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CRM Analytics and Einstein Discovery Consultant
Study Checklist
Checklist progress
0/196Learned
Given data sources, use Data Manager to extract and load the data into the CRM Analytics application to create datasets.
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Given business needs and consolidated data, implement refreshes for data syncs and dataflows/recipes while keeping limits and considerations in mind.
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Given business/user requirements, perform data transformations in dataflows/recipes.
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Given user requirements or ease of use strategies, manage dataset extended metadata (XMD) by editing labels, values, and colors.
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Implement delivery management strategies in dataflows/recipes including versioning and conversion.
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How does CRM Analytics support versioning for dataflows?
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When converting a dataflow to a recipe, the output schema is inferred from the recipe's transformation chain rather than cloned from the original register node. Recipes support distinct data types such as DateOnly and DateTime, which can differ from the original dataset's field types, requiring consultants to verify and adjust the schema in the output node's Columns tab after conversion.