- A
Enable auto-scaling with Spot instances.
Why wrong: Auto-scaling with Spot instances does not increase the memory per instance and may introduce interruptions, so it does not resolve the memory bottleneck.
- B
Switch to a compute-optimized instance type like c5.2xlarge.
Switching to c5.2xlarge doubles memory (16 GB) and increases CPU cores, directly addressing the memory exhaustion and providing headroom for concurrent requests.
- C
Increase the number of instances further to 10.
Why wrong: Increasing the instance count to 10 still uses ml.c5.xlarge instances with 8 GB each, so each instance remains memory-constrained, and errors will persist.
- D
Use a memory-optimized instance type like r5.large.
Why wrong: While r5.large has 16 GB memory, it has only 2 vCPUs, which may bottleneck inference throughput; a compute-optimized instance with proportionate memory (c5.2xlarge) is more balanced for this workload.
MLS-C01 Memory-constrained workload Practice Question
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. A key principle to apply: memory-constrained workload. 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 operates a real-time fraud detection system using SageMaker. The model is deployed on an ml.c5.xlarge instance behind an Application Load Balancer (ALB). Recently, during a sales event, traffic spiked and the endpoint returned HTTP 503 errors. The team scaled the instance count from 2 to 5, but errors persisted. CloudWatch metrics show low CPU utilization (~30%) and high memory usage (~90%). The model loads a large dictionary file (2GB) into memory at startup. Which action should 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
Switch to a compute-optimized instance type like c5.2xlarge.
The high memory usage (~90%) with low CPU (~30%) indicates the instance is memory-constrained under load. The ml.c5.xlarge has 8 GB memory, and the model's 2 GB dictionary plus overhead exhausts memory, causing 503 errors. Scaling out doesn't help because each instance is individually memory-bound. Option B switches to c5.2xlarge, which provides 16 GB memory (doubling capacity) and more CPU cores, addressing both memory exhaustion and ensuring sufficient compute for concurrent requests. Option A (Spot instances) does not increase per-instance memory. Option C (scaling to 10 instances) still uses c5.xlarge instances with 8 GB each, so each remains memory-limited. Option D (r5.large) offers 16 GB memory but reduces vCPUs to 2, which could create a CPU bottleneck for the inference workload, making B the better choice.
Key principle: Memory-constrained workload
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable auto-scaling with Spot instances.
Why it's wrong here
Auto-scaling with Spot instances does not increase the memory per instance and may introduce interruptions, so it does not resolve the memory bottleneck.
- ✓
Switch to a compute-optimized instance type like c5.2xlarge.
Why this is correct
Switching to c5.2xlarge doubles memory (16 GB) and increases CPU cores, directly addressing the memory exhaustion and providing headroom for concurrent requests.
Related concept
Memory-constrained workload
- ✗
Increase the number of instances further to 10.
Why it's wrong here
Increasing the instance count to 10 still uses ml.c5.xlarge instances with 8 GB each, so each instance remains memory-constrained, and errors will persist.
- ✗
Use a memory-optimized instance type like r5.large.
Why it's wrong here
While r5.large has 16 GB memory, it has only 2 vCPUs, which may bottleneck inference throughput; a compute-optimized instance with proportionate memory (c5.2xlarge) is more balanced for this workload.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Memory-constrained workload
- Instance type selection
- Scaling out vs scaling up
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
Memory-constrained workload
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
Visual reference
What to study next
Got this wrong? Here's your next step.
Review memory-constrained workload, then practise related MLS-C01 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Memory-constrained workload.
What is the correct answer to this question?
The correct answer is: Switch to a compute-optimized instance type like c5.2xlarge. — The high memory usage (~90%) with low CPU (~30%) indicates the instance is memory-constrained under load. The ml.c5.xlarge has 8 GB memory, and the model's 2 GB dictionary plus overhead exhausts memory, causing 503 errors. Scaling out doesn't help because each instance is individually memory-bound. Option B switches to c5.2xlarge, which provides 16 GB memory (doubling capacity) and more CPU cores, addressing both memory exhaustion and ensuring sufficient compute for concurrent requests. Option A (Spot instances) does not increase per-instance memory. Option C (scaling to 10 instances) still uses c5.xlarge instances with 8 GB each, so each remains memory-limited. Option D (r5.large) offers 16 GB memory but reduces vCPUs to 2, which could create a CPU bottleneck for the inference workload, making B the better choice.
What should I do if I get this MLS-C01 question wrong?
Review memory-constrained workload, then practise related MLS-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Memory-constrained workload
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Last reviewed: Jun 20, 2026
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