- A
Create a Lambda layer with the model file and use it in the function
A layer allows the model to be included without increasing the function code size.
- B
Use API Gateway to proxy requests to the model stored in S3
Why wrong: API Gateway is not a way to include the model in Lambda.
- C
Store the model in S3 and download it on every invocation
Why wrong: Downloading on each invocation adds latency and cost.
- D
Mount an EFS file system containing the model
Why wrong: EFS is for large models but adds complexity; not simplest for a small model.
Question 451 of 1,000
easyMultiple ChoiceObjective-mapped
Using Lambda Layers for scikit-learn Model in Serverless Inference
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
An ML engineer needs to deploy a model as an AWS Lambda function for serverless inference. The model is a scikit-learn pipeline serialized as a pickle file. What is the best way to include the model in the Lambda deployment?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
✓
Create a Lambda layer with the model file and use it in the function
Option A is correct because Lambda layers allow you to package and include large dependencies, such as a serialized scikit-learn pipeline, separately from your function code. Layers are extracted into the /opt directory and are available across function invocations without cold-start overhead from downloading, making them the most efficient and best-practice approach for bundling static model artifacts in serverless inference.
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.
- ✓
Create a Lambda layer with the model file and use it in the function
Why this is correct
A layer allows the model to be included without increasing the function code size.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use API Gateway to proxy requests to the model stored in S3
Why it's wrong here
API Gateway is not a way to include the model in Lambda.
- ✗
Store the model in S3 and download it on every invocation
Why it's wrong here
Downloading on each invocation adds latency and cost.
- ✗
Mount an EFS file system containing the model
Why it's wrong here
EFS is for large models but adds complexity; not simplest for a small model.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think downloading from S3 on every invocation (Option C) is acceptable for serverless, but they overlook the severe cold-start latency and cost implications, or they confuse API Gateway's role as a proxy (Option B) without realizing it still needs a compute backend.
Detailed technical explanation
How to think about this question
Lambda layers are stored in the /opt directory and are loaded at function initialization, so the model is available in memory for subsequent invocations, avoiding repeated I/O. The maximum unzipped size for a Lambda deployment package (including layers) is 250 MB, so for larger models you may need to use EFS or container images; however, for a typical scikit-learn pipeline pickle, a layer is ideal. Under the hood, Lambda layers are extracted into /opt/python/lib/python3.x/site-packages for Python runtimes, allowing seamless import of the model file as part of the function code.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Create a Lambda layer with the model file and use it in the function — Option A is correct because Lambda layers allow you to package and include large dependencies, such as a serialized scikit-learn pipeline, separately from your function code. Layers are extracted into the /opt directory and are available across function invocations without cold-start overhead from downloading, making them the most efficient and best-practice approach for bundling static model artifacts in serverless inference.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.
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