- A
The DAX cluster is not large enough to cache all items, causing cache misses
Why wrong: Incorrect. A cache miss forces a read from DynamoDB, which returns the latest data; it does not cause stale reads.
- B
DAX is configured with eventual consistency, which returns stale data by design
Why wrong: Incorrect. While DAX uses eventual consistency, the staleness here is due to TTL not being short enough to invalidate updated items, not due to eventual consistency itself.
- C
The DAX cluster is deployed in a different Availability Zone than the application
Why wrong: Incorrect. DAX is accessible across Availability Zones within the same VPC; AZ placement does not affect data freshness.
- D
The TTL is too long, causing cached items to remain after updates
Correct. DAX uses a write-through cache for reads, and when a DynamoDB item is updated, the cached version remains until TTL expiry. If TTL is longer than the update frequency, stale data is served.
DBS-C01 DAX Write-Through Cache Practice Question
This DBS-C01 practice question tests your understanding of monitoring and troubleshooting. 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. A key principle to apply: dAX Write-Through Cache. 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 is using Amazon DynamoDB Accelerator (DAX) to improve read performance for a table. They notice that DAX is returning stale data even though the TTL is set to 5 minutes. The table is updated frequently by multiple writers. What is the most likely cause of the stale reads?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
The TTL is too long, causing cached items to remain after updates
The correct answer is D. DAX uses a write-through cache, meaning that items are cached only when they are read. When an item is updated in DynamoDB, the cached copy is not automatically invalidated; instead, it remains in the cache until the TTL expires. If the TTL is set too long (e.g., 5 minutes) and the item is updated frequently, stale data will be served from the cache until the TTL forces its removal. Option A is incorrect because cache misses would cause slower reads, not stale reads. Option B is incorrect because DAX always uses eventual consistency for reads, but stale data occurs here due to TTL, not consistency level. Option C is incorrect because DAX clusters are deployed in a single VPC and can be accessed from any AZ with proper routing; AZ placement does not cause staleness.
Key principle: DAX Write-Through Cache
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The DAX cluster is not large enough to cache all items, causing cache misses
Why it's wrong here
Incorrect. A cache miss forces a read from DynamoDB, which returns the latest data; it does not cause stale reads.
- ✗
DAX is configured with eventual consistency, which returns stale data by design
Why it's wrong here
Incorrect. While DAX uses eventual consistency, the staleness here is due to TTL not being short enough to invalidate updated items, not due to eventual consistency itself.
- ✗
The DAX cluster is deployed in a different Availability Zone than the application
Why it's wrong here
Incorrect. DAX is accessible across Availability Zones within the same VPC; AZ placement does not affect data freshness.
- ✓
The TTL is too long, causing cached items to remain after updates
Why this is correct
Correct. DAX uses a write-through cache for reads, and when a DynamoDB item is updated, the cached version remains until TTL expiry. If TTL is longer than the update frequency, stale data is served.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
DAX Write-Through Cache
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- DAX Write-Through Cache
- TTL (Time-to-Live)
- Cache Invalidation
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
DAX Write-Through Cache
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 dAX Write-Through Cache, then practise related DBS-C01 questions on the same topic to reinforce the concept.
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Monitoring and Troubleshooting — study guide chapter
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FAQ
Questions learners often ask
What does this DBS-C01 question test?
Monitoring and Troubleshooting — This question tests Monitoring and Troubleshooting — DAX Write-Through Cache.
What is the correct answer to this question?
The correct answer is: The TTL is too long, causing cached items to remain after updates — The correct answer is D. DAX uses a write-through cache, meaning that items are cached only when they are read. When an item is updated in DynamoDB, the cached copy is not automatically invalidated; instead, it remains in the cache until the TTL expires. If the TTL is set too long (e.g., 5 minutes) and the item is updated frequently, stale data will be served from the cache until the TTL forces its removal. Option A is incorrect because cache misses would cause slower reads, not stale reads. Option B is incorrect because DAX always uses eventual consistency for reads, but stale data occurs here due to TTL, not consistency level. Option C is incorrect because DAX clusters are deployed in a single VPC and can be accessed from any AZ with proper routing; AZ placement does not cause staleness.
What should I do if I get this DBS-C01 question wrong?
Review dAX Write-Through Cache, then practise related DBS-C01 questions on the same topic to reinforce the concept.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
DAX Write-Through Cache
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Last reviewed: Jun 20, 2026
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