PMLE Architecting low-code ML solutions Practice Test 2 — 15 Questions

Question 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?

Accepted Answer

Enable 'Enable weighted evaluation' and set the optimization objective to 'Maximize recall at a specific recall@P%' with a target precision of 0.5.. 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.

Answer

Manually split the data into a stratified train/test set to ensure the same proportion of defaults in each.

Answer

Train multiple models with different algorithms (e.g., XGBoost, Random Forest) and blend them using a custom script.

Answer

Under-sample the majority class to create a balanced dataset and retrain.

Question 2

A retail company wants to build a customer churn prediction model using AutoML Tables. They have a dataset with 5000 rows and 50 features, including customer ID, transaction history, and support tickets. The target is a binary column 'churned'. After training, the model shows high accuracy but low recall for the churned class. What is the most likely cause?

Accepted Answer

The churned class is underrepresented, causing the model to favor the majority class.. Option C is correct because in imbalanced datasets, AutoML Tables optimizes for overall accuracy, which can be high if the majority class dominates. Low recall for the churned class indicates the model predicts most instances as non-churned, a classic symptom of class imbalance. AutoML Tables provides class weighting and sampling options to mitigate this, but without them, the model favors the majority class.

Answer

The dataset is too small for AutoML to train effectively.

Answer

The features are not normalized, leading to biased predictions.

Answer

The dataset includes a unique customer ID feature, causing overfitting.

Question 3

Drag and drop the steps to set up data lineage tracking for ML pipelines using Vertex AI Experiments in the correct order.

Question 4

Match each model evaluation metric to its use case.

Question 5

A marketing team wants to analyze customer reviews for sentiment without writing code. Which Google Cloud service should they use?

Accepted Answer

Cloud Natural Language API. The Cloud Natural Language API (option D) is the correct choice because it provides pre-trained models for sentiment analysis, entity recognition, and syntax analysis via a simple REST API, requiring no code beyond sending HTTP requests. This aligns perfectly with the requirement to analyze customer reviews for sentiment without writing code, as the API abstracts all ML complexity.

Answer

Cloud Dataflow

Answer

Vertex AI Workbench

Answer

BigQuery ML

Question 6

A company uses AutoML Tables to predict customer churn. The model's AUC is low. Which action is most likely to improve performance?

Accepted Answer

Add more training data. Adding more training data often helps improve model performance. Increasing the training budget alone may not help if data is insufficient. Removing features with low importance could hurt. Changing the optimization objective may not directly improve AUC.

Answer

Use a different optimization objective

Answer

Increase the training budget to 10 hours

Answer

Remove features with low importance

Question 7

A data engineering team wants to orchestrate an ML pipeline that includes data preprocessing in Dataflow, AutoML training, and model deployment. They want to minimize operational overhead. Which approach is best?

Accepted Answer

Use Vertex AI Pipelines with custom components. Vertex AI Pipelines with custom components is the best choice because it provides a fully managed, serverless orchestration service that natively integrates with Dataflow, AutoML, and model deployment. This minimizes operational overhead by eliminating the need to manage infrastructure, handle retries, or maintain a separate orchestration server, while offering built-in artifact tracking and pipeline caching.

Answer

Use Cloud Composer with Apache Airflow DAG

Answer

Use AI Platform Training with script

Answer

Use Cloud Scheduler to trigger Cloud Functions

Question 8

A company wants to classify support ticket text into categories. They have labeled historical tickets. Which Google Cloud service allows them to train a custom classification model with no code?

Accepted Answer

AutoML Natural Language. AutoML Natural Language (now part of Vertex AI) is the correct service because it enables users to train custom text classification models using labeled data without writing any code. It provides a no-code interface for uploading datasets, training models, and evaluating performance, making it ideal for classifying support ticket text into custom categories.

Answer

Vertex AI Matching Engine

Answer

Cloud Natural Language API

Answer

Document AI

Question 9

A retailer uses BigQuery ML to build a linear regression model for sales forecasting. The model's evaluation shows high RMSE. Which step should they take first?

Accepted Answer

Examine the data for outliers and missing values. High RMSE in a linear regression model often indicates issues with data quality, such as outliers or missing values, which can disproportionately skew the model's predictions. BigQuery ML's linear regression is sensitive to such anomalies, so examining and cleaning the data is the most appropriate first step before considering model complexity or feature engineering.

Answer

Use a more complex model like XGBoost

Answer

Increase the number of features

Answer

Set a larger training budget

Question 10

A team is using Vertex AI AutoML to train a forecasting model. They need to retrain the model weekly and only if the new week's data significantly changes the data distribution. What is the most efficient way to achieve this?

Accepted Answer

Use Vertex AI Model Monitoring to detect drift and trigger a pipeline. Option C is correct because Vertex AI Model Monitoring can be configured to detect data drift on the model's input features, and when drift exceeds a threshold, it can trigger a Cloud Function or a Vertex AI pipeline to retrain the model. This approach avoids unnecessary retraining when the data distribution has not changed significantly, which is more efficient than always retraining. The integration with Cloud Functions or Pub/Sub allows for a serverless, event-driven retraining pipeline that only runs when needed.

Answer

Use a scheduled pipeline that always retrains

Answer

Use Cloud Monitoring alerts on data drift to trigger retraining

Answer

Use Cloud Functions on schedule to compare distributions

Question 11

A non-technical user wants to build a binary classification model using Vertex AI. Which UI should they use?

Accepted Answer

Vertex AI AutoML. Vertex AI AutoML is the correct choice because it provides a no-code graphical user interface specifically designed for non-technical users to build, train, and deploy machine learning models, including binary classification models, without writing any code. It automates the entire ML pipeline—feature engineering, model selection, hyperparameter tuning—allowing users to simply upload labeled data and get a production-ready model.

Answer

Vertex AI Workbench

Answer

Vertex AI Pipelines

Answer

Vertex AI Prediction

Question 12

A company deploys an AutoML Vision model for real-time defect detection. They notice high inference latency during peak hours. Which configuration change can help?

Accepted Answer

Increase the number of max replicas. Increasing the number of max replicas allows the AutoML Vision endpoint to scale horizontally during peak hours, distributing the inference load across more compute instances. This directly reduces per-request latency by preventing queuing and resource contention, as the Vertex AI Prediction service can spin up additional replicas up to the configured maximum to handle higher throughput.

Answer

Reduce the model's input resolution

Answer

Use batch prediction

Answer

Enable model compression

Question 13

An organization uses Vertex AI Pipelines to automate a model training workflow. They want to reuse previously trained models if the data hasn't changed. Which pipeline component best achieves this?

Accepted Answer

Use a conditional component that checks data hash. Conditional components can check data changes and skip training if unchanged. Caching exists but not for whole pipeline; Cloud Function check is external; Artifact Registry stores models but doesn't decide to retrain.

Answer

Use a caching mechanism in Vertex AI Pipelines

Answer

Use a Cloud Function to check BigQuery update time

Answer

Use Artifact Registry to store model versions

Question 14

A data analyst wants to use low-code ML to analyze text data. Which TWO Google Cloud services are appropriate?

Accepted Answer

Cloud Natural Language API. Cloud Natural Language API is a low-code ML service that provides pre-trained models for analyzing text, including sentiment analysis, entity recognition, and syntax analysis, without requiring custom model training. It is appropriate for a data analyst who wants to quickly extract insights from text data using simple API calls.

Answer

Vertex AI Workbench

Answer

Document AI

Answer

BigQuery ML for sentiment

Question 15

A company uses Vertex AI for AutoML training. Which THREE are best practices for managing model versions?

Accepted Answer

Use Vertex AI Model Registry to version models. Vertex AI Model Registry is the central repository for managing and versioning models, allowing you to track iterations, compare performance, and control deployments. It provides a structured way to organize models, roll back to previous versions if needed, and maintain lineage for compliance and reproducibility.

Answer

Deploy each model version to a separate endpoint

Answer

Automatically delete old versions after 30 days