MLA-C01 · topic practice

Deployment and Orchestration of ML Workflows practice questions

Q: How should I use these Deployment and Orchestration of ML Workflows practice questions?

Read each scenario carefully and choose your answer before revealing the explanation. Then check why your choice was right or wrong. Repeat until the reasoning feels automatic.

Q: Can I practise just Deployment and Orchestration of ML Workflows questions in a focused session?

Yes — use the session launcher on this page to start a 10-, 20-, 30- or 50-question session drawn entirely from the Deployment and Orchestration of ML Workflows domain.

This domain covers building, automating, and operating ML workflows on AWS: SageMaker Pipelines for orchestration, Model Registry for versioning and approval, and deployment strategies like real-time endpoints, batch transform, and multi-variant traffic shifting. Questions present a concrete operational goal and ask which SageMaker feature, condition, or configuration achieves it.

Courseiva uses original exam-style practice questions designed for learning and revision. The goal is to understand the concepts, recognise exam patterns, and improve through explanations — not memorise copied exam dumps.

Editorial oversight:Johnson Ajibi· MSc IT Security, IEEE Senior Member

20 questionsDomain: Deployment and Orchestration of ML Workflows

Practice 10 questions Browse domain →

What the exam tests

What to know about Deployment and Orchestration of ML Workflows

Be able to wire a SageMaker Pipeline that trains, evaluates, and conditionally registers a model, then deploy it via a real-time endpoint using the right traffic-shifting and auto scaling configuration. The critical skill is matching each requirement to the correct SageMaker feature and parameter.

SageMaker Pipelines step conditions and PropertyFile-based conditional model registration in Model Registry

SageMaker endpoint deployment patterns: linear and canary traffic shifting across production variants

SageMaker Pipelines caching configuration to skip unchanged steps and reuse prior outputs

Multi-AZ highly available real-time endpoints with Application Auto Scaling on InvocationsPerInstance

Watch out for

Common Deployment and Orchestration of ML Workflows exam traps

▸Confusing Model Registry approval status with endpoint deployment; registering a model does not automatically serve it
▸Assuming Pipelines always re-execute steps; caching must be explicitly enabled per step to reuse outputs
▸Mixing up canary and linear traffic shifting, or forgetting that variant weights must sum to 100

Practice set

Deployment and Orchestration of ML Workflows questions

20 questions · select your answer, then reveal the explanation

Question 1mediummultiple choice

Read the full Deployment and Orchestration of ML Workflows explanation →

An ML pipeline uses SageMaker Processing to run a feature engineering script. The script takes a long time and the team wants to speed up pipeline execution. What is the MOST effective approach?

Trap 1: Enable pipeline caching for the Processing step

Caching skips the step if inputs unchanged, but does not speed up execution when inputs change.

Trap 2: Use a larger instance type with more vCPUs

While a larger instance helps, parallelization with multiple instances is more effective for scalable speedup.

Trap 3: Use a Tuning step instead

Tuning is for hyperparameter optimization, not feature engineering.

Study all Deployment and Orchestration of ML Workflows common traps →

A
Increase the instance count for the Processing step
Increasing the instance count for the Processing step distributes the workload across multiple instances running in parallel, directly shortening the long-running script's wall-clock duration. This satisfies the stem's goal of speeding up pipeline execution, provided the script can be partitioned across shards.
B
Enable pipeline caching for the Processing step
Why it fails: Caching skips the step if inputs unchanged, but does not speed up execution when inputs change.
C
Use a larger instance type with more vCPUs
Why it fails: While a larger instance helps, parallelization with multiple instances is more effective for scalable speedup.
D
Use a Tuning step instead
Why it fails: Tuning is for hyperparameter optimization, not feature engineering.

Deployment and Orchestration of ML Workflows practice questions

What to know about Deployment and Orchestration of ML Workflows

Common Deployment and Orchestration of ML Workflows exam traps

Deployment and Orchestration of ML Workflows questions

An ML pipeline uses SageMaker Processing to run a feature engineering script. The script takes a long time and the team wants to speed up pipeline execution. What is the MOST effective approach?

A company uses SageMaker Model Registry to manage model versions. They want to automate the approval of models that pass automated evaluation, but require manual approval for others. Which Model Registry feature supports this workflow?

A team uses SageMaker Pipelines to train and evaluate a model. They want to run the training step only if the data quality check passes, otherwise skip. Which TWO pipeline step types are required? (Select TWO.)

A machine learning engineer needs to deploy a model that requires less than 100 ms inference latency for real-time predictions. The model is a small PyTorch model that fits in a single GPU. Which SageMaker inference option is MOST cost-effective for this scenario?

A company has 200 small models (each ~100 MB) that serve different customers. They want to minimize costs while keeping low latency for each customer. Which SageMaker deployment approach is MOST suitable?

A team uses MLflow on SageMaker for experiment tracking. They want to automatically deploy the best-performing model from an MLflow run to a SageMaker endpoint for real-time inference. What is the MOST efficient way to achieve this?

A company uses SageMaker Model Registry to manage model versions. They have a cross-account deployment requirement: models approved in the development account must be deployed to a production account. Which approach is the MOST secure and recommended?

A team wants to use AWS Step Functions to orchestrate a retraining workflow that is triggered when new data arrives in an S3 bucket. They also need to monitor model drift. Which event-driven approach should they use?

A company wants to deploy a PyTorch model that uses dynamic batching and model ensemble. They need to serve multiple models with different frameworks (PyTorch, TensorFlow) within the same endpoint. Which SageMaker feature should they use?

A team is optimizing a deep learning model for deployment on SageMaker using SageMaker Neo. Which THREE of the following are valid optimization techniques that Neo can apply? (Choose THREE.)

A team has a large deep learning model that needs to be deployed for real-time inference with GPU acceleration. They want to use the Triton Inference Server on SageMaker to maximize throughput. Which instance type and configuration should they choose?

A company wants to use SageMaker to deploy a model that requires GPU acceleration for inference but wants to minimize costs by using a smaller attached GPU. Which options can they use? (Select TWO.)

A data science team needs to deploy a PyTorch model that performs real-time inference with sub-100ms latency. The model requires GPU acceleration, but the team wants to minimize cost by sharing GPU instances across multiple models. Which SageMaker hosting option should they choose?

A team wants to use MLflow on SageMaker to track experiments and manage model lifecycle. They need to register models in the SageMaker Model Registry after training. Which approach allows them to use MLflow for experiment tracking and then register the best model to SageMaker Model Registry?

A financial services company needs to enforce that only approved model versions are deployed to production. They use SageMaker Model Registry to track versions, with an approval workflow. Which action must they take in the model registry to ensure only approved models can be deployed?

A data science team uses SageMaker Pipelines to automate their ML workflow. They want to reduce costs by reusing outputs from previous pipeline runs when the input data and code have not changed. Which TWO actions should they take? (Choose two.)

An ML engineer needs to deploy a model that requires GPU acceleration but wants to reduce inference cost by optimizing the model. They are considering SageMaker Neo compilation and Amazon Elastic Inference. Which TWO statements are correct about these services? (Choose two.)

Track your progress over time

Start a Deployment and Orchestration of ML Workflows only practice session

Related MLA-C01 topic practice pages

ML Model Development practice questions

Data Preparation for Machine Learning practice questions

Deployment and Orchestration of ML Workflows practice questions

ML Solution Monitoring, Maintenance, and Security practice questions

MLA-C01 fundamentals practice questions

MLA-C01 scenario practice questions

MLA-C01 troubleshooting practice questions

Frequently asked questions

Track your progress

Study resources

Exam traps to avoid