The correct answer is a tar.gz file containing the TensorFlow SavedModel. This is because the SageMaker built-in TensorFlow Serving container is pre-configured to load models exclusively from the SavedModel format, which bundles the computational graph, trained weights, and assets into a standardized directory structure that TensorFlow Serving natively expects. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of how SageMaker’s pre-built containers eliminate the need for custom inference code—a common trap is assuming you need to include a Dockerfile or inference script, when in fact the container handles everything as long as the model is properly archived. The key memory tip: think “tar.gz of SavedModel” as the only ingredient for a no-code deployment with the built-in container.
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 machine learning engineer needs to deploy a TensorFlow model to Amazon SageMaker and wants to use the built-in TensorFlow Serving container. What should the engineer provide in the model archive?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
A tar.gz file containing the TensorFlow SavedModel.
The built-in TensorFlow Serving container in Amazon SageMaker expects a TensorFlow SavedModel packaged in a tar.gz archive. This is because TensorFlow Serving natively loads models from the SavedModel format, which includes the model's computational graph, weights, and assets in a standardized directory structure. Providing a tar.gz of the SavedModel ensures compatibility with the container's default serving stack without requiring custom inference code.
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.
✗
A frozen graph of the TensorFlow model.
Why it's wrong here
SageMaker expects SavedModel format (including variables).
✓
A tar.gz file containing the TensorFlow SavedModel.
Why this is correct
SageMaker's TensorFlow serving container expects a SavedModel packaged as tar.gz.
Related concept
Read the scenario before looking for a memorised answer.
✗
Model artifacts and a Python inference script.
Why it's wrong here
Built-in container already has inference code.
✗
A Dockerfile and model artifacts.
Why it's wrong here
Dockerfile is needed only when using custom containers, not built-in TensorFlow.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that a frozen graph (Option A) is sufficient for TensorFlow Serving, but the exam expects candidates to know that TensorFlow Serving specifically requires the SavedModel format with its directory structure, not just a single protobuf file.
Detailed technical explanation
How to think about this question
Under the hood, TensorFlow Serving uses the SavedModel's `saved_model.pb` (or `saved_model.pbtxt`) file to define the model's signature and serving endpoints, while the `variables/` directory stores the trained weights. SageMaker's TensorFlow Serving container automatically starts a gRPC and HTTP server on port 8500 and 8501 respectively, and it expects the model archive to be extracted to `/opt/ml/model` with a numeric version subdirectory (e.g., `/opt/ml/model/1/`). A real-world scenario where this matters is when deploying a model with multiple signatures (e.g., classification and regression), as the SavedModel format cleanly supports multiple `SignatureDefs` that the container can route requests to.
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: A tar.gz file containing the TensorFlow SavedModel. — The built-in TensorFlow Serving container in Amazon SageMaker expects a TensorFlow SavedModel packaged in a tar.gz archive. This is because TensorFlow Serving natively loads models from the SavedModel format, which includes the model's computational graph, weights, and assets in a standardized directory structure. Providing a tar.gz of the SavedModel ensures compatibility with the container's default serving stack without requiring custom inference code.
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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