Which table would you leverage for a query when your objective is to understand how your current audience strategy is performing for your campaigns?
amazon_attributed_events_by_conversion_time
conversions_with_relevance
dsp_impressions_by_matched_segments
Correct Answer
dsp_impressions_by_matched_segments
Topics in this question
About the Amazon Marketing Cloud Certification
The Amazon Marketing Cloud Certification covers Amazon's clean-room analytics environment: how AMC data is structured, writing SQL queries against it, and turning results such as overlap and path-to-conversion analysis into audience and media decisions.
Exam guide and all 95 Amazon Marketing Cloud questions →Related Amazon Marketing Cloud questions
- 1Which table should you use in a query meant to help you choose new Amazon audiences that may benefit your Amazon DSP campaign performance?
- 2Rule-based audience creation leverages a separate set of tables than those utilized for AMC reporting.
- 3If you run multiple Amazon DSP and sponsored ads campaigns and reached similar audiences, which query can help you understand your customers’ conversion paths that have the highest conversion rate?
- 4SELECT supply_source, SUM (total_cost)/100000000 AS total_cost_dollars, ((SUM (total_cost)/100000000)/SUM (impressions)) *1000 AS avg_cpm, SUM (impressions) AS impressions FROM dsp_inventory GROUP BY 1 SELECT supply_source, SUM (total_cost)/100000 AS total_cost_dollars, ((SUM (total_cost)/100000)/SUM (impressions)) *1000 AS avg_cpm, SUM (impressions) AS impressions FROM dsp_impressions GROUP BY 1,2 SELECT supply_source, SUM (total_cost)/100000 AS total_cost_dollars, ((SUM (total_cost)/100000)/SUM (impressions)) *1000 AS avg_cpm, SUM (impressions) AS impressions FROM dsp_impressions GROUP BY 1 The correct answer is: SELECT supply_source, SUM (total_cost)/100000 AS total_cost_dollars, ((SUM (total_cost)/100000)/SUM (impressions)) *1000 AS avg_cpm, SUM (impressions) AS impressions FROM dsp_impressions GROUP BY 1 Explanation: That’s the correct query. Here is an explanation of the adaptation and why it works: The adapted query focuses on aggregating the desired metrics at the supply_source level, utilizing fields available in the dsp_impressions table: SQL SELECT supply_source, SUM(total_cost) / 100000 AS total_cost_dollars, ((SUM(total_cost) / 100000) / SUM(impressions)) * 1000 AS avg_cpm, SUM(impressions) AS impressions FROM dsp_impressions GROUP BY 1 🔍 Explanation of Changes The core principle of the adaptation is to replace the previous grouping dimensions with the single dimension required: supply_source .Multiple correct
- 5Why is this query unsuitable for assembling a rule-based audience of those who have completed a conversion event? SELECT user_id FROM conversions
- 6The submitted queries table provides details of the query executed and a link to download your aggregated report as a _______ when it’s ready.