SAP-C02 Practice Question: Design Solutions for Organizational Complexity
A company has a centralized logging solution where all VPC Flow Logs from member accounts are delivered to a central S3 bucket in the logging account. The logs contain sensitive IP addresses that must be redacted before analysis. What is the MOST scalable approach?
⚠ Common exam trap
Candidates often confuse S3 Object Lambda (which modifies data at read time for all access) with query-time redaction, failing to realize that Athena UDFs provide a more scalable and cost-effective solution for selective redaction during analysis without affecting other consumers of the data.
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 Athena with Lambda User-Defined Functions (UDFs) to redact data during query execution.
Amazon Athena with Lambda UDFs allows you to redact sensitive IP addresses at query time without modifying the underlying data in S3. This approach is highly scalable as it leverages Athena's serverless query engine and Lambda's stateless compute, enabling on-the-fly redaction across petabytes of VPC Flow Logs stored centrally in the logging account.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a Lambda function in each member account to redact logs before delivery.
Why it's wrong here
Not scalable; requires managing functions in many accounts.
- ✗
Use S3 Object Lambda to redact sensitive data when objects are read.
Why it's wrong here
S3 Object Lambda transforms objects at read time, but it's per-object, not per-query, and may not be efficient for large datasets.
- ✓
Use Amazon Athena with Lambda User-Defined Functions (UDFs) to redact data during query execution.
Why this is correct
Scalable and flexible; allows redaction on the fly without modifying stored data.
- ✗
Use Amazon Kinesis Data Firehose to transform data before writing to S3.
Why it's wrong here
Adds streaming complexity and cost; may not be necessary for batch analysis.
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
Related to this question
About these practice questions
One of 1,660 original SAP-C02 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This SAP-C02 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the SAP-C02 exam.