easyMultiple Select
PDE Practice Question: Which TWO are benefits of using Vertex AI…
Which TWO are benefits of using Vertex AI Endpoints for model serving?
⚠ Common exam trap
Google Cloud often tests the distinction between features that are 'built-in' versus those that require separate services or additional configuration, so candidates mistakenly assume batch prediction or automatic retraining are part of Endpoints when they are actually separate Vertex AI components.
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
✓
Integrated monitoring for prediction latency and error rates.
Option B is correct because Vertex AI Endpoints provide integrated monitoring that surfaces prediction latency and error rates, letting you track online serving health and performance directly in the console. Option C is correct because Vertex AI Endpoints support automatic scaling (autoscaling) based on traffic, adjusting the number of deployed model replicas to match request load. Options A, D, and E are not benefits of Endpoints: batch prediction is a separate Vertex AI feature (Batch Prediction jobs) rather than an Endpoint capability, automatic retraining on drift is not a built-in Endpoint function, and A/B testing requires explicit traffic-split configuration rather than working with no additional setup.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Batch prediction support out of the box.
Why it's wrong here
Endpoints are designed for online, low-latency request-response serving; batch prediction is performed by submitting a batch prediction job against a model, without an endpoint. It is tempting because batch prediction is a real Vertex AI capability, but it is a distinct job type rather than an endpoint benefit.
- ✓
Integrated monitoring for prediction latency and error rates.
Why this is correct
Vertex AI Endpoints provide built-in Cloud Monitoring metrics covering prediction latency, error rates and request counts per deployed model, satisfying the stem's requirement for serving benefits. This native observability removes the need to instrument custom telemetry, letting operators detect degradation and trigger autoscaling responses directly from the endpoint's own dashboards.
- ✓
Automatic scaling based on traffic.
Why this is correct
Vertex AI Endpoints scale replicas automatically in response to incoming traffic, satisfying the stem's requirement for a serving benefit. This removes manual capacity provisioning, so the deployed model handles demand spikes without intervention while scaling down during quiet periods to control cost.
- ✗
Automatic model retraining when drift is detected.
Why it's wrong here
Vertex AI Endpoints serve predictions from deployed models; retraining triggered by drift detection is handled by Vertex AI Model Monitoring plus pipelines, not by the endpoint itself. It is tempting because drift-triggered retraining is a genuine MLOps goal, achievable through those separate monitoring and pipeline components.
- ✗
Built-in support for A/B testing without any additional configuration.
Why it's wrong here
Traffic splitting across multiple deployed models on an endpoint requires explicit configuration, such as assigning traffic percentages between model versions; it is not automatic. It is tempting because endpoints do support A/B testing through traffic split, but that still demands deliberate setup rather than working out of the box.
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