AWS Certified Generative AI Developer - Professional: Multimodal ingestion pipeline

Multimodal ingestion pipeline text plus

docs audio images. Big idea one

sentence. You take messy multimodal

files in S3 and run them through a step

functions assembly line to extract text

structure. Then store standard JSON for

search analytics and prompting. How one

the AWS assembly line architecture exam

perfect miniab design you mentioned. You

can see the code in our conversation

history. what each piece does. S3, the

landing zone for uploads, PDFs, JPGs,

MP3s, etc. Event bridge detects the

upload event and triggers processing.

Step functions, orchestrates multi-step

workflow, retry, branching failures.

Textract extracts text plus tables,

forms from document scans. Transcribe

turns audio into text transcripts.

Recognition detects labels, text, faces,

moderation, and images depending on use.

S3 output stores normalized JSON results

per file. your single truth format. NHAR

2 static one multimodal edition static

fixed rules which extractor to use for

each file type confidence thresholds

output JSON schema retry backoff rules

where outputs are stored dynamic input

the uploaded file type metadata content

that's static plus one fixed pipeline

rules changing files

three how step functions branches by

file type core concept the step function

starts with a what is this file step if

PDF or.png/jpg

scan extract if m3wave transcribe

if.jpng image recognition if.txt basic

text normalization then it routes to the

right extraction path exam signal. If

you see multiple file types

orchestration retries branching step

functions

hammer four what the structured JSON

output typically looks like. realistic.

You want one consistent envelope so

downstream systems don't care about file

type. Example output JSON shape not

exact AWS output. You can see the code

in our conversation history.

For audio transcripts, you can see the

code in our conversation history.

For images, you can see the code in our

conversation history.

Number five, reliability patterns inside

the pipeline. Exam loves these. Check

retries plus backoff. Text track

transcribe may throttle or fail

temporarily. Step functions retries with

backoff. Dead letter handling. If

extraction fails after retries, store a

failure JSON send alert. SNS event

bridge. Item potency. If the same file

triggers twice, don't duplicate results.

Store output with deterministic key.

Number six, memory story so you don't

forget. The sorting factory. Imagine a

factory conveyor belt. Boxes arrive at

the loading dock. S3 upload. A bell

rings and tells the factory new box

arrived. Event bridge. The factory

manager routes the box to the right

machine. Step functions. The machines

document shredder reader text audio

stenographer transcribe image detective

recognition. Everything is rewritten

into the same standardized report format

structured JSON in S3. The trick is the

factory doesn't care what the box looked

like. It always outputs the same kind of

report. That's multimodal ingestion.

The mini lab in one sentence. A user

uploads a file to S3. An event fires.

Step functions runs the right extractor.

Textract transcribe recognition and the

result is saved as a clean JSON file

that downstream systems can use. Number

one, a concrete example in case. Step

zero. Upload to S3. A user uploads

patient intake01.pdf.

It lands in S3/myingestion

bucket uploads patient intake01.pdf.

S3 is your landing zone. Nothing else

happens yet. Step one. Event bridge

notices the upload S3 emits an event

like object created. Event bridge rule

listens for bucket equals my ingestion

bucket prefix equals upload slash event

type equals object created. When it

matches, event bridge triggers your step

function state machine and passes the

object info, bucket, key path, size,

timestamp. Why event bridge? Because

it's the clean event router for AWS. It

decouples ingestion from processing.

Step two, step functions runs the

workflow, the factory manager. Step

functions receives bucket my ingestion

bucket key, uploads patient intake

001.pdf.

Now it runs the workflow one. Detect

file type PDF, audio, image. Two, branch

to the correct extractor. Three,

normalize the extractor output into your

standard JSON format. Four, store JSON

in an output location. Five, if anything

fails, retry back off, then store error

JSON plus notify.

Step three, extract text track for docs.

Because it's a PDF, Step Functions

routes to text. Textract extracts raw

text, key value pairs, forms, tables, if

any. Step four, store structured JSON

the single truth format. Step functions

writes a JSON output file to S3MYestion

buckets processed patient intake

001.json. Now your rag system/database

app can read JSON without caring it came

from a PDF. Two, the same pipeline but

with audio and images. Example audio

call recording. Upload uploads calls

call 772.wave.

Step functions routes to transcribe then

outputs. Process calls call 7772.j JSON

example B photo of a receipt upload

upload/ images receipt.jpeg step

functions routes to recognition labels

text detection outputs processed images

receipt.json JSON. The exact same

pipeline works only the extractor

changes. Number three, what step

functions looks like logically. Super

clear. Think of your state machine like

this. State one get object metadata.

Read S3 key size content type determine

extension. State two choose extractor.

Choice state. If file ends with PDF or

is a scanned image text branch

if.wave/mpp3 wave/mpp3 transcribe branch

if image recognition branch else

fallback branch unsupported file type

state three run extractor call the

service state four normalize to standard

JSON convert raw service output your

unified JSON schema state five write

output save to processed slack JSON

error handling retry with backoff on

permanent fail save error JSON notify

this is exactly why step functions is

used it's built for branching plus

retries failure handling. Number four,

what structured JSON actually means.

Real example, textract output normalized

example. You can see the code in our

conversation history.

Transcribe normalized example. You can

see the code in our conversation

history.

Recognition normalized example. You can

see the code in our conversation

history. Notice the trick. All outputs

have the same envelope source extraction

content metadata. This makes your

downstream code dead simple. Number

five, why this pipeline is exam correct.

The key reasoning if AWS asks for

multiple file types, orchestration,

retries, branching, consistent output.

Your answer should naturally include S3

plus event bridge plus step functions

plus the right extractor service. Tiny

memory story so it sticks. S3 is the

mailbox. Event bridge is the doorbell.

Step functions is the receptionist. Text

track, transcribe, recognition are the

specialists. Structure JSON is the

patient chart.