vmartinez

 

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.

 

 

Which of the following business questions can be answered with an overlap analysis?

 

• What was the average impressions per day per campaign per unique user_id value?
• What are the top performing geographic regions for your campaign based on purchase rates?
• What is the impact of your Sponsored Products campaigns and Amazon DSP campaigns in isolation or in combination?

 

Explanation: The correct answer is **What is the impact of your Sponsored Products campaigns and Amazon DSP campaigns in isolation or in combination?** because an overlap analysis helps you understand the relationship between different groups or segments by comparing how users exposed to multiple campaigns, such as Sponsored Products and Amazon DSP, overlap. This analysis enables you to determine whether the campaigns are having a combined effect or whether they perform better in isolation. It can also show the incremental impact of each campaign or their intersection on driving conversions or other metrics. The other options, such as analyzing impressions per day per unique user or identifying top-performing geographic regions based on purchase rates, are more related to other types of analysis, such as performance reporting or geographic segmentation, and are not the focus of overlap analysis.

 

Overlap analyses enable you to generate insights by comparing different groups of __________ values in isolation and when overlapped.

• user_id
• purchases
• request_tag
• impression_id

 

Explanation:

The correct answer is **user_id** because overlap analyses in Amazon Marketing Cloud (AMC) allow you to generate insights by comparing different groups of **user_id** values, both in isolation and when they are overlapped. The **user_id** represents a pseudonymous identifier for individual users, and by comparing these values, you can analyze the overlap between different user segments or audiences, helping to understand how users from one group also belong to another. This analysis is critical for identifying audience intersections and refining targeting strategies. The other options, such as request_tag, purchases, and impression_id, are not related to the concept of overlap analysis, which specifically deals with comparing user-level data (i.e., **user_id**).

 

 

The LIKE function is unsupported by AMC. Which of the following AMC-supported functions can serve as a substitute?

 

• CAST
• COLLECT
• SIMILAR TO