vmartinez

 

Which two functions can be used together to filter records based on presence of a string within a column’s values?

 

• WHERE, SIMILAR TO
• WHERE, COLLECT
• WHERE, LIKE
• WHERE, CONCAT

 

Explanation: The correct answer is **WHERE, SIMILAR TO** because the **WHERE** clause is used to filter records based on specific conditions, and the **SIMILAR TO** function in SQL allows for pattern matching with more advanced regular expression-like syntax compared to **LIKE**. When combined, **WHERE, SIMILAR TO** can filter records based on the presence of a string or pattern within a column’s values, making it more flexible for complex string matching scenarios. This combination enables more precise filtering of records by allowing patterns to be specified in a more robust way. The **WHERE, LIKE** combination is also commonly used, but **SIMILAR TO** provides greater flexibility for pattern matching. The other options, **COLLECT** and **CONCAT**, do not serve the purpose of filtering based on string presence within a column.

 

If 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?

• Path to Conversion by Campaign Groups
• Audience Overlap Analysis
• Sponsored Products and DSP Display Overlap

 

Explanation:

The correct answer is **Path to Conversion by Campaign Groups** because this query helps analyze the customer journey by identifying the most effective conversion paths that lead to high conversion rates across multiple Amazon DSP and sponsored ads campaigns. By grouping campaigns together, the query provides insights into how different campaign types contribute to conversions, allowing advertisers to see which combinations of DSP and sponsored ads are most effective in driving conversions. This analysis helps in understanding how customers interact with various ads and the sequence of engagements that lead to successful conversions. The other options, such as Sponsored Products and DSP Display Overlap or Audience Overlap Analysis, focus on audience reach and overlap but do not provide the same detailed analysis of conversion paths.

 

A single on-Amazon transaction that includes multiple ASINs from your brand will be represented by a single conversion event within each of the amazon_attributed_events_* tables.

• True
• False

 

Explanation:

The correct answer is **False** because a single on-Amazon transaction that includes multiple ASINs from the same brand will typically be represented by multiple conversion events in the amazon_attributed_events_* tables. Each ASIN within the transaction is considered an individual product and will have its own conversion event recorded, even though they are part of the same transaction. This ensures that the attribution data for each individual ASIN is accurately captured, providing insights into which specific products contributed to the overall conversion, rather than grouping them under a single conversion event for the entire transaction.

 

To minimize the number of values rendered NULL due to aggregation thresholds, you can:

• Use more granular dimensions in your final GROUP BY statement
• Look at certain dimensions in combination rather than in isolation
• Reduce the time window of the query
• Broaden any filtering that may be in place on the query

 

Explanation:

The correct answer is indeed Broaden any filtering that may be in place on the query. To minimize the number of values rendered NULL due to aggregation thresholds, broadening the filtering in the query can help increase the likelihood of having sufficient data to meet those thresholds. By relaxing filters or expanding the criteria, you allow for a larger set of data to be included in the aggregation, which increases the chances of having enough data in each group to avoid NULL values. This approach helps ensure that smaller or more specific segments are not excluded, which might otherwise result in insufficient data to meet the minimum requirements for the aggregation, leading to NULL values.

 

 

Rule-based audience creation leverages a separate set of tables than those utilized for AMC reporting.

 

• True

 

• False

 

 

Which AWS service can be leveraged by the AWS Admin to provide appropriate permissions and access keys to AMC API users?

 

• QuickSight
• IAM
• SageMaker