- A
Set up a real-time inference endpoint
Why wrong: Inference endpoints are separate from training workflow.
- B
Create a training job
A training job is required to start model training.
- C
Create a custom training image
Why wrong: Built-in algorithms come with pre-built Docker images; no custom image needed.
- D
Set hyperparameters
Hyperparameters must be specified to configure the algorithm.
- E
Monitor training with CloudWatch
Monitoring metrics via CloudWatch is a standard practice during training.
Question 238 of 1,000
mediumMultiple SelectObjective-mapped
MLA-C01 Practice Question: Which THREE steps are part of the typical…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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.
Which THREE steps are part of the typical workflow when using SageMaker built-in algorithms?
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 training job
Option B is correct because creating a training job is a fundamental step in the SageMaker workflow for built-in algorithms. You must specify the algorithm, input data location in S3, output path, and compute resources to start model training. Without a training job, no model artifact is generated for deployment.
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.
- ✗
Set up a real-time inference endpoint
Why it's wrong here
Inference endpoints are separate from training workflow.
- ✓
Create a training job
Why this is correct
A training job is required to start model training.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Create a custom training image
Why it's wrong here
Built-in algorithms come with pre-built Docker images; no custom image needed.
- ✓
Set hyperparameters
Why this is correct
Hyperparameters must be specified to configure the algorithm.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Monitor training with CloudWatch
Why this is correct
Monitoring metrics via CloudWatch is a standard practice during training.
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 the deployment step (setting up an endpoint) with the core training workflow, or think custom images are required for built-in algorithms, when in fact SageMaker handles the container automatically.
Detailed technical explanation
How to think about this question
SageMaker built-in algorithms, such as XGBoost or Linear Learner, are provided as pre-built Docker images in Amazon ECR. When you create a training job, SageMaker automatically pulls the appropriate image, mounts your S3 input data, and runs the training script with your specified hyperparameters. The training logs and metrics are streamed to CloudWatch Logs, enabling real-time monitoring without manual setup.
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
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?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Create a training job — Option B is correct because creating a training job is a fundamental step in the SageMaker workflow for built-in algorithms. You must specify the algorithm, input data location in S3, output path, and compute resources to start model training. Without a training job, no model artifact is generated for deployment.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
This MLA-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 MLA-C01 exam.
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