SAP-C02 Design for New Solutions Practice Question
A logistics company is designing a new shipment-tracking platform on AWS. The platform ingests telemetry from thousands of trucks, and downstream analytics must query the latest position of any truck within one second. The company also needs to retain six months of raw telemetry for audit at the lowest possible storage cost, and the data must be queryable with standard SQL for ad hoc reports. The company wants a fully managed solution with minimal operational overhead. Which design should the solutions architect recommend?
⚠ Common exam trap
The trap here is trying to satisfy both the sub-second operational lookup and the cheap SQL audit from a single engine instead of splitting the serving and archival paths.
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
✓
Write telemetry to Amazon Kinesis Data Streams, use AWS Lambda to write the latest position to Amazon DynamoDB and the raw records to Amazon S3, and query S3 with Amazon Athena
The design separates the low-latency serving path from the cheap long-term store. Kinesis Data Streams absorbs the telemetry firehose, a Lambda consumer maintains a DynamoDB table keyed by truck ID so the latest position is readable in milliseconds, and the same stream is archived to S3 for audit. Athena then provides standard SQL over the archived data at low cost, and S3 lifecycle rules keep six months of telemetry affordable.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Write telemetry to Amazon S3 and use Amazon Athena with a view that selects the maximum timestamp per truck for latest-position queries
Why it's wrong here
Athena queries scan S3 and typically return in seconds, not sub-second, so it cannot satisfy the one-second latest-position requirement for thousands of trucks. The design is cost-effective for audit and ad hoc SQL, but it lacks a low-latency serving layer for the operational lookup, so it only solves part of the problem.
- ✗
Write telemetry to Amazon OpenSearch Service and retain six months of indices for both latest-position lookups and ad hoc SQL reporting
Why it's wrong here
OpenSearch serves low-latency lookups well, but retaining six months of raw telemetry in OpenSearch indices is significantly more expensive than S3, and its query language is not standard SQL for ad hoc reports. The design overpays for audit storage and still requires an additional engine for SQL analytics.
- ✓
Write telemetry to Amazon Kinesis Data Streams, use AWS Lambda to write the latest position to Amazon DynamoDB and the raw records to Amazon S3, and query S3 with Amazon Athena
Why this is correct
Kinesis Data Streams ingests high-volume telemetry durably, Lambda maintains a DynamoDB table keyed by truck ID for sub-second latest-position lookups, and the same function archives raw records to S3. Athena runs standard SQL directly against S3, and S3 lifecycle policies can move older telemetry to cheaper storage classes, meeting the six-month low-cost audit requirement with no servers to manage.
- ✗
Write telemetry directly to Amazon Redshift, use materialized views for latest positions, and keep all raw data in Redshift for six months
Why it's wrong here
Redshift is a strong analytical warehouse, but using it as the ingestion endpoint and retaining six months of raw telemetry there is far more expensive than object storage, and the cluster requires capacity management. The latest-position lookup would also compete with analytical queries, making the one-second access target harder to guarantee.
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
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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