The answer is to switch to a memory-optimized instance like r5.large. This is correct because the custom container memory optimized instance directly addresses the root cause: the container is consuming more memory than allocated, which throttles inference and causes timeouts. Memory-optimized instances, such as the r5 family, offer a higher memory-to-vCPU ratio, allowing the model to handle its working set without swapping or OOM errors, thus restoring low latency without wasting compute. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your ability to diagnose performance bottlenecks from logs and select the right instance family for the workload—a common trap is to scale out horizontally or increase vCPUs, which does not fix memory pressure. Remember the mnemonic: "Memory Meltdown? Reach for the 'R' series."
MLA-C01 Practice Question: ML Solution Monitoring, Maintenance and Security
This MLA-C01 practice question tests your understanding of ml solution monitoring, maintenance and security. 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 financial services company uses a custom container on Amazon SageMaker to serve a fraud detection model. The model's inference latency has recently increased, causing timeouts for some requests. The team reviews the SageMaker logs and finds that the container is consuming more memory than allocated. What should the team do to maintain service quality while ensuring cost-effectiveness?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Change the instance type to a memory-optimized instance, such as r5.large
The correct answer is D because the root cause is that the container is consuming more memory than allocated, leading to increased latency and timeouts. Switching to a memory-optimized instance like r5.large directly addresses the memory constraint by providing more memory per vCPU, which resolves the performance issue without over-provisioning compute resources. This approach is cost-effective because it targets the specific bottleneck (memory) rather than scaling out or changing unrelated parameters.
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.
✗
Decrease the model's batch size to reduce memory usage
Why it's wrong here
Decreasing batch size may reduce throughput and not solve the memory issue if it's due to model size.
✗
Increase the number of instances in the endpoint to distribute the load
Why it's wrong here
Adding instances spreads load but doesn't fix per-instance memory shortage.
✗
Implement an auto-scaling policy based on memory utilization
Why it's wrong here
Auto-scaling adds instances but each still has insufficient memory.
✓
Change the instance type to a memory-optimized instance, such as r5.large
Why this is correct
Switching to a memory-optimized instance provides more memory per instance, resolving the issue cost-effectively.
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 often confuse scaling out (adding instances) with scaling up (choosing a larger instance type), and they may incorrectly assume that auto-scaling based on memory utilization will prevent timeouts, when in fact it only reacts after the problem occurs.
Detailed technical explanation
How to think about this question
SageMaker real-time endpoints use instance families like r5 (memory-optimized) that offer higher RAM-to-vCPU ratios (e.g., r5.large has 16 GiB memory vs. 8 GiB on m5.large). When a container exceeds its memory limit, the kernel's OOM killer may terminate processes, causing latency spikes or 504 errors. Monitoring CloudWatch metrics like 'MemoryUtilization' helps identify such bottlenecks, and choosing the right instance type is a fundamental capacity planning decision before considering scaling policies.
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 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.
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.
ML Solution Monitoring, Maintenance and Security — This question tests ML Solution Monitoring, Maintenance and Security — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Change the instance type to a memory-optimized instance, such as r5.large — The correct answer is D because the root cause is that the container is consuming more memory than allocated, leading to increased latency and timeouts. Switching to a memory-optimized instance like r5.large directly addresses the memory constraint by providing more memory per vCPU, which resolves the performance issue without over-provisioning compute resources. This approach is cost-effective because it targets the specific bottleneck (memory) rather than scaling out or changing unrelated parameters.
What should I do if I get this MLA-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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