The correct answer is to increase the DynamoDB write capacity units to 100 and enable auto scaling, because the `ProvisionedThroughputExceededException` directly indicates that the table’s 10 WCU is insufficient for the burst of writes from the Lambda aggregation. Even though the average write rate is roughly 100 writes per minute (one per symbol), the Lambda function writes in bursts at the end of each 1-minute window, and with 5,000 trades per second being aggregated, the instantaneous write demand far exceeds 10 WCU. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your ability to diagnose DynamoDB write capacity throttling in a Kinesis Lambda pipeline, where the common trap is to focus on shard count or Lambda concurrency instead of the actual throttling source. A key memory tip is “follow the error code”—the `ProvisionedThroughputExceededException` always points to DynamoDB capacity, not stream throughput, so always check WCU first before scaling shards or changing services.
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. 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 financial services company ingests real-time stock trade data from multiple exchanges into Amazon Kinesis Data Streams. Each trade record is a JSON object with fields: trade_id, symbol, price, quantity, timestamp. The stream has 5 shards. The data is consumed by an AWS Lambda function that aggregates trades per symbol every minute and writes the results to an Amazon DynamoDB table for a real-time dashboard. Recently, the dashboard has been showing outdated data, and the Lambda function is experiencing high error rates. The CloudWatch logs show 'ProvisionedThroughputExceededException' errors from DynamoDB. The DynamoDB table has 10 read capacity units (RCU) and 10 write capacity units (WCU). The average trade volume is 5,000 trades per second across all symbols, and there are 100 symbols. The Lambda function is configured with a batch size of 100 and a 1-minute window. The data volume is expected to double in the next month. As a data engineer, what is the most appropriate course of action?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the DynamoDB write capacity units to 100 and enable auto scaling
Option C is correct because the DynamoDB table is throttling due to insufficient write capacity. With 5,000 trades/s and updating per symbol per minute, the write rate is about 100 writes per minute (one per symbol), but the aggregation may cause bursts. However, the 'ProvisionedThroughputExceededException' indicates WCU is too low. Increasing WCU to 100 resolves the immediate issue; auto scaling handles future growth. Option A (increase shards) addresses Lambda concurrency but not DynamoDB throttling. Option B (use S3) changes the architecture and loses real-time capabilities. Option D (use Firehose) is for delivery to S3, not real-time dashboard.
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 storage from DynamoDB to Amazon S3 and use Amazon Athena for the dashboard
Why it's wrong here
S3 and Athena are not suitable for real-time dashboard updates.
✗
Increase the number of Kinesis shards to 10 to increase Lambda concurrency
Why it's wrong here
More shards increase Lambda concurrency but do not solve DynamoDB write throttling.
✓
Increase the DynamoDB write capacity units to 100 and enable auto scaling
Why this is correct
Resolves the write throttling error and auto scaling handles future growth.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Amazon Kinesis Data Firehose to deliver data to S3 and use Amazon QuickSight for the dashboard
Why it's wrong here
Firehose and QuickSight introduce latency, not suitable for real-time.
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
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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.
What to study next
Got this wrong? Here's your next step.
Identify which DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the DynamoDB write capacity units to 100 and enable auto scaling — Option C is correct because the DynamoDB table is throttling due to insufficient write capacity. With 5,000 trades/s and updating per symbol per minute, the write rate is about 100 writes per minute (one per symbol), but the aggregation may cause bursts. However, the 'ProvisionedThroughputExceededException' indicates WCU is too low. Increasing WCU to 100 resolves the immediate issue; auto scaling handles future growth. Option A (increase shards) addresses Lambda concurrency but not DynamoDB throttling. Option B (use S3) changes the architecture and loses real-time capabilities. Option D (use Firehose) is for delivery to S3, not real-time dashboard.
What should I do if I get this DEA-C01 question wrong?
Identify which DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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