SAP-C02 Design for New Solutions Practice Question
A company is building a new data lake on AWS to store and analyze petabytes of data from various sources. The data includes structured (CSV, Parquet), semi-structured (JSON), and unstructured (images, videos) files. The company needs a cost-effective storage solution that allows running SQL queries directly on the data without loading it into a database. Data is accessed infrequently but must be available within minutes. Which solution should a Solutions Architect recommend?
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
✓
Store data in Amazon S3 using lifecycle policies to transition infrequent data to S3 Standard-IA. Use Amazon Athena for SQL queries.
Amazon S3 is the ideal storage for a data lake, supporting structured, semi-structured, and unstructured data. Lifecycle policies can transition infrequent data to S3 Standard-IA to reduce costs while maintaining low latency retrieval. Amazon Athena enables serverless SQL queries directly on data stored in S3 without requiring loading into a database. Option A is incorrect because S3 Glacier Deep Archive is for long-term archival with retrieval times of hours, not minutes, making it unsuitable for queries that require availability within minutes. Option B is incorrect because Amazon Redshift is a data warehouse that requires data to be loaded into it; Redshift Spectrum can query S3, but the primary storage is not designed for a data lake scenario where data is queried directly. Option D is incorrect because Amazon EBS is block storage attached to a single EC2 instance, which does not provide the scalability or cost-effectiveness for petabytes of data in a data lake.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Store data in Amazon S3 Glacier Deep Archive for cost savings, and use Athena for queries.
Why it's wrong here
Glacier Deep Archive has restore times of 12 hours, not minutes; Athena cannot query Glacier directly.
- ✗
Store data in Amazon Redshift, use Redshift Spectrum to query data in S3.
Why it's wrong here
Redshift is a data warehouse; data must be loaded into Redshift for optimal performance; Spectrum can query S3 but adds cost.
- ✓
Store data in Amazon S3 using lifecycle policies to transition infrequent data to S3 Standard-IA. Use Amazon Athena for SQL queries.
Why this is correct
S3 is cost-effective object storage; Athena is serverless and queries data directly in S3.
- ✗
Store data in Amazon EBS volumes attached to an EC2 instance running a SQL engine.
Why it's wrong here
EBS is block storage, not scalable for petabytes; a single EC2 instance is a bottleneck.
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 |
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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.