FME Workflows Quiz

Bringing together multiple streams of features will automatically join them based on shared attributes.

True False Brining together streams merges features. To join them based on a common key requires using a join transformer.

Readers can have multiple feature types and feature types can belong to multiple readers.

True False Readers and writers can contain multiple feature types, but not the other way around.

A **feature type fanout** creates multiple _layers_ in the written file based on attribute values.

True False Fanouts split your data up according to attribute values just before writing it. Feature type fanouts split the data stream into different layers (or tables, levels, etc. depending on the format). Dataset fanouts split the data stream into different files.

Which of the following scenarios would be well-suited to using feature caching? Check all that apply.

Reading from a large database Reading from a large web dataset Running a production workspace Running a simple workspace with a Creator and a single Emailer to send an email Using partial runs to incrementally develop a workspace with a complicated workflow

Workspaces that read large datasets or data that is slow to access, including databases or data on a network, can benefit from feature caching. Read the data in once to cache it and then use Run From This or Run To This.

The initial process of feature caching takes longer than running the workspace without feature caching on, so it is not a good idea to keep feature caching on with a production workspace.

A very simple workspace with only one or two transformers, neither of which produce many features, will not benefit from feature caching.

Using partial runs with feature caching is a great way to quickly build and test sections of your workspace.

Who might benefit from your use of annotation and bookmarks as part of best practice/style? Check all that apply.

Other FME users in your organization Customers to whom you deliver FME workspaces End users of the data produced by the workspace Yourself in the future, coming back to edit the workspace Anyone who might have to edit or understand your workspace will benefit if it uses bookmarks and annotation effectively.

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