The answer is to upload the model artifacts to an S3 bucket and create a SageMaker endpoint configuration. These two steps are required because SageMaker’s real-time inference workflow relies on model artifacts stored in S3 as the source of truth; when deploying a custom TensorFlow model, you must first save the trained model in a format like SavedModel, upload it to S3, and then define an endpoint configuration that specifies the instance type and the S3 path to those artifacts. On the AWS Certified AI Practitioner AIF-C01 exam, this question tests your understanding of the deployment pipeline for custom frameworks, often appearing as a trap where candidates mistakenly select “create a training job” or “compile with Neo” instead of focusing on the core deployment steps. A common memory tip is to think of S3 as the “garage” where the model car is parked, and the endpoint configuration as the “keys” that tell SageMaker which car to drive and on what road—without both, the car never leaves the lot.
AIF-C01 Fundamentals of AI and ML Practice Question
This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. 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 data scientist wants to deploy a custom model built with TensorFlow to Amazon SageMaker for real-time inference. Which TWO steps are required? (Choose two.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Upload the model artifacts to an S3 bucket
Option B is correct because SageMaker requires model artifacts (the trained model files) to be stored in an S3 bucket before they can be used for inference. When deploying a custom TensorFlow model, you must upload the saved model (e.g., in SavedModel format) to S3, and then SageMaker will download it to the inference container during endpoint creation.
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 an Amazon ECR repository for the inference container
Why it's wrong here
SageMaker provides a pre-built TensorFlow container; a custom container is not required.
✓
Upload the model artifacts to an S3 bucket
Why this is correct
Model artifacts must be stored in S3 for SageMaker to access.
Related concept
Read the scenario before looking for a memorised answer.
✗
Submit a training job to SageMaker
Why it's wrong here
The model is already trained; no need for a new training job.
✓
Create a SageMaker endpoint configuration
Why this is correct
The endpoint configuration specifies the instance type and model to deploy.
Related concept
Read the scenario before looking for a memorised answer.
✗
Convert the model to ONNX format
Why it's wrong here
TensorFlow models can be deployed directly without conversion.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often think they must build a custom container (Option A) or convert the model (Option E), but SageMaker's pre-built TensorFlow containers eliminate those steps, and the key requirements are simply uploading artifacts to S3 and creating the endpoint configuration.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker uses TensorFlow Serving as the inference engine for TensorFlow models. When you create a SageMaker endpoint, the service launches an EC2 instance running the TensorFlow Serving Docker image, which loads the model from the S3 URI you specify. The endpoint configuration defines the instance type, initial instance count, and the production variant that links the model to the endpoint, making it ready for real-time predictions via the SageMaker Runtime API.
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.
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.
Fundamentals of AI and ML — This question tests Fundamentals of AI and ML — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Upload the model artifacts to an S3 bucket — Option B is correct because SageMaker requires model artifacts (the trained model files) to be stored in an S3 bucket before they can be used for inference. When deploying a custom TensorFlow model, you must upload the saved model (e.g., in SavedModel format) to S3, and then SageMaker will download it to the inference container during endpoint creation.
What should I do if I get this AIF-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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