DEA-C01 Data Store Management Practice Question
A data engineer is managing an Amazon DynamoDB table that stores user session data. The table has a partition key of user_id and a sort key of session_start_time. The workload includes frequent queries that retrieve all sessions for a user within a specific time range. The engineer notices that some queries are slow and wants to optimize the table design. Which action should the engineer take to improve query performance?
⚠ Common exam trap
The trap here is assuming that adding a GSI or increasing capacity will solve slow queries, when the base table design is already correct and the issue may be query patterns.
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
✓
Ensure that queries use the partition key and sort key condition to retrieve data efficiently, and consider enabling adaptive capacity.
The correct action is to ensure queries use the partition key and sort key condition to retrieve data efficiently, and to consider enabling adaptive capacity. The table's key schema is already optimized for the query pattern, so leveraging it properly is key. Other options introduce unnecessary complexity, do not address the query pattern, or simply scale capacity without fixing efficiency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Ensure that queries use the partition key and sort key condition to retrieve data efficiently, and consider enabling adaptive capacity.
Why this is correct
The base table's key schema (user_id as partition key, session_start_time as sort key) is ideal for the query pattern. Using both keys in queries allows DynamoDB to efficiently locate the relevant items. Enabling adaptive capacity helps handle uneven access patterns by automatically adjusting throughput. This approach directly addresses performance by leveraging the existing design and DynamoDB's built-in optimizations.
- ✗
Increase the provisioned read capacity units (RCUs) for the table to handle the query load.
Why it's wrong here
Increasing RCUs can improve throughput if the table is throttled, but it does not address query efficiency. If queries are slow due to inefficient access patterns (e.g., scanning without using the sort key), adding capacity will not solve the problem and may increase cost. The engineer should first ensure queries are using the key schema effectively before scaling capacity.
- ✗
Create a global secondary index (GSI) with session_start_time as the partition key and user_id as the sort key.
Why it's wrong here
A GSI with session_start_time as the partition key would distribute data based on time, which may lead to hot partitions if many sessions start at the same time. It also does not align with the query pattern of retrieving sessions for a specific user within a time range. The base table already supports this query efficiently with user_id as partition key and session_start_time as sort key.
- ✗
Enable DynamoDB Streams to capture changes and use AWS Lambda to maintain a separate queryable table.
Why it's wrong here
DynamoDB Streams and Lambda can be used for materialized views or replication, but this adds complexity and latency. It does not directly optimize the existing table for the described query pattern. The base table's key schema is already well-suited for retrieving sessions by user and time range. This approach is an over-engineering solution that does not address the root cause of slow queries.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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