The answer is to use Amazon Kinesis Data Analytics to process the stream and join with a DynamoDB table. This architecture is correct because Kinesis Data Analytics offers a reference data feature that allows you to perform real-time SQL joins directly against a DynamoDB table, enabling low-latency enrichment of streaming transaction data with the frequently updated fraud reference dataset. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of real-time data processing versus batch ETL or ML training services—a common trap is choosing AWS Glue for its ETL capabilities, but Glue is designed for batch, not real-time streaming. Remember that when you need to join streaming data with a live DynamoDB table, Kinesis Data Analytics is the only service that natively supports this low-latency, highly available pattern. Memory tip: think "Kinesis joins live" to recall that Kinesis Data Analytics handles real-time joins with DynamoDB reference data.
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.
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.
Option C is correct because Kinesis Data Analytics can perform real-time SQL joins with a DynamoDB table using the reference data feature, providing low latency. Option A is wrong because Glue is for batch ETL, not real-time. Option B is wrong because SageMaker is for ML training, not real-time data processing. Option D is wrong because Athena is for querying S3, not real-time streaming.
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
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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. — Option C is correct because Kinesis Data Analytics can perform real-time SQL joins with a DynamoDB table using the reference data feature, providing low latency. Option A is wrong because Glue is for batch ETL, not real-time. Option B is wrong because SageMaker is for ML training, not real-time data processing. Option D is wrong because Athena is for querying S3, not real-time streaming.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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Question Discussion
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