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Deployment and Orchestration of ML Workflows →easyMultiple ChoiceObjective-mapped

Quick Answer

The answer is to increase the Lambda function memory to 1024 MB or higher because the current 512 MB allocation is insufficient to load the 400 MB TensorFlow model, causing the runtime to rely on excessive disk swapping or trigger out-of-memory errors that stall inference. In AWS Lambda, memory directly correlates with CPU allocation, so a memory-starved function cannot load large ML models efficiently, leading to a Lambda inference timeout even when the configured timeout is 30 seconds. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding that Lambda memory must accommodate both the model size and runtime overhead, with a common trap being to increase the timeout instead of the memory. Remember the 80% rule: keep your model size under 80% of the allocated memory to avoid swapping-induced timeouts.

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. 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.

A startup is building a serverless inference API using AWS Lambda. They have a TensorFlow model that is 400 MB in size. They packaged the model and inference code into a Lambda function using a container image. When they test the function with a small input, it consistently times out after 3 seconds. The Lambda function has 512 MB of memory and a timeout of 30 seconds. The business requirement is that inference must complete in less than 5 seconds under normal conditions. What is the most likely cause of the slow performance, and which change should they make?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.

Question 1easymultiple choice

Full question →

A
The function timeout is too low; increase the timeout to 60 seconds.
Why wrong: The task requires under 5 seconds; 3 seconds is already below 5, so the timeout is not the issue; the function is slow due to memory.
B
The function is experiencing a cold start; use provisioned concurrency to keep the container warm.
Why wrong: Cold starts cause initial latency, but the 3-second timeout occurs on invocation, not on startup, and provisioned concurrency would not fix memory constraint.
C
The Lambda function memory is insufficient for the model size; increase memory to 1024 MB or higher.
Lambda allocates CPU proportionally to memory. More memory speeds up computation and reduces swapping.
D
Use a Lambda function with a GPU container to accelerate inference.
Why wrong: Lambda does not support GPU; it runs on CPU only.

Full breakdown with real-world context →

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

✓

The Lambda function memory is insufficient for the model size; increase memory to 1024 MB or higher.

The most likely cause is that the Lambda function's memory (512 MB) is insufficient to load the 400 MB TensorFlow model into memory, causing excessive swapping or out-of-memory errors that drastically slow inference. Increasing memory to 1024 MB or higher provides more CPU and memory resources, allowing the model to fit and inference to complete within the required 5 seconds.

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.

✗
The function timeout is too low; increase the timeout to 60 seconds.
Why it's wrong here
The task requires under 5 seconds; 3 seconds is already below 5, so the timeout is not the issue; the function is slow due to memory.
✗
The function is experiencing a cold start; use provisioned concurrency to keep the container warm.
Why it's wrong here
Cold starts cause initial latency, but the 3-second timeout occurs on invocation, not on startup, and provisioned concurrency would not fix memory constraint.
✓
The Lambda function memory is insufficient for the model size; increase memory to 1024 MB or higher.
Why this is correct
Lambda allocates CPU proportionally to memory. More memory speeds up computation and reduces swapping.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a Lambda function with a GPU container to accelerate inference.
Why it's wrong here
Lambda does not support GPU; it runs on CPU only.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates confuse cold start latency with runtime performance issues, assuming provisioned concurrency (Option B) fixes all slow Lambda functions, when in fact memory/CPU insufficiency is the root cause for large model inference.

Detailed technical explanation

How to think about this question

Lambda allocates CPU proportionally to memory, so increasing memory from 512 MB to 1024 MB roughly doubles the available CPU, which is critical for loading and running a 400 MB TensorFlow model. The model must be loaded into memory for each invocation, and if the function's memory is too small, the Lambda runtime may swap to disk or fail to allocate enough heap space, causing severe latency. In practice, for models over 200 MB, Lambda memory should be at least 2–3 times the model size to accommodate the model, inference code, and runtime overhead.

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.

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FAQ

Questions learners often ask

What does this MLA-C01 question test?

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: The Lambda function memory is insufficient for the model size; increase memory to 1024 MB or higher. — The most likely cause is that the Lambda function's memory (512 MB) is insufficient to load the 400 MB TensorFlow model into memory, causing excessive swapping or out-of-memory errors that drastically slow inference. Increasing memory to 1024 MB or higher provides more CPU and memory resources, allowing the model to fit and inference to complete within the required 5 seconds.

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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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