- A
Increase the DynamoDB table's provisioned read capacity units to a high static value.
Why wrong: Static high capacity is costly and may still be exceeded during extreme bursts.
- B
Use an Amazon SQS queue to buffer the Lambda requests before querying DynamoDB.
Why wrong: Buffering with SQS adds latency and does not prevent DynamoDB throttling if the requests are still bursty.
- C
Enable DynamoDB auto scaling for the table to automatically adjust read capacity based on demand.
Auto scaling adjusts capacity dynamically to handle bursts without manual intervention.
- D
Configure an Amazon SNS topic to throttle the data stream before it reaches Lambda.
Why wrong: SNS is for pub/sub messaging, not for throttling or buffering data streams.
Question 803 of 1,755
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. 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 is streaming real-time sensor data from IoT devices to Amazon Kinesis Data Streams. The data is then consumed by an AWS Lambda function that enriches the records with metadata from an Amazon DynamoDB table and writes the results to an Amazon S3 bucket. Recently, the Lambda function has been failing with 'ProvisionedThroughputExceededException' errors from DynamoDB. The data volume is variable, with occasional bursts. Which solution should a data engineer implement to resolve this issue without losing data?
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 auto scaling for the table to automatically adjust read capacity based on demand.
Option C is correct because DynamoDB auto scaling dynamically adjusts the table's provisioned read capacity based on actual traffic patterns, handling bursty sensor data without manual intervention. This prevents ProvisionedThroughputExceededExceptions while ensuring no data loss, as the Lambda function can retry failed operations. Auto scaling is the most cost-effective and operationally efficient solution for variable workloads.
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.
- ✗
Increase the DynamoDB table's provisioned read capacity units to a high static value.
Why it's wrong here
Static high capacity is costly and may still be exceeded during extreme bursts.
- ✗
Use an Amazon SQS queue to buffer the Lambda requests before querying DynamoDB.
Why it's wrong here
Buffering with SQS adds latency and does not prevent DynamoDB throttling if the requests are still bursty.
- ✓
Enable DynamoDB auto scaling for the table to automatically adjust read capacity based on demand.
Why this is correct
Auto scaling adjusts capacity dynamically to handle bursts without manual intervention.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Configure an Amazon SNS topic to throttle the data stream before it reaches Lambda.
Why it's wrong here
SNS is for pub/sub messaging, not for throttling or buffering data streams.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse buffering the Lambda invocation (Option B) with addressing the DynamoDB throttling error, but the error occurs inside the Lambda function after invocation, so an SQS queue does not solve the read capacity issue.
Detailed technical explanation
How to think about this question
DynamoDB auto scaling uses the AWS Application Auto Scaling service to adjust provisioned read/write capacity based on a target utilization (default 70%). It monitors consumed capacity over a 5-minute window and scales up or down gradually, which is ideal for bursty IoT workloads. However, auto scaling has a cooldown period (typically 5–10 minutes), so extremely sudden spikes may still cause throttling; in such cases, a DynamoDB Accelerator (DAX) or on-demand capacity mode could be considered as alternatives.
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.
Visual reference
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
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable DynamoDB auto scaling for the table to automatically adjust read capacity based on demand. — Option C is correct because DynamoDB auto scaling dynamically adjusts the table's provisioned read capacity based on actual traffic patterns, handling bursty sensor data without manual intervention. This prevents ProvisionedThroughputExceededExceptions while ensuring no data loss, as the Lambda function can retry failed operations. Auto scaling is the most cost-effective and operationally efficient solution for variable workloads.
What should I do if I get this MLS-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: Jul 4, 2026
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