Jeff’s Note #
Unlike generic exam dumps, ADH analyzes this scenario through the lens of a Real-World Lead Developer.
For AWS DVA-C02 candidates, the confusion often lies in how to automate large data ingestion while minimizing operational overhead. In production, this is about knowing exactly which AWS service integrations allow seamless data fetching, storage, and querying without excessive maintenance or complexity. Let’s drill down.
The Certification Drill (Simulated Question) #
Scenario #
BrightWave Solutions manages several critical applications fully hosted on premises. These on-premises servers expose audit logs via an HTTP REST endpoint. The engineering team must design an automated solution that periodically ingests and stores massive volumes of audit data from these servers. Moreover, the team wants to run ad-hoc queries to extract meaningful insights from the audit logs efficiently.
The Requirement: #
Build a solution that regularly pulls the audit logs from the HTTP endpoint, stores the data in a scalable way, and supports fast querying. The solution must be the MOST operationally efficient, meaning minimal manual intervention and automated scheduling.
The Options #
- A) Export audit logs from on-prem servers manually, then upload the logs to an Amazon S3 bucket. Afterward, import the logs into an Amazon RDS database instance for querying.
- B) Create an AWS Lambda function that calls the HTTP endpoint to fetch audit logs. Use Amazon EventBridge to schedule the Lambda function periodically. Configure the Lambda to push the logs to AWS CloudTrail Lake.
- C) Use AWS DataSync to transfer audit logs regularly to an Amazon S3 bucket. Store logs in S3 and use Amazon Athena to query them directly.
- D) Install the Amazon CloudWatch Agent on the on-premises servers, enabling it to push audit logs to Amazon CloudWatch Logs. Use CloudWatch Logs Insights to query the logs.
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Correct Answer #
C
Quick Insight: The Developer Imperative #
For AWS Developers, the key is building automation with minimal maintenance. DataSync automates data transfer reliably at scale without custom code for each fetch. S3 with Athena gives serverless, scalable querying without managing databases or complex ingestion pipelines.
Content Locked: The Expert Analysis #
You’ve identified the answer. But do you know the implementation details that separate a Junior from a Senior?
The Expert’s Analysis #
Correct Answer #
Option C
The Winning Logic #
This option leverages AWS DataSync, a fully managed service designed specifically for automating and accelerating data transfers from on-premises storage to AWS. It reduces operational burden by eliminating the need to build custom-fetching logic or manual uploads.
Once logs land in Amazon S3, Athena provides a serverless interactive query engine that can run SQL queries directly on the logs stored as files—without moving data into databases or managing infrastructure. This combination delivers:
- Fully automated, scheduled ingestion (DataSync handles sync and transfer)
- Scalable storage (S3 can handle massive log volumes)
- Low operational overhead (no servers or constant code updates)
- Cost-effective querying (Athena charges per query, no instance management)
The Trap (Distractor Analysis): #
-
Why not A?
Manually exporting logs and importing into RDS adds significant operational overhead and latency. RDS is relational and requires schema management; it’s overkill for large-scale log ingestion. -
Why not B?
While Lambda + EventBridge scheduling is a common pattern, CloudTrail Lake is designed primarily for cloud-native event auditing, not arbitrary on-prem HTTP log streams. Also, invoking Lambda to fetch large volumes regularly can be prone to timeouts or complexity. -
Why not D?
Installing CloudWatch agent on on-prem servers is plausible but introduces operational complexity managing agent installations, and CloudWatch is more for real-time logs than large batch query analytics. Also, the agent’s support for HTTP endpoints is non-trivial.
The Technical Blueprint #
Developer CLI snippet to create EventBridge rule for Lambda scheduled invocation (context for option B) #
aws events put-rule --name "FetchAuditLogsRule" --schedule-expression "rate(1 hour)"
aws lambda add-permission --function-name FetchAuditLogsFunction --statement-id "EventBridgeInvoke" --action "lambda:InvokeFunction" --principal events.amazonaws.com --source-arn arn:aws:events:region:account-id:rule/FetchAuditLogsRule
aws events put-targets --rule "FetchAuditLogsRule" --targets "Id"="1","Arn"="arn:aws:lambda:region:account-id:function:FetchAuditLogsFunction"
The Comparative Analysis #
| Option | API Complexity | Performance | Use Case |
|---|---|---|---|
| A | Low (manual upload) | Low (manual and DB-bound) | Suitable for small batch uploads |
| B | Medium (Lambda + EventBridge + CloudTrail Lake API) | Variable (Lambda limits/log volume) | Best for cloud logs ingestion but not ideal for large on-prem HTTP logs |
| C | Low (DataSync managed API) | High (scalable S3 + Athena) | Large scale batch ingestion and analytics with minimal Ops |
| D | Medium (CloudWatch agent setup) | Medium (real-time logs only) | On-prem server monitoring, less suited for heavy batch queries |
Real-World Application (Practitioner Insight) #
Exam Rule #
“For the exam, always pick DataSync + S3 + Athena when you see ‘automated bulk data transfer from on-premises’ and ‘query logs efficiently’.”
Real World #
“In reality, you might additionally preprocess logs via Lambda or Glue ETL depending on complexity, but DataSync and Athena form the operationally simplest core pattern.”
(CTA) Stop Guessing, Start Mastering #
Disclaimer
This is a study note based on simulated scenarios for the AWS DVA-C02 exam.