- A
Use Amazon DynamoDB export to S3 (incremental) feature instead of Glue
The export feature does not consume read capacity and can be automated.
- B
Increase the DynamoDB write capacity units to handle the stream writes
Why wrong: Write capacity is for writes, not relevant to Glue's reads.
- C
Use DynamoDB Streams with AWS Lambda to write data directly to S3 in near-real-time, bypassing Glue
Lambda can write to S3 without consuming DynamoDB read capacity repeatedly.
- D
Increase the DynamoDB read capacity units to handle Glue's workload
Why wrong: Increasing read capacity increases cost and load, not reduces.
- E
Configure Glue to read from a S3 snapshot exported earlier instead of directly from DynamoDB
Reading from S3 reduces the load on DynamoDB.
Question 450 of 1,755
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. 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 company is using Amazon DynamoDB as a source for a machine learning pipeline. The data is exported nightly to Amazon S3 using DynamoDB Streams and an AWS Glue job. The Glue job reads the stream records, transforms them, and writes to S3 in Parquet format. The team notices that the Glue job is taking too long and consuming high DynamoDB read capacity. Which THREE actions would reduce the load on DynamoDB and improve performance? (Choose THREE.)
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
✓
Use Amazon DynamoDB export to S3 (incremental) feature instead of Glue
Option A is correct because DynamoDB's native export to S3 (incremental) feature directly exports data to S3 without consuming read capacity units (RCUs) or requiring a separate compute service like AWS Glue. This eliminates the bottleneck of Glue reading from DynamoDB Streams, which consumes RCUs and adds latency, thereby reducing load on DynamoDB and improving overall performance.
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.
- ✓
Use Amazon DynamoDB export to S3 (incremental) feature instead of Glue
Why this is correct
The export feature does not consume read capacity and can be automated.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the DynamoDB write capacity units to handle the stream writes
Why it's wrong here
Write capacity is for writes, not relevant to Glue's reads.
- ✓
Use DynamoDB Streams with AWS Lambda to write data directly to S3 in near-real-time, bypassing Glue
Why this is correct
Lambda can write to S3 without consuming DynamoDB read capacity repeatedly.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the DynamoDB read capacity units to handle Glue's workload
Why it's wrong here
Increasing read capacity increases cost and load, not reduces.
- ✓
Configure Glue to read from a S3 snapshot exported earlier instead of directly from DynamoDB
Why this is correct
Reading from S3 reduces the load on DynamoDB.
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 often assume increasing DynamoDB capacity (RCUs or WCUs) is the solution to performance issues, but the exam tests understanding that native export features and architectural changes (like using Lambda or S3 snapshots) can eliminate the root cause of high read consumption without scaling capacity.
Detailed technical explanation
How to think about this question
DynamoDB's incremental export to S3 uses a point-in-time recovery (PITR) mechanism to export data without consuming RCUs, as it reads from internal backups rather than the live table. This contrasts with DynamoDB Streams, which captures item-level changes in near-real-time and requires RCUs for reads when processed by Glue or Lambda. In real-world scenarios, using native export is ideal for large-scale nightly batch exports, while Lambda with Streams suits near-real-time needs but may still incur RCU costs for stream processing.
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
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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: Use Amazon DynamoDB export to S3 (incremental) feature instead of Glue — Option A is correct because DynamoDB's native export to S3 (incremental) feature directly exports data to S3 without consuming read capacity units (RCUs) or requiring a separate compute service like AWS Glue. This eliminates the bottleneck of Glue reading from DynamoDB Streams, which consumes RCUs and adds latency, thereby reducing load on DynamoDB and improving overall performance.
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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