MLS-C01 Data Engineering Practice Question
An ML team is building a recommendation system. The training data includes user-item interactions stored in Amazon DynamoDB. The team wants to export this data to S3 in Parquet format for use with Amazon SageMaker. The export should be incremental (only new or changed records) and run daily. Which approach meets these requirements with MINIMAL operational overhead?
⚠ Common exam trap
A common mix-up: candidates choose Option A because they assume 'Export to S3' is incremental, but it actually exports the entire table, not just changes, leading to higher costs and redundant data processing.
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 DynamoDB Streams with AWS Lambda to write changes to S3 in Parquet format.
DynamoDB Streams capture every change (insert, update, delete) in near real-time, and AWS Lambda can process these events to write only the changed records to S3 in Parquet format. This approach provides incremental, daily exports with minimal operational overhead, as it is fully serverless and requires no infrastructure management.
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 the DynamoDB Export to S3 feature and schedule it daily with AWS Glue.
Why it's wrong here
DynamoDB Export to S3 exports the full table, not incremental data.
- ✓
Use DynamoDB Streams with AWS Lambda to write changes to S3 in Parquet format.
Why this is correct
Streams capture changes in near-real-time, enabling incremental exports with minimal overhead.
- ✗
Use a script that scans the DynamoDB table and filters by last updated timestamp.
Why it's wrong here
Scanning the full table is inefficient and not truly incremental.
- ✗
Set up an Amazon EMR cluster running Spark jobs to read DynamoDB and write to S3.
Why it's wrong here
EMR adds operational overhead of managing clusters.
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 |
Go deeper
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