- A
Enable Multi-AZ deployment to improve read performance.
Why wrong: Multi-AZ improves availability, not query performance related to indexes.
- B
Shard the collection across multiple DocumentDB clusters.
Why wrong: DocumentDB supports sharding, but that does not address the index limitation.
- C
Use the MongoDB aggregation pipeline to bypass indexing.
Why wrong: Aggregation pipeline may still require indexes for performance; not a direct workaround.
- D
Evaluate using covered queries instead of secondary indexes.
This is correct. Amazon DocumentDB fully supports secondary indexes, but for reporting queries that frequently access the same fields, covered queries (where all required data is retrieved from the index without accessing the documents) can provide significant performance benefits by reducing I/O and index overhead. Therefore, evaluating covered queries is a key design consideration.
Evaluating Covered Queries vs Secondary Indexes in DocumentDB
This DBS-C01 practice question tests your understanding of workload-specific database design. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 migrating an on-premises MongoDB workload to Amazon DocumentDB. The current workload uses secondary indexes heavily for reporting queries. Which design consideration should the company evaluate to ensure optimal performance on DocumentDB?
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
Evaluate using covered queries instead of secondary indexes.
Amazon DocumentDB fully supports secondary indexes, similar to MongoDB. However, for reporting workloads that frequently query the same fields, covered queries (which retrieve all required data from the index without accessing the underlying documents) can provide significant performance benefits by reducing I/O and eliminating document lookups. This can lower index maintenance overhead and storage costs, making it a valuable design consideration when heavy reliance on secondary indexes is expected. The key is to design indexes that support covered queries for the most common reporting queries, rather than assuming secondary indexes alone will suffice.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable Multi-AZ deployment to improve read performance.
Why it's wrong here
Multi-AZ improves availability, not query performance related to indexes.
- ✗
Shard the collection across multiple DocumentDB clusters.
Why it's wrong here
DocumentDB supports sharding, but that does not address the index limitation.
- ✗
Use the MongoDB aggregation pipeline to bypass indexing.
Why it's wrong here
Aggregation pipeline may still require indexes for performance; not a direct workaround.
- ✓
Evaluate using covered queries instead of secondary indexes.
Why this is correct
This is correct. Amazon DocumentDB fully supports secondary indexes, but for reporting queries that frequently access the same fields, covered queries (where all required data is retrieved from the index without accessing the documents) can provide significant performance benefits by reducing I/O and index overhead. Therefore, evaluating covered queries is a key design consideration.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume secondary indexes are always necessary for query performance, overlooking that covered queries can eliminate document lookups and reduce index overhead, which is a key optimization for DocumentDB's architecture.
Detailed technical explanation
How to think about this question
Covered queries work when all fields in the query projection and filter are included in a single index, allowing DocumentDB to return results directly from the index without fetching documents. This is particularly effective for reporting queries that access a subset of fields, as it reduces disk I/O and memory pressure. In practice, designing indexes to support covered queries can significantly reduce the number of secondary indexes needed, lowering write amplification and storage costs.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
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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 — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Evaluate using covered queries instead of secondary indexes. — Amazon DocumentDB fully supports secondary indexes, similar to MongoDB. However, for reporting workloads that frequently query the same fields, covered queries (which retrieve all required data from the index without accessing the underlying documents) can provide significant performance benefits by reducing I/O and eliminating document lookups. This can lower index maintenance overhead and storage costs, making it a valuable design consideration when heavy reliance on secondary indexes is expected. The key is to design indexes that support covered queries for the most common reporting queries, rather than assuming secondary indexes alone will suffice.
What should I do if I get this DBS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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