Question 445 of 1,746
Design for New SolutionshardMultiple ChoiceObjective-mapped

Quick Answer

The correct architecture is to use AWS IoT Core rules to route messages to Amazon Kinesis Data Firehose, which writes raw data to S3, then use Amazon Kinesis Data Analytics to read from S3 for real-time anomaly detection and write results back to S3 via Firehose. This works because Kinesis Data Analytics provides native SQL-based stream processing that scales elastically without concurrency limits, while Kinesis Data Firehose delivers data to S3 with near-real-time latency, meeting the one-minute window. On the AWS Certified Solutions Architect Professional SAP-C02 exam, this scenario tests your understanding of managed streaming services versus serverless functions for high-throughput IoT real-time analytics—a common trap is assuming Lambda can scale indefinitely, but concurrency limits are a hard ceiling. The key insight is that Kinesis Data Analytics handles the processing logic without invoking Lambda, avoiding throttling entirely. Memory tip: think "Firehose for delivery, Analytics for processing, no Lambda in the middle."

SAP-C02 Design for New Solutions Practice Question

This SAP-C02 practice question tests your understanding of design for new solutions. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.

A company is designing a data processing pipeline for real-time analytics. The pipeline ingests data from IoT devices that send JSON messages via MQTT to AWS IoT Core. The messages must be processed in real-time to detect anomalies and the results must be stored in Amazon S3 for later analysis. The company currently uses a Lambda function to process each message, but as the number of devices grows, the Lambda function is being throttled due to concurrency limits. The company needs a solution that scales to handle thousands of devices per second without losing messages. The processed data must be available in S3 within 1 minute of ingestion. Which architecture should the company use?

Question 1hardmultiple choice
Full question →

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 AWS IoT Core rules to route messages to Amazon Kinesis Data Firehose, which writes raw data to S3. Then use Amazon Kinesis Data Analytics to read from S3 and perform real-time anomaly detection, writing results back to S3 via Firehose.

Option C is correct because Kinesis Data Analytics provides real-time SQL processing, and Kinesis Data Firehose delivers the results to S3 with low latency. Option A is wrong because Lambda concurrency limits will still be hit; SQS does not solve concurrency limits. Option B is wrong because Kinesis Data Streams alone does not process data; Lambda would still be needed and could be throttled. Option D is wrong because S3 cannot process streaming data in real-time; S3 Select is for querying objects.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Answer analysis

Option-by-option breakdown

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

  • Send the IoT messages to an Amazon SQS queue and have Lambda poll the queue in batches to reduce the number of concurrent invocations.

    Why it's wrong here

    SQS does not prevent Lambda concurrency limits; even with batch processing, high throughput will hit the concurrency limit.

  • Store the raw messages in an S3 bucket and use S3 Select to query the data for anomalies periodically.

    Why it's wrong here

    S3 Select is for querying objects, not for real-time streaming; it would not meet the 1-minute latency requirement.

  • Ingest the messages into Amazon Kinesis Data Streams with multiple shards, and use a Lambda function to process records from the stream. Increase the Lambda concurrency limit.

    Why it's wrong here

    Increasing the concurrency limit is possible but may still be capped; also, Lambda may not keep up with high throughput without proper scaling.

  • Use AWS IoT Core rules to route messages to Amazon Kinesis Data Firehose, which writes raw data to S3. Then use Amazon Kinesis Data Analytics to read from S3 and perform real-time anomaly detection, writing results back to S3 via Firehose.

    Why this is correct

    This architecture separates ingestion (Firehose) from processing (Kinesis Data Analytics), which can scale independently and meet the 1-minute latency.

    Related concept

    Read the scenario before looking for a memorised answer.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.

Detailed technical explanation

How to think about this question

This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.

KKey Concepts to Remember

  • Read the scenario before looking for a memorised answer.
  • Find the constraint that changes the correct option.
  • Eliminate answers that are true in general but not in this case.
  • Use explanations to understand the rule behind the answer.

TExam Day Tips

  • Underline the problem statement mentally.
  • Watch for words such as best, first, most likely and least administrative effort.
  • Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.

What to study next

Got this wrong? Here's your next step.

Identify which SAP-C02 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

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FAQ

Questions learners often ask

What does this SAP-C02 question test?

Design for New Solutions — This question tests Design for New Solutions — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Use AWS IoT Core rules to route messages to Amazon Kinesis Data Firehose, which writes raw data to S3. Then use Amazon Kinesis Data Analytics to read from S3 and perform real-time anomaly detection, writing results back to S3 via Firehose. — Option C is correct because Kinesis Data Analytics provides real-time SQL processing, and Kinesis Data Firehose delivers the results to S3 with low latency. Option A is wrong because Lambda concurrency limits will still be hit; SQS does not solve concurrency limits. Option B is wrong because Kinesis Data Streams alone does not process data; Lambda would still be needed and could be throttled. Option D is wrong because S3 cannot process streaming data in real-time; S3 Select is for querying objects.

What should I do if I get this SAP-C02 question wrong?

Identify which SAP-C02 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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Same concept, more angles

1 more ways this is tested on SAP-C02

These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.

Variation 1. A company is designing a new real-time analytics platform that ingests data from thousands of IoT devices. The devices send JSON messages every second to an AWS IoT Core topic. The messages must be processed and stored in Amazon S3 for long-term analysis. The processing includes enrichment by calling a third-party API to add location data. The company expects the workload to vary significantly, with peak traffic of 100,000 messages per second. The solution must be cost-effective and minimize operational overhead. The current architecture uses a Lambda function subscribed to the IoT topic, which processes each message and writes to S3. However, during initial testing, the Lambda function frequently times out due to the third-party API latency, causing message loss. What should the company do to resolve this issue while meeting all requirements?

hard
  • A.Increase the Lambda function timeout to 15 minutes and memory to 10240 MB
  • B.Use Amazon Kinesis Data Firehose to buffer data and write to S3, then trigger a Lambda function to enrich data asynchronously
  • C.Enable Provisioned Concurrency on the Lambda function to reduce cold starts
  • D.Configure the IoT rule to write messages to an Amazon SQS queue. Then use a Lambda function with reserved concurrency to poll the queue and process messages at a controlled rate

Why D: Option D is correct because decoupling the ingestion from the processing using an SQS queue allows the Lambda function to poll messages at a controlled rate, preventing timeouts from third-party API latency. The SQS queue acts as a buffer, absorbing traffic spikes of up to 100,000 messages per second, and Lambda can process messages asynchronously without loss. This approach is cost-effective and minimizes operational overhead by leveraging managed services.

Last reviewed: Jun 20, 2026

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