The correct combination is Amazon S3 with lifecycle policies to transition objects to S3 Glacier Deep Archive after 90 days, and Amazon Athena for SQL queries. This works because S3 lifecycle rules automate the movement of log data from a hot storage class to a cold archive tier, meeting the 90-day retention and 5-year archival requirement at minimal cost, while Athena enables serverless SQL querying directly on the data stored in S3 without needing to load it into a separate database. On the AWS Certified Solutions Architect Professional SAP-C02 exam, this scenario tests your ability to distinguish cost-effective, serverless analytics from over-provisioned services like Redshift or RDS, which are unnecessary for simple log querying and incur higher operational overhead. A common trap is choosing Amazon OpenSearch Service, but it lacks native SQL support and is pricier for long-term archival. Memory tip: think “S3 + Athena = logs on a budget” — lifecycle handles the aging, Athena handles the querying.
SAP-C02 Design for New Solutions Practice Question
This SAP-C02 practice question tests your understanding of design for new solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 new solution to store and analyze large amounts of log data from multiple sources. The logs must be retained for 90 days for recent analysis, and then archived to a more cost-effective storage class for an additional 5 years. The solution must support SQL-based queries. Which combination of AWS services should the company use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Amazon S3 with lifecycle policies to transition objects to S3 Glacier Deep Archive after 90 days, and Amazon Athena for SQL queries.
Option A is correct because Amazon S3 can store logs with lifecycle policies to transition to S3 Glacier Deep Archive, and Amazon Athena can run SQL queries directly on S3. Option B is wrong because Amazon RDS is not cost-effective for large-scale log storage and querying. Option C is wrong because Amazon Redshift is optimized for data warehousing, not for simple log storage and querying, and may be overkill. Option D is wrong because Amazon OpenSearch Service is not SQL-based natively, and its storage costs are higher.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Amazon Redshift with data sharing to retain logs for 5 years.
Why it's wrong here
Redshift is for data warehousing, not for simple log storage.
✓
Amazon S3 with lifecycle policies to transition objects to S3 Glacier Deep Archive after 90 days, and Amazon Athena for SQL queries.
Static NAT maps one inside address to one outside address.
✗
Amazon OpenSearch Service with UltraWarm storage to archive logs after 90 days.
Why it's wrong here
OpenSearch does not natively support SQL and is more expensive for long-term storage.
✗
Amazon RDS for PostgreSQL with automated backups to retain logs for 5 years.
Why it's wrong here
RDS is not designed for large-scale log storage and is expensive.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related SAP-C02 NAT questions on configuration and troubleshooting.
Design for New Solutions — This question tests Design for New Solutions — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Amazon S3 with lifecycle policies to transition objects to S3 Glacier Deep Archive after 90 days, and Amazon Athena for SQL queries. — Option A is correct because Amazon S3 can store logs with lifecycle policies to transition to S3 Glacier Deep Archive, and Amazon Athena can run SQL queries directly on S3. Option B is wrong because Amazon RDS is not cost-effective for large-scale log storage and querying. Option C is wrong because Amazon Redshift is optimized for data warehousing, not for simple log storage and querying, and may be overkill. Option D is wrong because Amazon OpenSearch Service is not SQL-based natively, and its storage costs are higher.
What should I do if I get this SAP-C02 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related SAP-C02 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
About these practice questions
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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 wants to store application logs in a centralized location for analysis. The logs are generated by EC2 instances in an Auto Scaling group. The solution must be cost-effective and support real-time analysis. Which service should be used to collect and analyze the logs?
easy
A.Amazon S3
B.Amazon Kinesis Data Firehose
C.Amazon Elasticsearch Service
✓ D.Amazon CloudWatch Logs
Why D: Amazon CloudWatch Logs is the correct choice because it natively integrates with EC2 instances via the CloudWatch Logs agent to collect, monitor, and analyze log data in real time. It provides a centralized log storage with built-in metric filters and subscription filters for real-time analysis, making it cost-effective for logs generated by Auto Scaling groups without requiring additional infrastructure.
Variation 2. 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?
medium
A.Store data in Amazon S3 Glacier Deep Archive for cost savings, and use Athena for queries.
B.Store data in Amazon Redshift, use Redshift Spectrum to query data in S3.
✓ C.Store data in Amazon S3 using lifecycle policies to transition infrequent data to S3 Standard-IA. Use Amazon Athena for SQL queries.
D.Store data in Amazon EBS volumes attached to an EC2 instance running a SQL engine.
Why C: Option B is correct because S3 is the best storage for a data lake, and Athena allows serverless SQL queries directly on S3 data. Option A is wrong because Redshift is for data warehousing, not a data lake; data must be loaded. Option C is wrong because Glacier is for archival, not frequent querying. Option D is wrong because EBS is block storage for EC2, not designed for data lakes.
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Question Discussion
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