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Question 878 of 1,730

Workload-Specific Database Design →hardMultiple ChoiceObjective-mapped

Quick Answer

The correct answer is to partition the 'orders' table by 'status' and 'order_date' and create covering indexes on common query patterns. This approach directly addresses the root cause of slow queries on a large RDS MySQL table by using partitioning to prune the data scanned—since 80% of rows are 'completed' status and queries typically target the last 30 days, partitioning by status first eliminates the majority of irrelevant rows, then by date further narrows the scan range. Adding covering indexes ensures that all columns needed by the query are included in the index itself, avoiding costly table lookups. On the AWS Certified Database Specialty exam, this scenario tests your understanding that indexing alone is insufficient when data is skewed; partitioning is the key to physical data organization for query performance. A common trap is to choose vertical scaling or read replicas, which mask the problem without fixing the query plan. Memory tip: “Partition to prune, cover to avoid the book.”

DBS-C01 Workload-Specific Database Design Practice Question

This DBS-C01 practice question tests your understanding of workload-specific database design. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 runs an e-commerce platform on Amazon RDS for MySQL with a Multi-AZ deployment. The database has a table 'orders' with 50 million rows. During Black Friday sales, the application experiences severe slowdowns. Analysis shows that the CPU utilization is at 90% and there are many slow queries that perform full table scans on the 'orders' table. The development team has already added indexes on the most queried columns, but the problem persists. The database specialist suspects that the issue is not solely due to missing indexes. They notice that the queries often filter on a combination of 'order_date', 'customer_id', and 'status', and that the data distribution is heavily skewed: 80% of orders are 'completed' status. The 'order_date' range is typically the last 30 days. What should the database specialist do to improve query performance?

Question 1hardmultiple choice

Read the full NAT/PAT explanation →

A
Partition the 'orders' table by 'status' and 'order_date' and create covering indexes on common query patterns.
Partitioning reduces the data scanned, and covering indexes speed up queries without accessing the table.
B
Create multiple read replicas and distribute read traffic.
Why wrong: Read replicas offload read traffic but each replica still executes the same slow queries.
C
Implement an in-memory caching layer using Amazon ElastiCache for frequently accessed data.
Why wrong: Caching helps with repeated queries but not with the broad range of queries filtering on different date ranges.
D
Upgrade the RDS instance to a larger instance class with more vCPUs and memory.
Why wrong: Scaling up provides temporary relief but does not fix the full table scan issue.

Full breakdown with real-world context →

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

✓

Partition the 'orders' table by 'status' and 'order_date' and create covering indexes on common query patterns.

Option D is correct because partitioning the table by 'status' and 'order_date' can significantly reduce the amount of data scanned, as queries often filter on these columns. In addition, using a covering index can avoid lookups. Option A (vertical scaling) provides temporary relief but does not address the root cause. Option B (read replicas) offloads read traffic but does not fix the slow queries themselves. Option C (cache layer) can help with repeated queries but not with ad-hoc analytical queries that still scan large datasets.

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.

✓
Partition the 'orders' table by 'status' and 'order_date' and create covering indexes on common query patterns.
Why this is correct
Partitioning reduces the data scanned, and covering indexes speed up queries without accessing the table.
Related concept
Static NAT maps one inside address to one outside address.
✗
Create multiple read replicas and distribute read traffic.
Why it's wrong here
Read replicas offload read traffic but each replica still executes the same slow queries.
✗
Implement an in-memory caching layer using Amazon ElastiCache for frequently accessed data.
Why it's wrong here
Caching helps with repeated queries but not with the broad range of queries filtering on different date ranges.
✗
Upgrade the RDS instance to a larger instance class with more vCPUs and memory.
Why it's wrong here
Scaling up provides temporary relief but does not fix the full table scan issue.

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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.

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 DBS-C01 NAT questions on configuration and troubleshooting.

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FAQ

Questions learners often ask

What does this DBS-C01 question test?

Workload-Specific Database Design — This question tests Workload-Specific Database Design — Static NAT maps one inside address to one outside address..

What is the correct answer to this question?

The correct answer is: Partition the 'orders' table by 'status' and 'order_date' and create covering indexes on common query patterns. — Option D is correct because partitioning the table by 'status' and 'order_date' can significantly reduce the amount of data scanned, as queries often filter on these columns. In addition, using a covering index can avoid lookups. Option A (vertical scaling) provides temporary relief but does not address the root cause. Option B (read replicas) offloads read traffic but does not fix the slow queries themselves. Option C (cache layer) can help with repeated queries but not with ad-hoc analytical queries that still scan large datasets.

What should I do if I get this DBS-C01 question wrong?

What is the key concept behind this question?

Static NAT maps one inside address to one outside address.

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This DBS-C01 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 DBS-C01 exam.