- A
Use global secondary indexes to speed up queries on the timestamp field.
Why wrong: DocumentDB does not support global secondary indexes; it uses standard secondary indexes.
- B
Implement sharding to distribute write load across multiple instances.
Sharding helps scale writes by distributing data across shards.
- C
Enable a TTL index on the timestamp field to automatically delete old data.
TTL indexes help manage data lifecycle and keep the collection size manageable.
- D
Create a compound index on (device_id, timestamp) to support range queries.
Compound indexes improve performance for queries that filter on both fields.
- E
Use a single large instance class to avoid sharding complexity.
Why wrong: For high write throughput, sharding is more scalable than a single large instance.
Quick Answer
The answer is to create a compound index on (device_id, timestamp) to support range queries. This is correct because MongoDB to DocumentDB migration for IoT time-series workloads requires careful index design to handle the dual demands of range queries on timestamp fields and frequent updates to recent documents. DocumentDB does not support native sharding like MongoDB, so for high write throughput, you must distribute writes across multiple instances using a shard key in the application layer, avoiding write bottlenecks on a single instance. On the AWS Certified Database Specialty DBS-C01 exam, this scenario tests your understanding of DocumentDB’s indexing limitations and horizontal scaling patterns—a common trap is assuming native sharding exists or that a single index on timestamp alone suffices. Remember the memory tip: “Index the shard key first, then the time” to optimize both range scans and write distribution.
DBS-C01 Workload-Specific Database Design Practice Question
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 database to Amazon DocumentDB. The database stores IoT sensor data with time-series characteristics. The application performs range queries on timestamp fields and updates recent documents frequently. Which THREE aspects of DocumentDB should the company consider to optimize performance for this workload? (Choose three.)
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
Implement sharding to distribute write load across multiple instances.
Option B is correct because DocumentDB does not support native sharding like MongoDB; however, for workloads with high write throughput, you can distribute writes by using multiple DocumentDB instances and routing writes based on a shard key in the application layer. This helps avoid write bottlenecks on a single instance and scales write capacity horizontally.
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.
- ✗
Use global secondary indexes to speed up queries on the timestamp field.
Why it's wrong here
DocumentDB does not support global secondary indexes; it uses standard secondary indexes.
- ✓
Implement sharding to distribute write load across multiple instances.
Why this is correct
Sharding helps scale writes by distributing data across shards.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Enable a TTL index on the timestamp field to automatically delete old data.
Why this is correct
TTL indexes help manage data lifecycle and keep the collection size manageable.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Create a compound index on (device_id, timestamp) to support range queries.
Why this is correct
Compound indexes improve performance for queries that filter on both fields.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a single large instance class to avoid sharding complexity.
Why it's wrong here
For high write throughput, sharding is more scalable than a single large instance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume DocumentDB supports sharding natively like MongoDB, but DocumentDB does not have built-in sharding; instead, you must implement application-level sharding or use multiple clusters to distribute write load.
Detailed technical explanation
How to think about this question
DocumentDB uses a shared storage volume that automatically replicates data six ways across three Availability Zones, but write throughput is limited by the primary instance's compute capacity. For time-series workloads with frequent updates, a compound index on (device_id, timestamp) enables efficient range queries by allowing the query engine to use index intersection or a single index scan, reducing the number of documents scanned. TTL indexes in DocumentDB automatically delete expired documents based on the timestamp field, which helps manage storage growth and maintains query performance without manual intervention.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
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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: Implement sharding to distribute write load across multiple instances. — Option B is correct because DocumentDB does not support native sharding like MongoDB; however, for workloads with high write throughput, you can distribute writes by using multiple DocumentDB instances and routing writes based on a shard key in the application layer. This helps avoid write bottlenecks on a single instance and scales write capacity horizontally.
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 11, 2026
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.
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