- A
Enable DynamoDB adaptive capacity and implement write sharding by adding a suffix to the partition key for high-volume users
Adaptive capacity automatically manages partition throughput, and write sharding distributes writes across multiple partitions, reducing hot spots.
- B
Increase the provisioned WCUs to 10000 to handle spikes
Why wrong: This increases cost and does not address the root cause of hot partitions; throttling may still occur on hot partitions.
- C
Switch the DynamoDB table to on-demand capacity mode
Why wrong: On-demand handles spikes but is more expensive for predictable workloads; it may also still throttle on hot partitions.
- D
Reduce the Lambda function concurrency to 100 to limit write requests
Why wrong: This would cause a backlog in Kinesis and increase end-to-end latency.
Quick Answer
The answer is to enable DynamoDB adaptive capacity and implement write sharding by adding a suffix to the partition key for high-volume users. This resolves hot partition write throttling because a small number of users are overwhelming a single partition with writes, and write sharding distributes those writes across multiple physical partitions by appending a random or calculated suffix to the user_id, while adaptive capacity automatically adjusts throughput to handle uneven access patterns without increasing provisioned capacity. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of DynamoDB partition design and the difference between scaling horizontally versus vertically—a common trap is to simply increase WCUs, which wastes cost without fixing the hot partition. Remember the memory tip: “Shard the hot keys, not the wallet,” meaning you spread the load on the partition key rather than spending more on throughput.
DEA-C01 Data Store Management Practice Question
This DEA-C01 practice question tests your understanding of data store management. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 runs a real-time analytics platform using Amazon Kinesis Data Streams with a shard count of 10. The data is consumed by an AWS Lambda function that writes to an Amazon DynamoDB table. The DynamoDB table has a partition key of 'user_id' and a sort key of 'timestamp'. The table is provisioned with 5000 RCUs and 5000 WCUs. Recently, the application experienced increased write latency and throttling errors (ProvisionedThroughputExceededException) on the DynamoDB table. The CloudWatch metrics show that ConsumedWriteCapacityUnits averages 4500 with occasional spikes to 6000. The Lambda function’s concurrency is set to 1000. The data engineer suspects the issue is due to hot partitions. Upon investigation, the engineer finds that a small number of users generate a disproportionately large amount of data. Which course of action would best resolve the throttling while minimizing cost?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
Enable DynamoDB adaptive capacity and implement write sharding by adding a suffix to the partition key for high-volume users
Option A is correct because the root cause is hot partitions caused by a small number of high-volume users. Enabling DynamoDB adaptive capacity allows the table to automatically adjust throughput to accommodate uneven access patterns, but the key fix is write sharding — adding a random or calculated suffix to the partition key for those high-volume users. This distributes writes across multiple physical partitions, eliminating the hot spot without requiring a global increase in provisioned capacity, thus resolving throttling while minimizing cost.
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 DynamoDB adaptive capacity and implement write sharding by adding a suffix to the partition key for high-volume users
Why this is correct
Adaptive capacity automatically manages partition throughput, and write sharding distributes writes across multiple partitions, reducing hot spots.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the provisioned WCUs to 10000 to handle spikes
Why it's wrong here
This increases cost and does not address the root cause of hot partitions; throttling may still occur on hot partitions.
- ✗
Switch the DynamoDB table to on-demand capacity mode
Why it's wrong here
On-demand handles spikes but is more expensive for predictable workloads; it may also still throttle on hot partitions.
- ✗
Reduce the Lambda function concurrency to 100 to limit write requests
Why it's wrong here
This would cause a backlog in Kinesis and increase end-to-end latency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that throttling is always solved by increasing total provisioned capacity or switching to on-demand, when the real issue is partition-level hot spots that require key design changes like write sharding.
Detailed technical explanation
How to think about this question
DynamoDB partitions have a hard limit of 1,000 WCUs per partition for provisioned tables (3,000 RCUs). Adaptive capacity can temporarily burst beyond this limit, but sustained hot spots require write sharding. A common pattern is to append a random number (e.g., 0–9) to the partition key for high-volume users, then use a composite key or secondary index to retrieve data. In real-world scenarios, this is critical for IoT or gaming leaderboards where a few users generate the majority of writes.
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Data Store Management — study guide chapter
Learn the concepts, then practise the questions
- →
Data Store Management practice questions
Targeted practice on this topic area only
- →
All DEA-C01 questions
1,786 questions across all exam domains
- →
AWS Certified Data Engineer Associate DEA-C01 study guide
Full concept coverage aligned to exam objectives
- →
DEA-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related DEA-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Data Ingestion and Transformation practice questions
Practise DEA-C01 questions linked to Data Ingestion and Transformation.
Data Operations and Support practice questions
Practise DEA-C01 questions linked to Data Operations and Support.
Data Security and Governance practice questions
Practise DEA-C01 questions linked to Data Security and Governance.
Data Store Management practice questions
Practise DEA-C01 questions linked to Data Store Management.
DEA-C01 fundamentals practice questions
Practise DEA-C01 questions linked to DEA-C01 fundamentals.
DEA-C01 scenario practice questions
Practise DEA-C01 questions linked to DEA-C01 scenario.
DEA-C01 troubleshooting practice questions
Practise DEA-C01 questions linked to DEA-C01 troubleshooting.
Practice this exam
Start a free DEA-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Store Management — This question tests Data Store Management — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable DynamoDB adaptive capacity and implement write sharding by adding a suffix to the partition key for high-volume users — Option A is correct because the root cause is hot partitions caused by a small number of high-volume users. Enabling DynamoDB adaptive capacity allows the table to automatically adjust throughput to accommodate uneven access patterns, but the key fix is write sharding — adding a random or calculated suffix to the partition key for those high-volume users. This distributes writes across multiple physical partitions, eliminating the hot spot without requiring a global increase in provisioned capacity, thus resolving throttling while minimizing cost.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more ways this is tested on DEA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company runs a real-time analytics platform on Amazon ECS that ingests streaming data from Amazon Kinesis Data Streams, processes it, and stores results in Amazon DynamoDB. The data volume spikes unpredictably, causing DynamoDB to throttle write requests. The application uses on-demand capacity mode. The data engineer notices that the throttling occurs on a specific partition due to a hot key. The hot key is a customer ID that receives a disproportionate number of writes. The application cannot change the partition key design immediately. The engineer needs to reduce throttling while maintaining low latency. Which solution is most effective?
hard- A.Switch to provisioned capacity with auto scaling and increase the write capacity units.
- ✓ B.Implement a write buffer using Amazon SQS, and have consumers write to DynamoDB at a controlled rate.
- C.Enable DynamoDB Accelerator (DAX) to cache the hot key writes.
- D.Use DynamoDB Streams to trigger a Lambda function that retries throttled writes.
Why B: Option B is correct because buffering writes through Amazon SQS decouples the ingestion rate from DynamoDB's capacity, allowing consumers to write at a controlled pace. This directly mitigates throttling on the hot key without requiring a partition key redesign, and SQS provides low-latency, durable buffering suitable for real-time analytics.
Keep practising
More DEA-C01 practice questions
- A data pipeline uses Kinesis Data Firehose to deliver streaming data to an S3 bucket. The data volume spikes occasionall…
- An e-commerce company uses AWS Glue to run ETL jobs that transform clickstream data from Amazon S3. The job reads Parque…
- A data engineering team uses Amazon Kinesis Data Analytics for Apache Flink to process streaming data. They notice that…
- A company uses AWS Glue to process streaming data from Amazon Kinesis Data Streams. The job reads JSON records and write…
- A data engineer is designing a serverless data ingestion pipeline that uses Amazon Kinesis Data Firehose to deliver data…
- A company runs a nightly AWS Glue ETL job that reads from a JDBC source (PostgreSQL) and writes to S3 in Parquet format.…
Last reviewed: Jun 30, 2026
This DEA-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 DEA-C01 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.