- A
Use SageMaker Ground Truth to transform inference requests
Why wrong: Ground Truth is for data labeling, not inference.
- B
Use SageMaker Processing jobs to preprocess data before inference
Why wrong: Processing jobs are for batch preprocessing, not real-time.
- C
Use a built-in SageMaker algorithm with the default inference code
Why wrong: Built-in algorithms do not support custom preprocessing/postprocessing.
- D
Create a SageMaker model with a custom inference script that includes pre- and post-processing functions
Custom inference scripts allow full control over request handling.
Question 1,408 of 1,755
Implement Custom Preprocessing in SageMaker Inference Script
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 is deploying a model to a SageMaker endpoint for real-time inference. The model requires a custom inference script that preprocesses input data and postprocesses predictions. Which SageMaker feature should be used to implement this custom logic?
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 SageMaker model with a custom inference script that includes pre- and post-processing functions
Option D is correct because SageMaker allows you to bring your own container or use a pre-built container with a custom inference script that defines `input_fn`, `predict_fn`, `output_fn`, and `model_fn` functions. These functions handle preprocessing of input data, model prediction, and postprocessing of predictions, enabling custom logic for real-time inference endpoints without requiring separate infrastructure.
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.
- ✗
Use SageMaker Ground Truth to transform inference requests
Why it's wrong here
Ground Truth is for data labeling, not inference.
- ✗
Use SageMaker Processing jobs to preprocess data before inference
Why it's wrong here
Processing jobs are for batch preprocessing, not real-time.
- ✗
Use a built-in SageMaker algorithm with the default inference code
Why it's wrong here
Built-in algorithms do not support custom preprocessing/postprocessing.
- ✓
Create a SageMaker model with a custom inference script that includes pre- and post-processing functions
Why this is correct
Custom inference scripts allow full control over request handling.
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 confuse SageMaker Processing jobs (batch) with real-time inference preprocessing, or assume built-in algorithms can be customized via inference scripts, when in fact only custom containers or scripts provide that flexibility.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker inference endpoints use the SageMaker Inference Toolkit, which invokes the custom script's functions in a specific order: `model_fn` loads the model, `input_fn` deserializes and preprocesses the request, `predict_fn` runs inference, and `output_fn` serializes the response. This design decouples the inference pipeline, allowing engineers to handle diverse data formats (e.g., JSON, CSV, images) and apply business logic without modifying the model artifact.
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.
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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 — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Create a SageMaker model with a custom inference script that includes pre- and post-processing functions — Option D is correct because SageMaker allows you to bring your own container or use a pre-built container with a custom inference script that defines `input_fn`, `predict_fn`, `output_fn`, and `model_fn` functions. These functions handle preprocessing of input data, model prediction, and postprocessing of predictions, enabling custom logic for real-time inference endpoints without requiring separate infrastructure.
What should I do if I get this MLS-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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Last reviewed: Jul 4, 2026
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