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 scoring run requires that a training run has occurred.
• B. 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 has occurred.
• C. 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 has occurred.
• D. 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 has occurred.

What is model scoring in the Data Science Workspace?

• A. Engineering features for a model
• B. Building a recipe
• C. Building and evaluating a model
• D. Applying a model to a data set

A national workout studio chain deploys a new booking system and can now track when a customer puns to 90 into a training session. The studio chain wants to use that information to power their email campaigns. The event data that is collected when a customer books a session does not include information like the Sport category or the level of physical condition the customers should have. That information is stored in a different dataset. The studio chain is using AEP and will have access to the following databases; BOOK A TRAINING SESSION EVENT DATABASE: Time stamp Session ID Member ID TRAINING SESSION METADATA DATABASE:
Session ID
Sport category
Required physical condition
The data architects needs to configure the AEP XDM schemas to be able to build a relationship between the two databases so that the Sport category and the Required physical condition can be used in the AEP segment builder.
How should the data architect configure the AEP XDM to meet these requirements?

• A. Create one schema using Experience Event as the XDM class
  Create Field Groups to include all the attributes from both the Book a Training Session Event and the Training Session Metadata Use the Member ID as the primary identity of that schema Use the Session ID as a secondary identity of that schema Configure one dataflow per database but use the same dataset to store the info
• B. Create one schema (schema A) using the Experience Event Schema as the XDM class 'or the Book a Training Session Event Create one schema (schema B) using a custom XDM class for the Training Session Metadata Use the Member ID as the primary identity of the schema A Use the Session ID as a relationship field on schema A Use the Session ID as the primary identity of the schema B Enable both schemas for profile Create one schema (schema A) using the Experience Event Schema as the XDM class for the Book a Training Session Event
• C. Create one schema (schema B) using a custom XDM class for the Training Session Metadata Use the Member ID as the primary identity of the schema A Use the Session ID as the secondary identity of the schema A Use the Session ID as the primary identity of the schema B Enable both schemas for profile
• D. Create one schema (schema A) using the Experience Event Schema as the XDM class for the Book a Training Session Event Create out schema (schema B> using individual Profile as the XDM class for the Training Session Metadata Use the Member ID as the primary identity of the schema A Use the Session ID as a relationship field on schema A Use the Session ID as the primary identity of the schema B Enable schema A for profile

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.
  

Given the following:

How should a data engineer ingest this data?

• A. AEP Ul > Locate Dataset » Drag and drop the JSON files
• B.
  
• C.