Which Step Functions State Parallel Processes a Collection of Data Files?

DEA-C01 By islever Editorial Last updated Oct 17, 2026

Orchestrate data pipelines. Automate data processing by using AWS services.

Short answer

C Use the Step Functions Map state to iterate over the collection of data files and apply the transformation steps to each file in parallel.

Question

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?

Parallel state
Choice state
Map state Correct Answer
Wait state

Key Takeaways

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.

Common mistake: 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.

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

These links open the official vendor documentation in a new tab.

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.

More DEA-C01 FAQ →

Community Votes

100%

100% of anonymous learners picked answer C. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 8 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 8

sveni1502 👍 3 Answered C

C is Correct To meet the requirement of parallel processing a large collection of data files and applying a specific transformation to each file, the data engineer should use the Map state in AWS Step Functions. The Map state is specifically designed to run a set of tasks in parallel for each element in a collection or array. Each element (in this case, each data file) is processed independently and in parallel, allowing the workflow to take advantage of parallel processing.
pypelyncar 👍 3 Answered C

The Map state allows you to define a single execution path for processing a collection of data items in parallel. This aligns perfectly with the data engineer's requirement of parallel processing a large collection of data files
rralucard_ 👍 2 Answered C

The Map state is specifically designed for processing a collection of items (like data files) in parallel. It allows you to apply a transformation or a set of steps to each item in the input array independently. The Map state automatically iterates over each item in the array and performs the defined steps. This makes it ideal for scenarios where you need to process a large number of files in a similar manner, as in your requirement.
Aesthet 👍 1

With Step Functions, you can orchestrate large-scale parallel workloads to perform tasks, such as on-demand processing of semi-structured data. These parallel workloads let you concurrently process large-scale data sources stored in Amazon S3. For example, you might process a single JSON or CSV file that contains large amounts of data. Or you might process a large set of Amazon S3 objects. To set up a large-scale parallel workload in your workflows, include a Map state in Distributed mode.
Aesthet 👍 1

C is correct. Map state is designed precisely for the requirement described. It allows you to iterate over a collection of items, processing each item individually. The Map state can automatically manage the iteration and execute the specified transformation on each item in parallel, making it the perfect choice for parallel processing of a large collection of data files.

Show 3 more comments

GabrielSGoncalves 👍 1 Answered C

Clearly is mapping state
lucas_rfsb 👍 1 Answered C

C, Map state is correct
FunkyFresco 👍 1 Answered C

to execute in parallel

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