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GCP
Professional Machine Learning Engineer
Topics
caching

Professional Machine Learning Engineer

Build, deploy, and monitor production ML and generative AI solutions on Gemini Enterprise Agent Platform, and track your readiness for the Professional Machine Learning Engineer exam.

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Concept

caching

Topic in 4 exam domains

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caching · Concept
caching · Feature · Get Data Tables action
Caching · Concept · AlloyDB architecture component

A mechanism used to store backend responses to reduce load on backend servers and improve API response times.

Where this appears on the exam

4 subdomains in 3 of 6 domains

Domain 1
Architecting low-code AI solutions
~13% of exam
1. 1.2Building AI solutions using Google Cloud AI APIs or foundational models
  - Building applications using industry-specific APIs (e.g., Document AI API, Vision API, and Translate API)
  - Optimizing Gemini-based applications for cost, latency, and availability
Domain 2
Collaborating within and across teams to manage data and models
~16% of exam
1. 2.1Exploring and preprocessing data for ML
  - Organizing and exploring different data types (e.g., tabular, text, and images) for efficient experimenting, training, and serving
Domain 4
Serving and scaling models
~20% of exam
1. 4.2Scaling online model serving
  - Managing and serving features using Agent Platform Feature Store

Related topics

Part of

AlloyDB architecture
Concept

caching

How learners score on this topic

Last updated November 2, 2026

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