MLS-C01 Data Engineering Practice Question
A data engineering team is using Apache Spark on Amazon EMR to process streaming data from Amazon Kinesis Data Streams. The Spark application uses structured streaming to read from Kinesis, perform transformations, and write to Amazon S3 in Parquet format. The team notices that the application is falling behind and the processing latency is increasing. The Kinesis stream has 5 shards, and the EMR cluster has 5 core nodes of type r5.xlarge. The Spark application is configured with 5 executors, each with 2 cores and 8 GB memory. The team wants to reduce processing latency. Which change would be most effective?
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
✓
Increase the number of shards in the Kinesis stream to 10 and increase the number of core nodes to 10.
The number of shards (5) matches the number of executors (5), but each shard can be processed by a single executor. To increase parallelism, the team should increase the number of shards in the Kinesis stream and correspondingly increase the number of executors or cores. Alternatively, they can increase the number of cores per executor to allow parallel processing of multiple shards per executor.
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 executor memory to 16 GB.
Why it's wrong here
Memory is likely not the bottleneck; latency is due to insufficient parallelism.
- ✓
Increase the number of shards in the Kinesis stream to 10 and increase the number of core nodes to 10.
Why this is correct
More shards increase parallelism, and more nodes allow more concurrent processing.
- ✗
Use a larger instance type for the core nodes, such as r5.4xlarge.
Why it's wrong here
Larger instances improve per-node throughput but do not increase parallelism if the shard count is the limiting factor.
- ✗
Change the output format from Parquet to CSV to reduce write time.
Why it's wrong here
CSV is larger and slower to write, worsening latency.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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