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Adobe Real-Time CDP Business Practitioner Professional Sample Questions:

1. A marketer would like to create an Audience by ranking the existing set based on the loyalty points, and select the top 1000 profiles. The marketer would also like to enrich the audience with additional attributes like city and gender from a profile enabled record type dataset. The marketer wishes to accomplish all of this using Adobe Experience Platform.
Which method should the marketer use to create an Audience to achieve this?

A) Apply filters to create a "Ranked audience" using the dataset
B) Compose Audience
C) Rule-based Audience
D) Import Audience

2. A data engineering team is tasked with onboarding data to a dataset with a substantial amount of batch data (300GB) from multiple JSON files for a historical load of customer data. What is the most efficient way for a data engineer to perform the data ingestion?

A) Use manual upload through the UI for historical data
B) Ingest the data using a database source connector
C) Ingest the data using the batch ingestion API
D) Ingest the data using a cloud storage source connector

3. A retail company wants to send product recommendations to customers who recently browsed their website, within seconds of their interaction.
Which activation pattern should they use?

A) Scheduled activation.
B) Batch activation.
C) Edge-based activation.
D) Manual activation.

4. A large retailer is moving customer loyalty data from a data warehouse to the Adobe RTCDP. The source connectors of the Adobe Experience Platform do not support this data warehouse. The data architect plans to use the API to push the data into the experience platform so that the data can be used in the segment in a few minutes to send campaigns.
What is the efficient way to ingest this data in RTCDP?

A) Use the Batch Ingestion API to ingest this data in bulk
B) Use Real-Time APIs to send multiple single-record requests in parallel
C) Use the single HTTP API request to send Multiple Messages
D) Use the Data Landing Zone API to ingest this data immediately

5. A marketer views the license usage dashboard first thing in the morning and notices the average profile richness metric is hovering around 300KB per profile when they only have 100KB in their contract. The marketer appropriately identifies a dataset causing the issue and determines they can drop the entire dataset without impacting their marketing use cases. They know the dataset contributes around ~150KB of average profile richness per profile. When the marketer reviews the dashboard license usage dashboard before the end of the day, they notice it has not been updated with the 150KB of richness removed. They confirm the data is deleted from the profile and are confused.
Assuming no other factors are present that are contributing to the average profile richness, why is the dashboard still showing the old value of 300KB?

A) The marketer forgot the license usage dashboard only updates one time per day.
B) The marketer needs to re-enable the deleted dataset for the dashboard to reflect the changes.
C) The marketer needs to manually run the "Update license usage" job to refresh the metrics.
D) The marketer needs to wait until the next streaming segmentation job runs.

Solutions: