The answer is to increase the number of core nodes in the cluster. This resolves the EMR instance store disk space error because core nodes in Amazon EMR handle both HDFS data and local shuffle spills; adding more nodes distributes intermediate shuffle data and temporary files across a larger pool of local instance store volumes, directly reducing per-node disk usage without altering the Spark job logic. On the AWS Certified Data Engineer Associate DEA-01 exam, this scenario tests your understanding of EMR cluster architecture and the distinction between scaling compute capacity versus storage—a common trap is to mistakenly resize instance store volumes, which are ephemeral and cannot be expanded. Remember that when a job fails with “No space left on device” on core nodes, the fix is to scale horizontally, not vertically. Memory tip: “Shuffle spills need more hills”—adding core nodes gives the shuffle data more places to spill.
DEA-C01 Data Operations and Support Practice Question
This DEA-C01 practice question tests your understanding of data operations and support. 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 data engineer is monitoring an Amazon EMR cluster running a Spark job. The job is processing a large dataset and the engineer notices that the cluster is using a high percentage of disk space on the core nodes. The job fails with 'No space left on device' error. What is the most effective way to resolve this issue without modifying the job logic?
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 core nodes in the cluster.
Option D is correct because increasing the number of core nodes distributes the intermediate shuffle data and temporary files across more nodes, reducing the per-node disk usage. This directly addresses the 'No space left on device' error without altering the Spark job logic, as core nodes in EMR store both HDFS data and local shuffle spills.
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.
✗
Attach additional EBS volumes to the core nodes.
Why it's wrong here
EMR does not use EBS for temporary data by default.
✗
Increase the EBS volume size attached to the core nodes.
Why it's wrong here
EMR uses instance store for temporary data, not EBS.
✗
Change the core node instance type to one with more memory.
Why it's wrong here
Memory does not affect disk space.
✓
Increase the number of core nodes in the cluster.
Why this is correct
More nodes distribute the intermediate data, reducing disk usage per node.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse storage issues with memory or compute issues, and incorrectly choose to increase EBS volume size (Option B) instead of scaling horizontally, which is the most effective way to distribute disk load in a distributed system like EMR.
Detailed technical explanation
How to think about this question
In Amazon EMR, core nodes run both the DataNode (for HDFS) and NodeManager (for YARN), and Spark spills intermediate shuffle data to local disk (typically /mnt or /emr/instance-controller). When a cluster hits disk capacity, scaling out core nodes adds more HDFS capacity and more local scratch space, while scaling up (increasing volume size) only helps if the bottleneck is the root volume. A real-world scenario is a Spark job with large shuffles or sort operations that fill the ephemeral storage on a few nodes; adding core nodes spreads the data and reduces the risk of disk exhaustion.
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Data Operations and Support — This question tests Data Operations and Support — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the number of core nodes in the cluster. — Option D is correct because increasing the number of core nodes distributes the intermediate shuffle data and temporary files across more nodes, reducing the per-node disk usage. This directly addresses the 'No space left on device' error without altering the Spark job logic, as core nodes in EMR store both HDFS data and local shuffle spills.
What should I do if I get this DEA-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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Question Discussion
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