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NEW QUESTION 45
A data engineer it running some tests And tending in event data
How should the data engineer validate that the event is properly attributed to the correct profiler
- A. Use the Dataset Preview to look at a few rows and see if data is in profile.
- B. Use Query service to query events
- C. Use profile lookup to view the events associated to a given profile.
- D. Use the Identity Graph Viewer to view how the identities are mapped.
Answer: C
NEW QUESTION 46
A data engineer is using Data Prep to format data for ingestion. The data engineer needs to load Date/Time as Date values for a field called CustomerCreateDate. The use of this field does not require a time component and as is String based.
The JSON value in this example is '.platosaprod.customerCreateDateVZOOS-11 -04 15:44".
Which Data Prep functions and order should the data engineer use to format the date without the time component?
- A. format(date(_platosaprod.customerCreateDate),'dd-MM-yyyy')
- B. dformaUdateLP'atosaprod.customerCreateDateJ.'dd-MM-yyyy')
- C. format(p'atosaprod.custometCreateDate.dd-MM-yyyy')
- D. date(_platosaprod.customerCreateDate.'dd-MM-yyyy')
Answer: B
NEW QUESTION 47
A data architect wants to create a new XDM field that represents a prize promotion called listOfPrizes. The field represents a list of prizes and contains three sub-fields: prizeld (string). monetaryValue (integer), and prize (Object).
This new field needs to be reusable multiple times within the same class, The sub-fields are created separately.
How should the data architect create the listOfPrizes field?
- A. Create and save a new string array field, then add a nested object field under the string array field.
- B. Create and save a new object array field, then in the right rail select Convert to new data type.
- C. Create and save a new object field, then create a nested array object under the object field.
- D. Create and save a new custom field group, then add an object array field to that field group.
Answer: C
NEW QUESTION 48
What is model scoring in the Data Science Workspace?
- A. Building and evaluating a model
- B. Engineering features for a model
- C. Building a recipe
- D. Applying a model to a data set
Answer: A
NEW QUESTION 49
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