- A
Install dependencies via SageMaker's lifecycle configuration instead of a custom container
Why wrong: Lifecycle configurations apply to notebooks, not training jobs.
- B
Push the custom container to Amazon ECR and create a training job with the container URI
ECR is the correct registry for Docker images used in SageMaker.
- C
Use SageMaker's built-in framework container and override the entry point
Why wrong: A custom container is specifically requested, so built-in containers are insufficient.
- D
Upload the container to S3 and reference it in the training job
Why wrong: Containers must be stored in Amazon ECR, not S3.
Question 279 of 1,000
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 company wants to use Amazon SageMaker to train a model using a custom Docker container that has specific dependencies. The training code is stored in an S3 bucket. Which steps must be taken to run the training job?
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
✓
Push the custom container to Amazon ECR and create a training job with the container URI
Amazon SageMaker requires custom Docker containers to be stored in Amazon Elastic Container Registry (ECR) to run training jobs. The container URI from ECR is specified in the `AlgorithmSpecification` parameter of the `CreateTrainingJob` API call, allowing SageMaker to pull and execute the container with the training code from S3. Option B correctly describes this mandatory workflow.
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.
- ✗
Install dependencies via SageMaker's lifecycle configuration instead of a custom container
Why it's wrong here
Lifecycle configurations apply to notebooks, not training jobs.
- ✓
Push the custom container to Amazon ECR and create a training job with the container URI
Why this is correct
ECR is the correct registry for Docker images used in SageMaker.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use SageMaker's built-in framework container and override the entry point
Why it's wrong here
A custom container is specifically requested, so built-in containers are insufficient.
- ✗
Upload the container to S3 and reference it in the training job
Why it's wrong here
Containers must be stored in Amazon ECR, not S3.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that any S3-uploaded artifact (including Docker images) can be directly referenced in a training job, but SageMaker strictly requires container images to be stored in ECR, not S3.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker uses the `RegistryPath` field in the `AlgorithmSpecification` to locate the container image in ECR, which must be in the same AWS region as the training job. The container must include an entry point that handles the `SM_CHANNELS`, `SM_MODEL_DIR`, and `SM_OUTPUT_DATA_DIR` environment variables to read training data from S3 and write model artifacts back. A real-world scenario is when using GPU-optimized libraries like CUDA or custom C++ extensions—these must be baked into the container image because SageMaker’s built-in containers cannot be modified at runtime.
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
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 |
What to study next
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
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: Push the custom container to Amazon ECR and create a training job with the container URI — Amazon SageMaker requires custom Docker containers to be stored in Amazon Elastic Container Registry (ECR) to run training jobs. The container URI from ECR is specified in the `AlgorithmSpecification` parameter of the `CreateTrainingJob` API call, allowing SageMaker to pull and execute the container with the training code from S3. Option B correctly describes this mandatory workflow.
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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Last reviewed: Jun 30, 2026
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