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Data Engineering →hardMultiple ChoiceObjective-mapped

MLS-C01 Data Engineering Practice Question

This MLS-C01 practice question tests your understanding of data engineering. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 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?

Clue words in this question

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

Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

A
Use Amazon Athena to query the DynamoDB table and join with streaming data.
Why wrong: Athena is not designed for real-time streaming joins.
B
Use Amazon Kinesis Data Analytics to process the stream and join with a DynamoDB table.
Kinesis Data Analytics supports real-time joins with DynamoDB using reference data.
C
Use AWS Glue streaming ETL to read from Kinesis and join with DynamoDB.
Why wrong: Glue streaming ETL has higher latency and is not optimized for sub-second joins.
D
Use Amazon SageMaker to host a model that queries DynamoDB for each inference.
Why wrong: SageMaker endpoints can query DynamoDB but latency may be high for large volumes.

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 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.

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 Athena to query the DynamoDB table and join with streaming data.
Why it's wrong here
Athena is not designed for real-time streaming joins.
✓
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.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often 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.

Detailed technical explanation

How to think about this question

Under the hood, Kinesis Data Analytics for Apache Flink maintains a local, in-memory copy of the DynamoDB reference table (via the Flink Async I/O or Table API), which is refreshed periodically based on the DynamoDB Streams or a scheduled scan. This avoids per-record DynamoDB reads, reducing p99 latency to single-digit milliseconds. In a real-world scenario, a financial services firm processing 10,000 transactions per second would see DynamoDB read capacity throttling if querying per event, whereas the Flink-based approach amortizes the read cost across the reference refresh interval.

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

An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.

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 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.

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: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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