurrency.
Why wrong: Logistic regression may not capture complex fraud patterns, potentially still having low recall. Cloud Run may not handle 5,000 QPS with <100ms latency consistently due to cold starts and concurrency limits.
- C
Export the AutoML Tables model as a TensorFlow SavedModel and deploy it on Vertex AI Prediction with a larger machine type and increased min replicas.
Why wrong: Exporting the model retains the original training settings, so recall would still be low. Larger machines and min replicas increase cost without guaranteeing latency during spikes, and do not address the model's bias toward the majority class.
- D
Use Vertex AI Workbench to manually tune a deep neural network with class imbalance techniques, then deploy as a custom container on App Engine.
Why wrong: Manual tuning contradicts the low-code approach and requires significant effort. App Engine is not optimized for ML inference serving and may introduce high latency and scaling challenges.
Quick Answer
The correct answer is to retrain the model with adjusted class weights in AutoML Tables to increase recall, then deploy using Vertex AI Prediction with autoscaling enabled. This approach directly addresses handling class imbalance in AutoML Tables by assigning a higher penalty to misclassifications of the minority fraud class, which forces the model to improve recall without sacrificing the high precision already achieved. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding that AutoML Tables supports class weight adjustments as a built-in mechanism for imbalanced datasets, and that serving optimization requires autoscaling to handle throughput spikes while maintaining sub-100ms latency. A common trap is assuming you must switch to a custom model or manually resample the data, but AutoML Tables handles this natively. Memory tip: “Weight the minority, scale the majority”—adjust class weights for recall, then enable autoscaling for throughput.
PMLE Architecting low-code ML solutions Practice Question
This PMLE practice question tests your understanding of architecting low-code ml solutions. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 company is using AutoML Tables to build a fraud detection model. The dataset has 10 million rows with 100 features, heavily imbalanced (fraud cases 0.1%). They used AutoML Tables with default settings and achieved high precision but very low recall. They need to deploy the model for real-time scoring on a Vertex AI Endpoint. The model will be used by a transaction processing system that requires low latency (<100 ms per prediction) and high throughput. The team is concerned about cost as the endpoint will receive up to 5,000 predictions per second. After deploying the model, they notice that the endpoint's latency occasionally spikes to over 1 second during peak hours. The team wants to optimize both model performance (recall) and serving performance. Which course of action should they take?
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
Retrain the model with adjusted class weights in AutoML Tables to increase recall, then deploy using Vertex AI Prediction with autoscaling enabled.
Option A is correct because AutoML Tables allows adjusting class weights to handle imbalanced datasets, which directly addresses the low recall issue by penalizing misclassifications of the minority class more heavily. Deploying on Vertex AI Prediction with autoscaling ensures the endpoint can handle up to 5,000 predictions per second while maintaining low latency, as autoscaling dynamically adjusts resources based on traffic, preventing spikes during peak hours.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Retrain the model with adjusted class weights in AutoML Tables to increase recall, then deploy using Vertex AI Prediction with autoscaling enabled.
Why this is correct
AutoML Tables supports class weights to handle imbalance, improving recall. Vertex AI Prediction with autoscaling dynamically adjusts resources to maintain latency during spikes and control costs.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use BigQuery ML to create a logistic regression model with class weights, then deploy it on Cloud Run with maximum concurrency.
Why it's wrong here
Logistic regression may not capture complex fraud patterns, potentially still having low recall. Cloud Run may not handle 5,000 QPS with <100ms latency consistently due to cold starts and concurrency limits.
- ✗
Export the AutoML Tables model as a TensorFlow SavedModel and deploy it on Vertex AI Prediction with a larger machine type and increased min replicas.
Why it's wrong here
Exporting the model retains the original training settings, so recall would still be low. Larger machines and min replicas increase cost without guaranteeing latency during spikes, and do not address the model's bias toward the majority class.
- ✗
Use Vertex AI Workbench to manually tune a deep neural network with class imbalance techniques, then deploy as a custom container on App Engine.
Why it's wrong here
Manual tuning contradicts the low-code approach and requires significant effort. App Engine is not optimized for ML inference serving and may introduce high latency and scaling challenges.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that exporting a managed model to a custom format (like TensorFlow SavedModel) and deploying on a larger machine type is the best way to optimize serving performance, when in fact autoscaling and class weight adjustments within the managed service are the correct low-code approach.
Detailed technical explanation
How to think about this question
AutoML Tables uses gradient-boosted trees and neural architecture search under the hood; adjusting class weights modifies the loss function to give higher weight to the minority class, which improves recall without requiring manual feature engineering. Vertex AI Prediction's autoscaling uses a target CPU utilization metric (default 60%) to add or remove replicas, and the endpoint can be configured with a minimum number of replicas to handle baseline traffic, while the maximum replicas cap costs—critical for 5,000 QPS where latency spikes often occur due to insufficient compute during sudden load bursts.
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
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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: Retrain the model with adjusted class weights in AutoML Tables to increase recall, then deploy using Vertex AI Prediction with autoscaling enabled. — Option A is correct because AutoML Tables allows adjusting class weights to handle imbalanced datasets, which directly addresses the low recall issue by penalizing misclassifications of the minority class more heavily. Deploying on Vertex AI Prediction with autoscaling ensures the endpoint can handle up to 5,000 predictions per second while maintaining low latency, as autoscaling dynamically adjusts resources based on traffic, preventing spikes during peak hours.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more ways this is tested on PMLE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A financial services company uses Vertex AI AutoML Tables to build a credit risk model. The dataset contains 500,000 rows and 50 features, including loan amount, credit score, debt-to-income ratio, and employment length. The target variable is binary: 'default' (1) or 'no default' (0). The data is highly imbalanced, with only 2% defaults. The data scientist trains a model with AutoML Tables using default settings. The evaluation metrics show an AUC of 0.85, but the confusion matrix reveals that the model predicts 'no default' for almost all cases, missing most defaults. The data scientist needs to improve the model's ability to identify defaults without significantly increasing false positives. They have limited time and cannot write custom code. What should they do?
medium- A.Manually split the data into a stratified train/test set to ensure the same proportion of defaults in each.
- B.Train multiple models with different algorithms (e.g., XGBoost, Random Forest) and blend them using a custom script.
- ✓ C.Enable 'Enable weighted evaluation' and set the optimization objective to 'Maximize recall at a specific recall@P%' with a target precision of 0.5.
- D.Under-sample the majority class to create a balanced dataset and retrain.
Why C: Option C is correct because AutoML Tables allows you to set a custom optimization objective to handle class imbalance without custom code. By enabling weighted evaluation and setting the objective to 'Maximize recall at a specific recall@P%' with a target precision of 0.5, the model will be tuned to prioritize identifying defaults (recall) while maintaining a specified precision level, directly addressing the need to catch more defaults without a massive increase in false positives.
Last reviewed: Jun 30, 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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