hardMultiple ChoiceObjective-mapped
PMLE An ML engineer at a large e-commerce company Practice Question
You are an ML engineer at a large e-commerce company. Your team has developed a product recommendation model using TensorFlow and deployed it on Vertex AI Endpoints for real-time inference. The model is retrained weekly using a Vertex AI Pipeline that reads new user interaction data from BigQuery, trains the model, evaluates it, and deploys the new version to the endpoint with a traffic split: 10% to the new model and 90% to the previous champion model. Recently, the team noticed that the new model's online prediction latency has increased significantly (from 50ms to 200ms) after deployment, causing timeouts for some requests. The training code has not changed, and the model size is similar. The pipeline uses a custom container with the same TensorFlow Serving image as before. The deployment step uses the same machine type (n1-standard-4) for the endpoint. What is the most likely cause of the latency increase?
⚠ Common exam trap
Google Cloud often tests the concept that changes in the ML pipeline (like adding a data validation step) can alter the serving signature and increase latency, even when the model architecture and infrastructure remain unchanged, tricking candidates into focusing on hardware or data distribution instead.
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
✓
The pipeline now includes a data validation step that modifies the SavedModel's serving signature, adding an extra preprocessing operation.
The pipeline now includes a data validation step that modifies the SavedModel's serving signature, adding an extra preprocessing operation. This additional operation runs during inference on Vertex AI Endpoints, increasing the per-request latency from 50ms to 200ms, even though the model architecture and size remain unchanged. The custom container and machine type are identical, so the latency increase must stem from a change in the serving graph itself.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The endpoint is using a machine type that is not optimized for the new model's computation.
Why it's wrong here
The machine type is the same as before.
- ✗
The new model has a significantly different architecture that requires more computation.
Why it's wrong here
The training code hasn't changed, so architecture is likely similar.
- ✓
The pipeline now includes a data validation step that modifies the SavedModel's serving signature, adding an extra preprocessing operation.
Why this is correct
A data validation step might have inadvertently added preprocessing ops, increasing latency.
- ✗
The new model is experiencing data skew because the training data distribution has changed.
Why it's wrong here
Data skew affects model accuracy, not latency.
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