MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. 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.
A company wants to deploy a PyTorch model on SageMaker for real-time inference. Which two steps are required? (Select TWO.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Package the model artifacts into a tar.gz file.
Option C is correct because SageMaker requires model artifacts to be packaged as a single tar.gz file (containing the model weights, serialized PyTorch model, and any dependencies) for deployment. This compressed archive is uploaded to S3 and referenced when creating the model object for real-time inference.
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.
✗
Upload the training data to an S3 bucket.
Why it's wrong here
Training data upload is part of training, not deployment.
✗
Register the model in the SageMaker Model Registry.
Why it's wrong here
Model Registry is optional for versioning, not required for deployment.
✓
Package the model artifacts into a tar.gz file.
Why this is correct
SageMaker expects model artifacts in a tar.gz format.
Related concept
Read the scenario before looking for a memorised answer.
✓
Create a SageMaker endpoint configuration with the desired instance type.
Why this is correct
Endpoint configuration specifies instance type and other settings.
Related concept
Read the scenario before looking for a memorised answer.
✗
Set up a SageMaker Notebook instance.
Why it's wrong here
Notebook instances are for development and experimentation.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse the optional Model Registry step (B) as mandatory for deployment, or mistakenly think uploading training data (A) is needed for inference, when in fact only the model artifact packaging (C) and endpoint configuration (D) are the two required steps for real-time inference on SageMaker.
Detailed technical explanation
How to think about this question
When deploying a PyTorch model on SageMaker, the tar.gz archive must include the model's state_dict (or the full model object) and a inference script (e.g., inference.py) that defines the model loading and prediction logic. SageMaker uses the SageMaker PyTorch inference toolkit, which expects the model artifact to be at /opt/ml/model/ inside the container, and the endpoint configuration specifies instance type and initial instance count for scaling. A real-world scenario is deploying a fine-tuned BERT model for sentiment analysis, where the tar.gz includes the tokenizer and model weights, and the endpoint is configured with a GPU instance like ml.p3.2xlarge for low-latency inference.
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.
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Package the model artifacts into a tar.gz file. — Option C is correct because SageMaker requires model artifacts to be packaged as a single tar.gz file (containing the model weights, serialized PyTorch model, and any dependencies) for deployment. This compressed archive is uploaded to S3 and referenced when creating the model object for real-time inference.
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.
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Question Discussion
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