MLS-C01 Data Engineering Practice Question
A financial services company is building a fraud detection model that requires joining real-time transaction data with a reference dataset of known fraudulent accounts stored in Amazon DynamoDB. The solution must minimize latency and be highly available. The reference dataset is updated frequently (every few minutes). Which architecture should the team use?
⚠ Common exam trap
Many candidates choose AWS Glue streaming ETL (Option C) because they associate Glue with ETL and DynamoDB, but Glue streaming ETL lacks native DynamoDB reference join support, making Kinesis Data Analytics the correct low-latency streaming join service.
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 Kinesis Data Analytics to process the stream and join with a DynamoDB table.
Amazon Kinesis Data Analytics (now managed Apache Flink) can directly reference a DynamoDB table as a reference source via the Flink Table API or SQL JOINs, enabling low-latency, stateful stream enrichment without external query overhead. This architecture minimizes latency by performing the join in-memory within the streaming application, and it supports high availability through Kinesis Data Analytics' automatic checkpointing and failover.
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 Athena to query the DynamoDB table and join with streaming data.
Why it's wrong here
Amazon Athena is unsuitable for this real-time fraud detection scenario because its query execution model introduces significant latency, making it unable to meet the requirement for minimal latency lookups. While Athena can query DynamoDB through a federated connector, this is designed for analytical workloads and ad-hoc reporting over large datasets, not for the low-latency, sub-second responses needed for real-time transaction processing. It would be a good choice for batch analytics or data exploration.
- ✓
Use Amazon Kinesis Data Analytics to process the stream and join with a DynamoDB table.
Why this is correct
Kinesis Data Analytics supports real-time joins with DynamoDB using reference data.
- ✗
Use AWS Glue streaming ETL to read from Kinesis and join with DynamoDB.
Why it's wrong here
Glue streaming ETL has higher latency and is not optimized for sub-second joins.
- ✗
Use Amazon SageMaker to host a model that queries DynamoDB for each inference.
Why it's wrong here
SageMaker endpoints can query DynamoDB but latency may be high for large volumes.
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