vmartinez

Which of these statements is true regarding recurring query automation?

• Delivery of recurring AMC queries can be automated via API only
• AMC queries executed via UI or API will automatically recur on a daily cadence
• Once recurring queries are set up, modifications to the query schedule can be made in the Instance info panel of the AMC UI
• You can download the results of automatically-recurring queries from the Submitted queries table in the AMC UI

 

Explanation:

Recurring query automation refers to the process of scheduling and delivering recurring queries automatically. In the context of Amazon Marketing Cloud, recurring queries can be automated via API only .

API stands for Application Programming Interface, which is a set of protocols, routines, and tools for building software applications. In this case, the API allows you to automate the delivery of recurring queries in Amazon Marketing Cloud.

By automating the delivery of recurring queries, you can save time and effort by eliminating the need to manually run the same queries over and over again. Instead, you can set up a schedule for the queries to run automatically, freeing up your time to focus on other tasks.

 

The submitted queries table provides details of the query executed and a link to download your aggregated report as a _______ when it’s ready.

• PDF file
• CSV file
• ZIP file
• LOG file

 

Explanation: The correct answer is **CSV file** because the submitted queries table in Amazon Marketing Cloud (AMC) provides details of the queries executed and includes a link to download the resulting aggregated report as a **CSV file** when the query is complete. CSV files are commonly used for exporting and sharing structured data in a tabular format, making them ideal for handling large datasets. By using CSV files, users can easily download, view, and analyze the query results in tools like Excel or other data analysis software. The other options, such as ZIP, PDF, or LOG files, are not typically the formats used for query results in AMC.

 

The _________________ panel within AMC is a repository of Amazon-authored SQL templates and includes associated use cases, instructions, and a guide to interpret the results.

• Paid features
• Schema explorer
• Query editor
• Instructional queries

 

Explanation: The correct answer is **Instructional queries** because the **Instructional queries** panel within Amazon Marketing Cloud (AMC) serves as a repository for Amazon-authored SQL templates. This panel includes a variety of pre-built query templates that are designed to help users quickly start their analysis. It also provides associated use cases, detailed instructions, and a guide on how to interpret the results, making it easier for users, especially those who are new to AMC, to understand and apply common SQL queries for their advertising campaigns. This resource is particularly helpful for users looking to perform specific analyses without having to write SQL queries from scratch. The other options, such as Schema explorer, Query editor, and Paid features, do not focus on providing instructional SQL templates in the same way the **Instructional queries** panel does.

 

This table contains impression and click events from Sponsored Products (SP), Sponsored Brands (SB), and Sponsored Display (SD) campaigns.

• adserver_traffic
• dsp_impressions_by_user_segments:
• conversions_with_relevance
• sponsored_ads_traffic

 

Explanation:

The correct answer is **sponsored_ads_traffic** because this table specifically contains impression and click events from Amazon’s advertising campaigns, including Sponsored Products, Sponsored Brands, Sponsored Display, and Sponsored Television. These campaigns are part of Amazon’s advertising suite, and the **sponsored_ads_traffic** table aggregates data related to the traffic driven by these ad types, including both impressions and clicks, which are essential metrics for evaluating the performance of sponsored ads. The other options, such as conversions_with_relevance, adserver_traffic, and dsp_impressions_by_user_segments, focus on different types of data or ad-related events not directly tied to these specific campaign types.

 

 

Why is it considered a good best practice to exclude NULL user_id values from reach and frequency queries?

 

• Reach and frequency cannot be accurately determined for NULL user_id values, so this group is excluded to prevent NULL user_id values from skewing the results.
• Because NULL user_id values are deemed invalid traffic in post-impression server-side models.
• This would be redundant; records with NULL user_id values have been removed upstream of query processing anyway.

 

Explanation: The correct answer is **Reach and frequency cannot be accurately determined for NULL user_id values, so this group is excluded to prevent NULL user_id values from skewing the results** because NULL user_id values indicate that the specific user information is missing or unavailable. Since reach and frequency metrics are designed to track unique users and the frequency with which they are exposed to ads, having NULL values for user IDs makes it impossible to accurately attribute these metrics to specific individuals. Including such records would lead to inaccurate calculations, potentially distorting the results and leading to misleading conclusions. Therefore, excluding NULL user_id values ensures the integrity of the reach and frequency analysis, allowing for more accurate measurement of campaign performance.