- A
Increase the DPU count on the Glue streaming ETL job and reduce the checkpoint interval to improve performance.
Why wrong: Scaling Glue streaming jobs may not resolve checkpoint errors and high latency caused by the complexity of sliding window aggregations.
- B
Use Amazon Kinesis Data Analytics for Apache Flink to perform the sliding window aggregations with built-in state management and exactly-once processing, then write the features to S3 and DynamoDB.
Kinesis Data Analytics for Flink provides stateful stream processing optimized for sliding windows, ensuring low latency and fault tolerance.
- C
Use AWS Lambda functions to process records from Kinesis, store intermediate aggregation results in Amazon DynamoDB, and read them back to compute windowed features.
Why wrong: Lambda is stateless and requires external state management, leading to complexity and eventual consistency issues for sliding windows.
- D
Use Amazon SageMaker Processing jobs that run periodically every hour to read data from S3 (landing from Kinesis Firehose) and perform the aggregations batch-wise.
Why wrong: Batch processing every hour introduces latency that is unacceptable for near-real-time recommendation updates.
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 social media company is processing a real-time stream of user activity data from Amazon Kinesis Data Streams to train a machine learning model for content recommendation. The raw data includes user ID, timestamp, content ID, interaction type (like, share, comment), and device type. The data scientists need to aggregate features per user over a sliding window of 7 days, including counts of interaction types, unique content IDs engaged, and a moving average of interaction timestamps. The aggregated data will be used to update a user embedding model. The streaming data volume is approximately 500 records per second, and the company uses an AWS Glue streaming ETL job for transformation. However, the Glue job is failing frequently with high latency and checkpoint errors. The team needs a more robust solution to prepare the streaming data features. Which approach should the team take?
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 for Apache Flink to perform the sliding window aggregations with built-in state management and exactly-once processing, then write the features to S3 and DynamoDB.
Option B is correct because Amazon Kinesis Data Analytics for Apache Flink provides native support for sliding window aggregations with managed state and exactly-once processing semantics, which directly addresses the high latency and checkpoint errors seen in the Glue streaming ETL job. Flink's checkpointing mechanism ensures fault-tolerant state management for the 7-day sliding window, while Glue's Spark Streaming engine struggles with long-running stateful operations at 500 records/sec due to its micro-batch architecture and checkpoint overhead.
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.
- ✗
Increase the DPU count on the Glue streaming ETL job and reduce the checkpoint interval to improve performance.
Why it's wrong here
Scaling Glue streaming jobs may not resolve checkpoint errors and high latency caused by the complexity of sliding window aggregations.
- ✓
Use Amazon Kinesis Data Analytics for Apache Flink to perform the sliding window aggregations with built-in state management and exactly-once processing, then write the features to S3 and DynamoDB.
Why this is correct
Kinesis Data Analytics for Flink provides stateful stream processing optimized for sliding windows, ensuring low latency and fault tolerance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use AWS Lambda functions to process records from Kinesis, store intermediate aggregation results in Amazon DynamoDB, and read them back to compute windowed features.
Why it's wrong here
Lambda is stateless and requires external state management, leading to complexity and eventual consistency issues for sliding windows.
- ✗
Use Amazon SageMaker Processing jobs that run periodically every hour to read data from S3 (landing from Kinesis Firehose) and perform the aggregations batch-wise.
Why it's wrong here
Batch processing every hour introduces latency that is unacceptable for near-real-time recommendation updates.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume increasing resources (DPU) on Glue streaming ETL will fix performance issues, but the root cause is Spark's micro-batch architecture's inability to efficiently manage long-running stateful sliding windows, which Flink's native streaming engine is designed for.
Detailed technical explanation
How to think about this question
Apache Flink's managed keyed state uses RocksDB or heap-based backends with incremental checkpointing to maintain per-user aggregates over a 7-day sliding window without reprocessing the entire stream, unlike Glue's Spark Structured Streaming which relies on write-ahead logs and watermarking that struggle with large state sizes. The exactly-once semantics in Flink are achieved via a two-phase commit protocol with Kinesis as the source, ensuring that each record contributes exactly once to the aggregated features even during failures, which is critical for ML model training consistency.
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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — 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 for Apache Flink to perform the sliding window aggregations with built-in state management and exactly-once processing, then write the features to S3 and DynamoDB. — Option B is correct because Amazon Kinesis Data Analytics for Apache Flink provides native support for sliding window aggregations with managed state and exactly-once processing semantics, which directly addresses the high latency and checkpoint errors seen in the Glue streaming ETL job. Flink's checkpointing mechanism ensures fault-tolerant state management for the 7-day sliding window, while Glue's Spark Streaming engine struggles with long-running stateful operations at 500 records/sec due to its micro-batch architecture and checkpoint overhead.
What should I do if I get this MLA-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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Last reviewed: Jun 24, 2026
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