Which Step Functions State Parallel Processes a Collection of Data Files?
A data engineer needs to use AWS Step Functions to design an orchestration workflow. The workflow must parallel process a large collection of data files and apply a specific transformation to each file. Which Step Functions state should the data engineer use to meet these requirements?
Community Votes
100% of anonymous learners picked answer C. Votes are pick records left by other test-takers — they are not the verified answer.
Community Insight
The exam tests the difference between Map (same workflow applied to every element of an array, dynamic fan-out) and Parallel (fixed branches of different work executed together), and the trap is picking Parallel simply because the word "parallel" appears in the requirement.
AWS Step Functions uses the Map state to iterate over a collection of items such as S3 data files and run the same transformation steps on each one concurrently. The page confirms that Map, not Parallel, is the correct state for fan-out processing of a variable-sized file collection.
Candidates choose the Parallel state because the requirement says "parallel process", but Parallel state runs a fixed set of distinct branches once, whereas Map iterates over an unknown number of data files and applies one transformation path to each.
Community Discussion (8 comments)
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Expert Analysis
Why the Answer Is Correct
The requirement has two halves: process a large collection of data files, and apply one specific transformation to every file. The Map state is the only Amazon States Language state built for exactly that pattern — it takes an array (for example, a list of S3 object keys produced by a previous task) and runs the same set of steps against each element concurrently. As one learner put it, "The Map state allows you to define a single execution path for processing a collection of data items in parallel." Map also supports a MaxConcurrency field, so the engineer can throttle how many files are transformed at once, which matters for large collections. That combination of dynamic iteration plus a repeated transformation is the definitive signal for Map in DEA-C01 orchestration questions.
Why the Other Options Are Wrong
The Parallel state executes multiple different branches that you hard-code into the definition and waits for all of them to finish; it does not iterate over an array of files, so it cannot scale to an unknown collection size. The Choice state only adds branching logic based on a comparison, and the Wait state simply pauses execution for a duration or until a timestamp — neither performs iteration or transformation work. Note that for very large collections, the Distributed Map mode is the modern variant, but the state itself is still Map, so the answer does not change.
Community Comment Notes
The community is unanimous here — all 100 recorded votes selected C, and as sveni1502 explained, Map "is specifically designed to run a set of tasks in parallel for each element in a collection or array." rralucard_ added that Map "automatically iterates over each item in the array and performs the defined steps," which captures the fan-out behaviour the scenario needs. Aesthet pointed to the AWS guidance on orchestrating large-scale parallel workloads and processing "a large set of Amazon S3 objects," while GabrielSGoncalves summed it up bluntly: "Clearly is mapping state." No commenter argued for the Parallel state.
Official Reference
Exam Strategy
When a Step Functions question mentions a collection, array, or list of items that all need the same processing, Map is almost always the answer, even if the word "parallel" appears. Reserve Parallel state for a fixed, hand-authored set of distinct branches, and remember that Distributed Map is the choice when the collection is huge or the child workflows are long-running.
Frequently Asked Questions
Why is the Parallel state wrong if the requirement says "parallel process"?
Parallel state runs a fixed set of distinct branches you define once. It does not iterate over an array of files, so it cannot apply one transformation across an unknown number of data files.
When should the engineer use Distributed Map instead of Map?
Distributed Map is a mode of the Map state for very large collections or long-running child workflows, running iterations across multiple child executions. It is still the Map state, so the answer stays C.
Related Analysis
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