Google AI-Powered Shopping Ads study plan

Map the full Shopping ads campaign structure: Merchant Center account, product feed, linked Google Ads account, and the Standard vs. Smart Shopping distinction as it exists today

Define every required and recommended product feed attribute (id, title, description, link, image_link, availability, price, brand, gtin, condition, google_product_category, product_type) and explain what each one drives

Diagram the end-to-end flow from feed upload through product approval to ad serving, labeling each failure point

Write a one-page glossary of Shopping-specific terms (product group, offer ID, item-level bidding, disapproved product, feed rule)

Diagnose the ten most common product disapproval and warning reasons and state the corrective action for each

Explain how feed quality signals (title structure, image quality, completeness, price/availability accuracy) influence eligibility and performance

Describe how to use feed rules and supplemental feeds to fix attributes at scale without re-uploading the whole file

Compare automated feed vs. scheduled fetch vs. Google Sheets upload and pick the right method for three given scenarios

Explain how product groups are built from the feed hierarchy and how to subdivide them for bidding control

Distinguish campaign-level, ad-group-level, and product-group-level bidding, and describe when each applies

Interpret Shopping reporting dimensions (product, brand, category, offer ID) and connect them to optimization decisions

Calculate the effect of a bid change on impression share and ROAS using a worked example with sample numbers

Describe how AI-driven Shopping campaign types differ in structure, bidding control, and reporting granularity

Explain how asset groups, headlines, descriptions, and images combine with feed data in AI-powered formats

Identify which levers a marketer still controls when automation handles bidding and creative assembly

Outline a testing approach to compare an automated campaign against a manually structured one using a controlled split

Explain how audience signals and first-party data feed AI-powered Shopping campaigns and what each signal type changes

Describe how Performance Max-style automation uses feed and asset inputs, and where Shopping inventory fits within it

Interpret new-to-brand, conversion value, and asset-level reporting to judge whether automation is working

Identify the guardrails (brand exclusions, negative keywords, account-level exclusions) that keep automated spend controlled

Set up conversion tracking requirements for Shopping: conversion actions, values, currency, and attribution model implications

Explain how to diagnose a campaign that has impressions but no conversions using a structured funnel check

Describe how to use Merchant Center diagnostics alongside Ads reporting to separate feed problems from bidding problems

Draft the diagnostic questions to ask when ROAS drops week over week

Review all prior weeks and close gaps by re-deriving key concepts from memory without notes

Practice interpreting scenario-based questions that describe a merchant situation and ask for the best next action

Compare pairs of similar concepts (feed rule vs. supplemental feed, product group vs. asset group, warning vs. disapproval) and state the distinguishing feature

Time yourself on a mixed set of practice scenarios and record accuracy by subtopic

Consolidate the weakest subtopics from week 7 into a final review sheet of no more than two pages

Rehearse explaining the full Shopping ads lifecycle aloud in under five minutes

Re-attempt every previously missed scenario and confirm each is now answered correctly with reasoning

Plan post-exam next steps for applying Shopping ads knowledge in a live account