MLA-C01 Data Preparation for Machine Learning Practice Question
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?
⚠ Common exam trap
Test-takers frequently 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.
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.
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.
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.
- ✗
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.
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