vmartinez

New-to-brand metrics are only relevant for __________.

• purchase events
• detail page view events
• pixel events
• add to cart events

 

Explanation:

The correct answer is **purchase events** because new-to-brand metrics are specifically designed to measure how many customers make their first purchase from a particular brand after being exposed to an ad. These metrics track customers who have not previously purchased from that brand, distinguishing them from repeat customers. By focusing on purchase events, new-to-brand metrics help advertisers understand the effectiveness of their campaigns in attracting new customers and driving brand awareness. This data is not relevant for add to cart, pixel, or detail page view events, as those metrics track different stages of the customer journey rather than actual purchases.

 

The _____________ field in the conversions_with_relevance table categorizes the relationship between the conversion event and the campaign it is relevant to.

• engagement_scope
• conversions
• request_tag
• purchases

 

Explanation:

The correct answer is **engagement_scope** because the **engagement_scope** field in the **conversions_with_relevance** table categorizes the relationship between the conversion event and the campaign it is relevant to. This field helps to specify whether the conversion event was directly related to an engagement with the campaign, such as a click or an impression, and whether it falls within the scope of the campaign’s attribution window. By using the **engagement_scope** field, you can identify how the conversion is tied to different campaign engagement levels, allowing for more accurate attribution and performance analysis. The other fields, such as request_tag, purchases, and conversions, do not specifically address the relationship between the conversion event and the campaign.

 

 

Which AMC tables would be required for determining the Amazon-attributed conversion volumes per frequency bucket (including Amazon DSP traffic only)?

 

• dsp_impressions_by_matched_segments, amazon_attributed_events_*
• dsp_impressions_by_user_segments, amazon_attributed_events_*
• dsp_impressions, amazon_attributed_events_*

 

Explanation: The correct answer is **dsp_impressions, amazon_attributed_events_** because these two tables are essential for determining Amazon-attributed conversion volumes per frequency bucket, specifically when including Amazon DSP traffic. The **dsp_impressions** table contains data on impressions served through Amazon DSP, which is necessary for understanding the exposure frequency and calculating the frequency bucket. The **amazon_attributed_events_** table contains the conversion data that is attributed to these DSP impressions. By combining these two tables, you can accurately measure conversions within the different frequency buckets, allowing you to assess how often users need to be exposed to an ad to drive conversions. The other options, such as dsp_impressions_by_user_segments or dsp_impressions_by_matched_segments, include different data segmentation but are not as relevant for this specific calculation.

 

 

Which value is not available for Sponsored Products?

 

• campaign_id
• engagement_scope
• purchases
• user_id

 

Why should you wait until 14 days after the end date of your query date range to run analyses that leverage the amazon_attributed_events_by_traffic_time table?

• All impression and conversion events in AMC have a 14-day lag from event occurrence to event present in AMC table.
• Amazon’s attribution logic leverages a 14-day window; queries run before the 14-day attribution window passes will be missing relevant conversion events.
• There is no need to wait 14 days after the end date of your query date range; waiting will have no impact on the query results.

 

Explanation:

The correct answer is **Amazon’s attribution logic leverages a 14-day window; queries run before the 14-day attribution window passes will be missing relevant conversion events** because Amazon’s attribution logic operates on a 14-day lookback window. This means that conversions attributed to a campaign or event are only counted if they occur within 14 days of the initial interaction, such as a click or impression. Running analyses before this 14-day period has passed could result in incomplete data, as some conversions may still be attributed to the campaign after the query date range ends. Therefore, to ensure that all relevant conversion events are included and the attribution logic is fully accounted for, it is recommended to wait until 14 days after the end of your query date range before running analyses that leverage the amazon_attributed_events_by_traffic_time table.

 

How can you determine if NULL values are the result of aggregation thresholds or a natural occurrence within your report?

• Use the Append aggregation threshold columns option in the Query editor
• Check the aggregation thresholds for the event tables in the Schema explorer
• NULL values don’t occur naturally within reports unless the query is invalid.
• Run the report multiple times and look for a variance in the results

 

Explanation:

The correct answer is **Use the Append aggregation threshold columns option in the Query editor** because the **Append aggregation threshold columns** option in the Query editor helps you determine if NULL values in your report are the result of aggregation thresholds or if they naturally occur in the data. This option appends additional columns that provide insight into whether aggregation thresholds have been applied to certain values, which could explain why some values are NULL due to data being below the threshold for reporting. By using this option, you can distinguish between NULL values caused by thresholds and those that represent missing or unavailable data in the report itself. The other options either do not provide specific tools for this task or are incorrect in their explanation of how NULL values occur.