vmartinez

The __________ field functions as a grouping indicator for the impression and any associated clicks, view events, or conversion events.

 

• conversions
• engagement_scope
• request_tag
• purchases

 

Explanation: The correct answer is **request_tag** because the request_tag field serves as a grouping indicator that associates impressions with any related clicks, view events, or conversion events. This tag is used to group all events that are part of a specific ad request, allowing advertisers to track the performance of an individual impression and its subsequent interactions across various touchpoints. By using the request_tag, it becomes easier to link all the actions (clicks, views, and conversions) that stem from a single impression, enabling more accurate attribution and analysis of the ad’s effectiveness. The other options—engagement_scope, conversions, and purchases—do not function as group indicators in this context.

 

Since the amazon_attributed_events_* tables contain ad-attributed conversions across all of your Amazon Ads media, you can aggregate them by sponsored ads vs. Amazon DSP using the __________.

• conversion_event_category field
• supply_source field
• traffic_event_type field
• ad_product_type field

 

Explanation:

The correct answer is **ad_product_type field** because the amazon_attributed_events_* tables contain data on ad-attributed conversions across various Amazon Ads media, and the **ad_product_type field** allows you to differentiate between the different types of advertising products, such as Sponsored Ads and Amazon DSP. By using this field, you can easily aggregate and analyze the data separately for these two advertising types, enabling you to compare their performance and understand the impact of each media type. The other fields, such as conversion_event_category, traffic_event_type, and supply_source, are related to different aspects of the data and do not specifically differentiate between Sponsored Ads and Amazon DSP.

 

How would you adapt this query to obtain cost and impression metrics by supply_source? SELECT device_type, operating_system, 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)/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

 

Explanation:

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** because this query correctly adapts the original query to obtain cost and impression metrics by **supply_source**. In this adjusted query, the **supply_source** field is added in both the SELECT and GROUP BY clauses to group the results by the specific supply source, which allows you to see the total cost, average CPM, and impressions broken down by supply source. The query uses **dsp_impressions** as the data source, which contains the relevant metrics. The other options either reference incorrect tables, such as **dsp_inventory**, or do not properly include the necessary grouping for supply source. This version correctly calculates the metrics by supply source, ensuring the results align with the user’s objective.

 

Why is this query unsuitable for assembling a rule-based audience of those who have completed a conversion event?

SELECT user_id FROM conversions

• The query cannot include user_id as a dimension within the final SELECT statement
• The query does not leverage an audience-specific table
• The query requires a WHERE clause to specify the relevant conversion event type(s)
• The query requires a GROUP BY statement

 

Explanation:

The correct answer is **The query does not leverage an audience-specific table** because this query is unsuitable for assembling a rule-based audience of those who have completed a conversion event as it is based on a generic **conversions** table without utilizing an audience-specific table. Audience-specific tables in Amazon Marketing Cloud (AMC) are designed to contain more targeted, segment-specific data that can be used to define rules and build audiences based on user behaviors, interactions, and conversions. In contrast, the **conversions** table contains raw conversion data but lacks the detailed segmentation needed to create a rule-based audience. To build such an audience, you would typically need to leverage tables specifically designed for audience creation, which allow for more nuanced segmentation and targeting based on various attributes or events.