You developed a custom model by using Vertex Al to forecast the sales of your company s products based on historical transactional data You…

The scenario requires monitoring for two types of changes: "changes in the feature distributions" (data drift) and "changes in the correlations between the features" (concept drift). Vertex AI Model Monitoring detects data drift by default. To detect concept drift, feature attribution monitoring must be enabled. Given the large volume of prediction requests and the need to minimize cost, the most effective control is the prediction sampling rate. Setting a low prediction-sampling-rate (a value closer to 0) reduces the amount of data logged and analyzed, directly lowering monitoring costs while still providing a representative sample to detect drift.

A. A higher monitoring frequency increases the number of analysis jobs run, which increases, not minimizes, cost.
B. A prediction sampling rate closer to 1 would log and analyze almost all requests, maximizing costs, which is unsuitable for a high-volume scenario.
C. While lowering monitoring frequency reduces cost, reducing the sampling rate is the most direct and impactful method for managing costs with high prediction traffic.

1. Google Cloud Documentation, "Introduction to Vertex AI Model Monitoring": This document explains the types of drift detection. It states, "Feature attribution skew and drift shows you how the attribution of your features has changed between your training data and recent production data. A change in feature attributions might mean that the model is relying on a feature more or less to make its predictions, which can be a signal of concept drift." This supports the need for feature attribution monitoring to detect changes in feature correlations.

2. Google Cloud Documentation, "Configure model monitoring": This page details the configuration parameters for a monitoring job. It defines prediction-sampling-rate as "The fraction of incoming prediction requests that are logged... For example, if you set this parameter to 0.1, then 10% of your requests are logged." This confirms that a low sampling rate reduces the volume of data analyzed.

3. Google Cloud Documentation, "Vertex AI pricing", Model Monitoring section: The pricing model is explicitly stated as being based on the volume of data analyzed. "Model monitoring is priced by the number of predictions analyzed per month." This directly links cost to the number of predictions analyzed, reinforcing that reducing the sampling rate is the primary mechanism for cost control.