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NEW QUESTION 39
A data engineer creates a custom identity namespace within AEP. However, this custom Identity namespace is the wrong Identity type. What can the data engineer do to update the identity namespace?

A. Using the Identity Namespace APIs, update the custom Identity type.B. Delete the customIdentityNamespace from the AEP User-Interface under Identities.C. Edit the Identity Namespace type within the AEP User-Interface under Identities.D. Create a new custom Identity Namespace with the correct Identity type.

Answer: C

 

NEW QUESTION 40
There are two existing entities in the Real-time Customer Profile store: one profile record and one event.

Both the event schema and profile schema have "email" as the primary identity. The _id is used to identify unique events.
Later, a data engineer ingests one additional profile fragment and another event:
Profile: ("email": "[email protected]*. "favBrand': "Adobe")
Event: {''_id": '5000". "email": "[email protected]". "purchase": "Photoshop 2021") What will the profile attributes and event look like when doing a profile [email protected]?
A)

B)

C)

D)

A. Option CB. Option AC. Option DD. Option B

Answer: C

 

NEW QUESTION 41
Time-series data has been ingested and linked with a Real-time Customer Profile.
A data engineer wants to look up only the most recent time-series event associated with the Luma ID "100".
Which API call will return the desired event object?
A)

B)

C)

D)

A. Option CB. Option AC. Option DD. Option B

Answer: C

 

NEW QUESTION 42
A data engineer wants to connect a new data source into AEP using an Amazon S3 Bucket. The S3 Bucket currently will be added with the daily deltas.
The historical data and the recurrent deltas must be imported.
In which way can this task be performed with minimal effort?

A. Create one scheduled dataflow and enable the backfillB. Create one scheduled dataflow and enable partial ingestionC. Create one scheduled dataflow for the deltas and import the historical data through a data ingestion workflowD. Create a one-time dataflow for the historical data and one scheduled dataflow for the deltas

Answer: B

 

NEW QUESTION 43
What is the difference between a data science training run and a data science scoring run?

A. A training run involves generating insights from your data while a scoring run is about learning patterns from your data. A training run requires that a scoring run hasoccurred.B. A training run involves generating insights from your data while a scoring run is about learning patterns from your data. A scoring run requires that a training run hasoccurred.C. A training run involves learning patterns from your data while a scoring run is about generating insights from your data. A scoring run requires that a training run hasoccurred.D. A training run involves learning patterns from your data while a scoring run is about generating insights from your data. A training run requires that a scoring run hasoccurred.

Answer: A

 

NEW QUESTION 44
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