AIF-C01 Fundamentals of AI and ML Practice Question
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.)
⚠ Common exam trap
Many candidates 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.
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
✓
Upload the model artifacts to an S3 bucket
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.
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.
- ✗
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.
- ✗
Convert the model to ONNX format
Why it's wrong here
TensorFlow models can be deployed directly without conversion.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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This AIF-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 AIF-C01 exam.