- A
Increase the number of shards to 200 to provide more parallelism.
Why wrong: Increasing shards does not fix the skew; if data is concentrated on a few shards, the new shards may remain underutilized.
- B
Modify the producer to add a random prefix to the partition key, ensuring even distribution across all shards, and monitor the stream using CloudWatch.
Adding a random prefix to partition keys uniformizes distribution, eliminating hot shards; CloudWatch helps confirm the fix.
- C
Check Amazon CloudWatch metrics for Kinesis to identify hot shards, then manually redistribute the data by repartitioning in Spark.
Why wrong: CloudWatch can identify hot shards, but manual repartitioning in Spark after ingestion does not resolve the root cause (skew in partition keys).
- D
Use the Kinesis Client Library (KCL) with a custom worker to rebalance the load across shards.
Why wrong: KCL is not typically used with Spark Streaming; Spark has its own Kinesis connector.
Quick Answer
The answer is to modify the producer to add a random prefix to the partition key. This resolves the Kinesis data distribution skew fix by ensuring sensor readings are hashed evenly across all 100 shards, eliminating the hot shard that was causing the Spark Streaming job to fall behind. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of stream partitioning mechanics versus scaling infrastructure—a common trap is to immediately increase shard count or reprovision the EMR cluster, but the root cause is a skewed partition key, not insufficient throughput. Remember that a 1 KB record size is tiny, so the bottleneck is almost certainly distribution, not capacity. Use CloudWatch metrics like IncomingBytes and ReadProvisionedThroughputExceeded to confirm the fix. Memory tip: "Random prefix, random shard—no more hot shards."
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. 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 company processes large streams of IoT sensor data using Amazon Kinesis Data Streams with 100 shards. Each sensor reading is about 1 KB. The data is consumed by an Amazon EMR cluster running Spark Streaming jobs. The team notices that the Spark Streaming job's processing time is gradually increasing, and the stream is falling behind. They suspect the issue is due to skewed data distribution across shards. Which approach should the team take to diagnose and resolve the issue?
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
Modify the producer to add a random prefix to the partition key, ensuring even distribution across all shards, and monitor the stream using CloudWatch.
Option B is correct because adding a random prefix to the partition key ensures that sensor data is evenly distributed across all 100 shards, eliminating hot shards that cause processing delays. This directly addresses the skewed data distribution issue without requiring infrastructure changes, and the team can monitor the improvement using CloudWatch metrics like IncomingBytes and ReadProvisionedThroughputExceeded.
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 number of shards to 200 to provide more parallelism.
Why it's wrong here
Increasing shards does not fix the skew; if data is concentrated on a few shards, the new shards may remain underutilized.
- ✓
Modify the producer to add a random prefix to the partition key, ensuring even distribution across all shards, and monitor the stream using CloudWatch.
Why this is correct
Adding a random prefix to partition keys uniformizes distribution, eliminating hot shards; CloudWatch helps confirm the fix.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Check Amazon CloudWatch metrics for Kinesis to identify hot shards, then manually redistribute the data by repartitioning in Spark.
Why it's wrong here
CloudWatch can identify hot shards, but manual repartitioning in Spark after ingestion does not resolve the root cause (skew in partition keys).
- ✗
Use the Kinesis Client Library (KCL) with a custom worker to rebalance the load across shards.
Why it's wrong here
KCL is not typically used with Spark Streaming; Spark has its own Kinesis connector.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse consumer-side rebalancing (KCL or Spark repartitioning) with producer-side data distribution, and incorrectly assume that increasing shards or using Spark repartitioning can fix a hot shard caused by a poor partition key.
Detailed technical explanation
How to think about this question
Kinesis Data Streams uses the partition key to determine which shard a record is written to, via a hash of the key modulo the number of shards. If many sensors share the same partition key (e.g., device ID), they all map to the same shard, creating a hot shard that throttles writes and increases processing latency. Adding a random prefix (e.g., a UUID or timestamp segment) to the partition key effectively randomizes the hash, spreading records uniformly across all shards—a common pattern called 'salting' in distributed systems.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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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: Modify the producer to add a random prefix to the partition key, ensuring even distribution across all shards, and monitor the stream using CloudWatch. — Option B is correct because adding a random prefix to the partition key ensures that sensor data is evenly distributed across all 100 shards, eliminating hot shards that cause processing delays. This directly addresses the skewed data distribution issue without requiring infrastructure changes, and the team can monitor the improvement using CloudWatch metrics like IncomingBytes and ReadProvisionedThroughputExceeded.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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