Google Cloud Professional Machine Learning Engineer practice questions
20 questions in 2 tests. Open one to answer it on its own, or take a whole test.
Test 1
Take Test 1 ›- A retailer needs to sort incoming supplier product photos into its own 40-category internal taxonomy. It has 6,000 images already labelled with those categories. The team… · Low-code AI Solutions
- Two teams share a customer-churn model. The data science team builds training sets with a BigQuery query that computes aggregates such as avg_order_value_30d, while the… · Managing Data & Models
- A fraud-detection model scores 0.98 AUC on its held-out test set but performs barely better than chance on live traffic. The training table is built by joining the transaction… · Managing Data & Models
- Your prototype trains a PyTorch model inside an Agent Platform Workbench notebook on a single attached GPU. The model must now be retrained nightly on fresh data as one step of… · Scaling Prototypes
- An ML team trains a large model on an on-premises GPU cluster that, for data residency reasons, cannot be moved to Google Cloud. Each training run takes about nine hours, so… · Scaling Prototypes
- A propensity model must score roughly 60 million customer rows held in BigQuery once per night, with the scores written back to BigQuery before the 06:00 marketing run. No… · Serving & Scaling Models
- Version 4 of a recommendation model has passed offline evaluation, and version 3 is currently serving all production traffic from an Agent Platform endpoint. Before a full… · Serving & Scaling Models
- An MLOps team has written its retraining workflow (data validation, training, evaluation, conditional model registration) as components in the Kubeflow Pipelines SDK. They want a… · ML Pipeline Automation
- A tabular credit-risk model is deployed to an Agent Platform endpoint, and the exact table used to train it is still available in BigQuery. Risk management wants to be alerted… · Monitoring ML Solutions
- A regulated lender must return per-decision reason codes for every application its model declines. The model is a custom-trained, fully differentiable TensorFlow deep neural… · Monitoring ML Solutions
Test 2
Take Test 2 ›- Three years of daily unit-sales history for 2,000 SKUs already sit in a BigQuery table. Supply chain analysts, who write SQL every day but have never used Python, need a 60-day… · Low-code AI Solutions
- Support-ticket text is being prepared as training data for a ticket-routing classifier. The raw tickets contain customer names, email addresses and payment card numbers typed… · Managing Data & Models
- Four data scientists are each tuning the same demand model, recording learning rate, tree depth, feature set and validation RMSE in personal spreadsheets. Nobody can reconstruct… · Managing Data & Models
- A video-understanding model is being moved from a research laptop onto Agent Platform custom training. Its main training loop calls two hand-written CUDA kernels for a custom… · Scaling Prototypes
- A PyTorch model is trained with DistributedDataParallel across five nodes, each with eight GPUs, as an Agent Platform custom training job. Profiling shows the GPUs idle for a… · Scaling Prototypes
- A risk model is implemented in a niche gradient-boosting library that Agent Platform does not ship a prebuilt inference container for, and each request needs a short… · Serving & Scaling Models
- A custom-trained underwriting model must be callable from application VPC networks in three different consumer projects. Security has ruled that inference traffic must never… · Serving & Scaling Models
- A delivery-time model is retrained on a fixed nightly schedule. Two problems have emerged: most nights the fresh model is statistically indistinguishable from the one it… · ML Pipeline Automation
- An ML platform team owns the retraining pipeline's component source. Today a data scientist edits a component, rebuilds its image by hand, recompiles the pipeline locally, and… · ML Pipeline Automation
- A gen AI assistant built on Gemini is about to be opened to external customers. Two risks are blocking launch: testers have made the assistant ignore its system instructions with… · Monitoring ML Solutions