- A
Switch the table to provisioned capacity and enable auto-scaling.
Why wrong: On-demand tables already handle spikes; auto-scaling is for provisioned mode.
- B
Increase the write capacity units to handle the peak load.
Why wrong: On-demand tables do not use write capacity units.
- C
Enable S3 bucket versioning to reduce the number of writes.
Why wrong: S3 versioning does not affect DynamoDB operations.
- D
Implement DynamoDB Accelerator (DAX) to cache frequent reads.
DAX reduces read load on the table, lowering throttling.
Question 1,627 of 1,786
DEA-C01 Data Operations and Support Practice Question
This DEA-C01 practice question tests your understanding of data operations and support. 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 team manages an Amazon DynamoDB table with on-demand capacity. Recently, they noticed increased throttling errors during peak hours. The table has a Lambda trigger that processes changes and writes to an S3 bucket. Which design change would BEST reduce throttling?
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 DynamoDB Accelerator (DAX) to cache frequent reads.
Option D is correct because DynamoDB Accelerator (DAX) provides an in-memory cache that reduces the number of read requests hitting the table, which can alleviate throttling during peak hours. The question describes throttling errors, which are typically caused by exceeding the table's read or write capacity; DAX offloads read traffic, reducing the load on the table and thus decreasing throttling events.
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.
- ✗
Switch the table to provisioned capacity and enable auto-scaling.
Why it's wrong here
On-demand tables already handle spikes; auto-scaling is for provisioned mode.
- ✗
Increase the write capacity units to handle the peak load.
Why it's wrong here
On-demand tables do not use write capacity units.
- ✗
Enable S3 bucket versioning to reduce the number of writes.
Why it's wrong here
S3 versioning does not affect DynamoDB operations.
- ✓
Implement DynamoDB Accelerator (DAX) to cache frequent reads.
Why this is correct
DAX reduces read load on the table, lowering throttling.
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 may assume throttling is always due to insufficient write capacity, but the question's context of a Lambda trigger writing to S3 can increase read traffic (e.g., via stream processing or re-reading items), making DAX a read-side solution that addresses the actual cause.
Detailed technical explanation
How to think about this question
DynamoDB on-demand capacity uses a per-partition throughput limit of 3,000 read request units (RRU) or 1,000 write request units (WRU) per partition, and throttling occurs when a single partition's traffic exceeds these limits. DAX acts as a write-through cache that absorbs repeated read requests, reducing the read load on individual partitions and preventing throttling without requiring capacity changes. In real-world scenarios, a Lambda trigger that processes DynamoDB Streams and writes to S3 can generate high read traffic if the Lambda function re-reads the same items, making DAX an effective mitigation.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Operations and Support — This question tests Data Operations and Support — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Implement DynamoDB Accelerator (DAX) to cache frequent reads. — Option D is correct because DynamoDB Accelerator (DAX) provides an in-memory cache that reduces the number of read requests hitting the table, which can alleviate throttling during peak hours. The question describes throttling errors, which are typically caused by exceeding the table's read or write capacity; DAX offloads read traffic, reducing the load on the table and thus decreasing throttling events.
What should I do if I get this DEA-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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