The answer is to configure the endpoint with a larger 'ModelCacheSize' parameter. This is correct because the ModelCacheSize determines how many model artifacts can remain loaded in the endpoint’s memory; by increasing it, you reduce cold-start latency in SageMaker multi-model endpoints by keeping frequently accessed models cached instead of forcing them to be downloaded from S3 and reloaded on every invocation. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this concept tests your understanding of how MMEs manage memory versus single-model endpoints, and a common trap is to suggest scaling the instance count or enabling auto-scaling, which addresses throughput but not the specific loading overhead. Remember the memory tip: “Cache size cures cold starts”—if latency spikes on first requests, think of increasing the cache, not just the compute.
MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. 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 deploys multiple models on a single Amazon SageMaker endpoint using a multi-model endpoint (MME). The models are stored in Amazon S3. Each model is approximately 500 MB and is loaded on demand. Users report high latency for cold-start scenarios. What should the company do to reduce cold-start latency?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Configure the endpoint to use a larger 'ModelCacheSize' parameter.
Option D is correct because increasing the 'ModelCacheSize' parameter allows the SageMaker multi-model endpoint to keep more models loaded in memory, reducing the frequency of cold starts where a model must be downloaded from S3 and loaded into memory. This directly addresses the latency issue by caching models that are frequently accessed, avoiding repeated loading overhead.
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.
✗
Reduce the instance size to increase the number of instances per unit cost.
Why it's wrong here
Smaller instances may have less memory, increasing disk swapping and latency.
✗
Increase the number of instances in the endpoint's auto-scaling group.
Why it's wrong here
More instances spread the load but each still may have cold starts.
✗
Deploy each model on a separate endpoint to avoid concurrent loading.
Why it's wrong here
This increases management overhead and cost, and doesn't directly address cold start.
✓
Configure the endpoint to use a larger 'ModelCacheSize' parameter.
Why this is correct
Increasing the model cache size allows more models to be cached in memory, reducing load time.
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 the number of instances (Option B) with improving per-request latency, but horizontal scaling does not reduce the time to load a model from S3 into memory on a given instance.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker multi-model endpoints use a model cache on the instance's local storage (SSD or memory) to store recently loaded models; the 'ModelCacheSize' parameter controls the maximum size of this cache in bytes. When a model is requested, if it is not in cache, it is downloaded from S3 and loaded into memory, which can take several seconds for a 500 MB model. In real-world scenarios, a financial services company might have models with different usage patterns, and tuning the cache size to accommodate the working set of models can dramatically reduce p99 latency for frequently accessed models.
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.
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Configure the endpoint to use a larger 'ModelCacheSize' parameter. — Option D is correct because increasing the 'ModelCacheSize' parameter allows the SageMaker multi-model endpoint to keep more models loaded in memory, reducing the frequency of cold starts where a model must be downloaded from S3 and loaded into memory. This directly addresses the latency issue by caching models that are frequently accessed, avoiding repeated loading overhead.
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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