vmartinez

What are the two ways to access AMC?

• Through the APIs or CLI
• Through the APIs or AWS
• Through the UI or the APIs

 

Explanation:

The correct answer is **Through the UI or the APIs** because Amazon Marketing Cloud (AMC) can be accessed in two primary ways: through the user interface (UI) and the APIs. The **UI** provides a graphical interface where users can interact with AMC, query data, view reports, and use various tools for analysis without needing to write code. This is ideal for users who prefer a visual, more user-friendly way of working with the platform. On the other hand, the **APIs** allow for programmatic access to AMC, enabling users to automate queries, retrieve data, and integrate AMC functionality into their systems. This flexibility provides both technical and non-technical users with the ability to interact with AMC depending on their preferences and needs. The other options involving CLI or AWS are incorrect, as they do not directly reflect the primary methods of accessing AMC.

 

The ___________ function can be used to append a string to ID fields (such as campaign_id), preventing issues reading the accurate ID values within Excel.

• CONCAT
• COLLECT
• SUM

 

Explanation:

The correct answer is **CONCAT** because the CONCAT function is used to combine or append strings, such as appending a string to ID fields like campaign_id. This helps prevent issues when reading or interpreting ID values in Excel, especially when the ID might be treated incorrectly or split across multiple columns. By using CONCAT, you ensure that the campaign_id remains in a single, cohesive value, making it easier to work with in Excel or other tools that might misinterpret or format the data differently. The other options, COLLECT and SUM, are not appropriate for appending strings and are used for other purposes such as gathering or adding numerical data.

 

How would you adapt this query to limit results to Sponsored Products keyword targeting only?

SELECT ad_product_type, targeting, customer_search_term,
match_type, SUM(spend)/100000000 AS total_cost_dollars,((SUM(spend)/100000000)/SUM(impressions))*1000 AS avg_cpm, SUM(impressions) AS impressions, SUM(clicks) AS clicks, (SUM(clicks)/SUM(impressions)) AS ctr FROM sponsored_ads_traffic WHERE match_type IN(‘PHRASE’, ‘BROAD’, ‘EXACT’) GROUP BY 1,2,3,4

• Adjust SELECT statement to include the sponsored_products field
• Adjust FROM statement to: FROM sponsored_products
• Add LIMIT clause: LIMIT sponsored_products
• Adjust WHERE statement to: WHERE match_type IN(‘PHRASE’, ‘BROAD’, ‘EXACT’) AND ad_product_type = ‘sponsored_products’

 

Explanation:

The correct answer is **Adjust WHERE statement to: WHERE match_type IN(‘PHRASE’, ‘BROAD’, ‘EXACT’) AND ad_product_type = ‘sponsored_products’** because to limit the query results specifically to Sponsored Products keyword targeting, the **WHERE** clause should be modified to include an additional condition that filters for **ad_product_type = ‘sponsored_products’**. This ensures that the query returns data only for Sponsored Products campaigns, specifically targeting the desired match types (PHRASE, BROAD, EXACT). The other options are incorrect because adding a **LIMIT** clause does not filter for Sponsored Products, modifying the **SELECT** statement to include the **sponsored_products** field is unnecessary, and adjusting the **FROM** statement to **sponsored_products** is not the correct way to filter the data for keyword targeting. The **WHERE** clause modification is the most effective way to filter the data as required.

 

Columns in the HIGH and VERY_HIGH aggregation threshold categories cannot be used when applying ____________ to your query.

• first-party insights
• aggregation functions such as SUM or AVG
• WHERE filters with literal (static) values
• UNION or JOIN functions

or

• WHERE filters
• JOIN functions
• third-party insights
• first-party insights

 

Explanation:

The correct answer is **WHERE filters with literal (static) values** because columns in the HIGH and VERY_HIGH aggregation threshold categories are typically derived from dynamic values or calculations that involve aggregation or other query functions. These columns cannot be used directly with **WHERE filters that apply static, literal values** because their values are not static and are calculated based on the context of the query. For example, a filter that attempts to use a literal value to restrict data based on dynamic aggregations would result in an error or incorrect results. Instead, such columns are intended to be used with more flexible query functions like aggregation or within calculations that adapt to the query context. The other options—aggregation functions, first-party insights, and UNION or JOIN functions—do not inherently conflict with the use of HIGH and VERY_HIGH threshold categories in the same way.

 

 

Which table would you leverage for a query when your objective is to understand how your current audience strategy is performing for your campaigns?

 

• dsp_impressions_by_matched_segments
• amazon_attributed_events_by_conversion_time
• conversions_with_relevance

 

Explanation: The correct answer is **dsp_impressions_by_matched_segments** because this table is specifically designed to help you understand how your current audience strategy is performing for your campaigns. It provides detailed insights into the impressions served through Amazon DSP, broken down by audience segments that have been matched to your campaigns. By using this table, you can evaluate the effectiveness of different audience segments, track engagement, and assess how well your audience targeting strategy is driving impressions and potentially conversions. The other tables, such as **conversions_with_relevance** and **amazon_attributed_events_by_conversion_time**, focus more on conversion tracking and attribution, which are important but do not directly provide insights into audience strategy performance.

 

 

Which of the following statements about paid features (beta) is false?

 

• Paid features are offered as a monthly subscription and can be managed by the administrator of your AMC account
• Paid features allow advertisers to upload their own first-party insights to better understand the shopping journey
• Paid features provide access to subscription-based tables that can be used in conjunction with the standard tables available in AMC

 

Explanation: The correct answer is **Paid features allow advertisers to upload their own first-party insights to better understand the shopping journey** because this statement is false. Paid features in Amazon Marketing Cloud (AMC) provide access to subscription-based data and insights, often from Amazon or third-party sources, but they do not directly allow advertisers to upload their own first-party data for integration within AMC. While advertisers can use their own first-party data in certain scenarios, the paid features themselves are typically focused on enhancing existing Amazon Ads data with external insights, not enabling the upload of first-party insights. The other statements are true: paid features are subscription-based and managed by the account administrator, and they provide access to additional tables that can complement the standard AMC data.