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Question 1,128 of 1,755

Data Engineering →mediumMultiple ChoiceObjective-mapped

Export DynamoDB Data to S3 for Athena with DynamoDB Streams and Lambda

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 uses Amazon DynamoDB as the primary data store for a real-time application. The data science team wants to analyze the data using Amazon Athena. What is the most efficient way to make the DynamoDB data available for Athena queries?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "primary"
Why it matters: Asks for the main purpose or function, not a secondary benefit. Eliminate answers that describe side-effects or partial functions.

A
Use AWS Glue to extract data from DynamoDB and load into S3 on a schedule.
Why wrong: Glue batch jobs add latency and are not real-time.
B
Use Amazon Redshift Spectrum to query DynamoDB directly.
Why wrong: Redshift Spectrum queries data in S3, not DynamoDB.
C
Use DynamoDB Streams to invoke an AWS Lambda function that writes data to Amazon S3 in Parquet format. Then query the data in S3 using Athena.
This provides a decoupled, cost-effective solution for analytics.
D
Use Amazon EMR to read directly from DynamoDB and run Hive queries.
Why wrong: EMR is more complex and not serverless.

Full breakdown with real-world context →

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 to invoke an AWS Lambda function that writes data to Amazon S3 in Parquet format. Then query the data in S3 using Athena.

Option C is correct because DynamoDB Streams captures real-time changes, and an AWS Lambda function can efficiently write these changes to Amazon S3 in Parquet format, which is optimized for columnar storage and Athena queries. This approach minimizes the overhead of scheduled batch jobs and provides near-real-time data availability for analytics.

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 AWS Glue to extract data from DynamoDB and load into S3 on a schedule.
Why it's wrong here
Glue batch jobs add latency and are not real-time.
✗
Use Amazon Redshift Spectrum to query DynamoDB directly.
Why it's wrong here
Redshift Spectrum queries data in S3, not DynamoDB.
✓
Use DynamoDB Streams to invoke an AWS Lambda function that writes data to Amazon S3 in Parquet format. Then query the data in S3 using Athena.
Why this is correct
This provides a decoupled, cost-effective solution for analytics.
Clue confirmation
The clue word "primary" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Amazon EMR to read directly from DynamoDB and run Hive queries.
Why it's wrong here
EMR is more complex and not serverless.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates may assume scheduled batch extraction (Option A) is sufficient for real-time analysis, overlooking the efficiency of streaming-based incremental updates that avoid full table scans and reduce costs.

Detailed technical explanation

How to think about this question

DynamoDB Streams captures item-level changes in near real-time with a 24-hour retention window, and Lambda can batch these changes to S3 using optimized formats like Parquet, which reduces storage costs and improves query performance in Athena due to predicate pushdown and column pruning. In a real-world scenario, this pattern is critical for applications like IoT sensor data or user activity logs where low-latency analytics are required without impacting the primary DynamoDB table's read capacity.

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

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.

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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 DynamoDB Streams to invoke an AWS Lambda function that writes data to Amazon S3 in Parquet format. Then query the data in S3 using Athena. — Option C is correct because DynamoDB Streams captures real-time changes, and an AWS Lambda function can efficiently write these changes to Amazon S3 in Parquet format, which is optimized for columnar storage and Athena queries. This approach minimizes the overhead of scheduled batch jobs and provides near-real-time data availability for analytics.

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.

Are there clue words in this question I should notice?

Yes — watch for: "primary". Asks for the main purpose or function, not a secondary benefit. Eliminate answers that describe side-effects or partial functions.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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