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Hardest Google Cloud Professional Machine Learning Engineer questions

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Review question 1

Automl & Vertex AI

Architecting low-code AI solutions
Not ranked

An ML engineer has structured customer data in a BigQuery table and must build a churn-prediction classification model. The team wants automatic model architecture search and hyperparameter tuning with no custom training code, and wants the resulting model to be easily registered and deployed to a Vertex AI endpoint for online predictions. Which approach best meets these requirements?

Correct answer
B. Run a Vertex AI AutoML Tabular training job pointed directly at the BigQuery table, letting Vertex AI perform automatic architecture search and hyperparameter tuning

Vertex AI AutoML Tabular can train directly from a BigQuery source table and performs automatic model architecture search and hyperparameter tuning without any custom training code, and the resulting model registers in Vertex AI Model Registry for straightforward endpoint deployment, matching every stated requirement. Creating a logistic regression model in BigQuery ML with manually specified hyperparameters requires the manual tuning effort the team wants to avoid and performs no architecture search. Writing a custom Keras script for a Vertex AI Training job requires exactly the custom training code the team wants to avoid, along with exporting data out of BigQuery first. A BigQuery ML boosted tree classifier with default hyperparameters uses a fixed algorithm with no automatic architecture search or tuning process, so it does not satisfy the automatic-tuning requirement.

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Review question 2

Experiment Tracking & ML Metadata

Collaborating to manage data and models
Not ranked

Your team is running 15 parallel experiments in Vertex AI to optimize a recommendation model using different feature engineering approaches, loss functions, and hyperparameters. After each experiment, you need to compare metrics (RMSE, AUC, latency), identify the best-performing configuration, and reproduce the exact training run for the winner. What is the most efficient way to track and manage this workflow?

Correct answer
D. Use Vertex AI Experiments with ML Metadata to automatically log parameters, metrics, and artifacts; compare runs in the Vertex AI console; use the logged metadata to reproduce the best run.

Vertex AI Experiments with ML Metadata is purpose-built for tracking, comparing, and reproducing ML experiments at scale. It captures parameters, metrics, and artifacts, provides a unified console for comparison, and records the inputs needed to reproduce the best run. Option D is the only approach that integrates experiment tracking, comparison, and reproducibility in a single platform. Option B (BigQuery approach) requires manual orchestration and lacks built-in reproducibility features. Option C (Sheets) is not scalable for 15 parallel runs and lacks integration with model artifacts. Option A (Airflow) is for orchestration, not experiment comparison.

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Review question 3

Hyperparameter Tuning & Foundation Models

Scaling prototypes into ML models
Not ranked

Your team is fine-tuning a large language model on Vertex AI for customer support ticket classification. Training on the full dataset takes 8 hours and costs $1,200. You want to experiment with different hyperparameters (learning rate, batch size, number of epochs) to improve F1-score. What is the most cost-effective approach to tune the model while maintaining reproducibility?

Correct answer
C. Use Vertex AI's Hyperparameter Tuning service with a search space for learning rate and batch size, with early stopping enabled to reduce wasted compute.

Vertex AI's Hyperparameter Tuning service automates the search for optimal hyperparameters, uses early stopping to terminate unpromising trials, and runs trials in parallel to reduce overall time and cost compared to manual sequential runs. Manual sequential runs waste compute and are error-prone. Training on a small sample does not guarantee hyperparameters generalize to the full dataset. A/B testing in production is unsafe for model tuning and introduces production risk.

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