SAP-C02 Design for New Solutions Practice Question

A media company is designing a video transcoding pipeline. They receive raw video files in Amazon S3, which need to be transcoded into multiple formats. The pipeline must handle sporadic bursts of uploads and complete processing within 30 minutes for each video. The cost should be minimized. Which design should they use?

⚠ Common exam trap

Many candidates assume Lambda can handle any short-lived compute task, but they overlook the 15-minute timeout and lack of GPU support for video encoding, making it unsuitable for transcoding jobs that require longer processing times or specialized hardware acceleration.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Use Amazon S3 event notifications to trigger an AWS Elemental MediaConvert job.

AWS Elemental MediaConvert is a fully managed, serverless video transcoding service designed for high-volume, bursty workloads. It integrates directly with S3 event notifications, scales automatically to handle sporadic uploads, and completes each job within the required 30-minute window. Its pay-per-use pricing minimizes cost by eliminating idle infrastructure, unlike provisioned EC2 clusters or Lambda's 15-minute execution limit.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Use AWS Lambda with layers containing FFmpeg to transcode videos.
Why it's wrong here
Lambda caps execution at 15 minutes and its /tmp storage is too small for large video files, so transcoding jobs would time out before completing. It is tempting because Lambda suits sporadic bursts with no idle cost. Lambda would be correct for short, small-payload processing, not multi-format video transcoding.
✓
Use Amazon S3 event notifications to trigger an AWS Elemental MediaConvert job.
Why this is correct
S3 event notifications invoke MediaConvert directly, giving a fully managed, serverless transcoding service that scales per job without idle compute. This satisfies the sporadic-burst requirement and the 30-minute completion window, while consumption-based pricing minimises cost compared with continuously running EC2 transcoding fleets.
✗
Provision a cluster of EC2 instances running FFmpeg, with Auto Scaling based on SQS queue depth.
Why it's wrong here
EC2 instances bill for provisioned hours, so a cluster idles expensively between sporadic bursts, and scaling lag risks the 30-minute deadline. It is tempting because Auto Scaling on queue depth handles variable load. This design would be correct for steady, predictable high-volume transcoding where reserved capacity is economical.
✗
Use Amazon Elastic Transcoder, which is fully managed and triggers from S3 events.
Why it's wrong here
Elastic Transcoder is deprecated and closed to new customers, so it cannot be selected for a new pipeline. It is tempting because it was fully managed and triggered directly from S3 events, matching the bursty, low-effort requirement. It would have been correct before deprecation; AWS Elemental MediaConvert is the current managed replacement.

Quick reference

AWS S3 Storage Class Comparison

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

Go deeper

SAP-C02 Design for New Solutions Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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