The answer is to use DynamoDB Streams with AWS Lambda to write changes to S3 in Parquet format. This is correct because DynamoDB Streams capture every insert, update, and delete in near real-time, and a Lambda function can process these events to perform an incremental export of only the changed records, converting them to Parquet on the fly before writing to S3. This fully serverless approach requires no infrastructure management, making it the lowest-overhead solution for a daily incremental DynamoDB export to S3 in Parquet format. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of event-driven architectures for data pipelines; a common trap is choosing a full export with AWS Glue or Data Pipeline, which adds operational overhead and fails the incremental requirement. Remember the key pairing: Streams for change capture, Lambda for lightweight transformation, and Parquet for SageMaker efficiency. A useful memory tip is “Streams + Lambda = incremental Parquet, no servers to park it.”
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.
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?
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.
Option B is correct because 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.
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 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.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
DynamoDB Streams records are ordered by the time of modification and can be configured to capture only the 'new image' (the item after the change) to minimize data volume. The Lambda function can use the `boto3` library to write data in Parquet format via PyArrow or AWS Glue DataBrew, and can batch multiple stream records into a single S3 object to reduce costs. A real-world scenario is a music streaming service that needs to update a daily recommendation training dataset with only the past 24 hours of user interactions, avoiding reprocessing all historical data.
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.
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 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 DynamoDB Streams with AWS Lambda to write changes to S3 in Parquet format. — Option B is correct because 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.
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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Question Discussion
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