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.

Practise AWS Certified Machine Learning Engineer Associate MLA-C01 Deployment and Orchestration of ML Workflows practice questions — original exam-style scenarios with answer choices, explanations, and analysis of common mistakes.

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.

Reviewed byJohnson Ajibi· MSc IT Security

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

Deployment and Orchestration of ML Workflows questions test whether you can apply the concept in context, not just recognise a definition.

How the topic appears in realistic exam-style scenarios.

Which detail in the question changes the correct answer.

How to eliminate plausible but wrong options.

How to connect the question back to the wider exam objective.

Watch out for

Common Deployment and Orchestration of ML Workflows exam traps

▸Answering from memory before reading the full scenario.
▸Missing a constraint such as cost, availability, security, scope or command context.
▸Choosing a broad answer when the question asks for the most specific fix.
▸Ignoring why the wrong options are tempting.

Practice set

Deployment and Orchestration of ML Workflows questions

20 questions · select your answer, then reveal the explanation

Question 1easymultiple choice

Read the full Deployment and Orchestration of ML Workflows explanation →

A data scientist needs to deploy a single ML model that will serve real-time predictions with low latency (under 10 ms) for a high-traffic web application. The model fits in memory and requires GPU acceleration. Which SageMaker inference option is MOST suitable?

Trap 1: Real-time endpoint on ml.m5 instances

ml.m5 instances are CPU-only; they may not achieve sub-10 ms latency for GPU-accelerated models.

Trap 2: Batch Transform

Batch Transform is for offline, asynchronous predictions on large datasets, not real-time serving.

Trap 3: Serverless Inference

Serverless Inference has a cold start latency that can exceed 10 ms and does not support GPU acceleration.

Study all Deployment and Orchestration of ML Workflows common traps →

A
Real-time endpoint on ml.m5 instances
Why wrong: ml.m5 instances are CPU-only; they may not achieve sub-10 ms latency for GPU-accelerated models.
B
Batch Transform
Why wrong: Batch Transform is for offline, asynchronous predictions on large datasets, not real-time serving.
C
Real-time endpoint on ml.g4dn instances
ml.g4dn instances offer GPU acceleration and are designed for low-latency, real-time inference.
D
Serverless Inference
Why wrong: Serverless Inference has a cold start latency that can exceed 10 ms and does not support GPU acceleration.

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

A data scientist needs to deploy a single ML model that will serve real-time predictions with low latency (under 10 ms) for a high-traffic web application. The model fits in memory and requires GPU acceleration. Which SageMaker inference option is MOST suitable?

A team has 200 small ML models that need to be served via HTTPS endpoints. Each model is used infrequently, and the team wants to minimize hosting costs. Which SageMaker deployment approach is MOST cost-effective?

An ML team uses SageMaker Pipelines to automate model retraining. They want to skip redundant training steps when input data has not changed. Which feature should they enable?

A company needs to deploy a new model version to a SageMaker real-time endpoint. They want to route 5% of traffic to the new version initially to monitor for errors before full rollout. Which deployment strategy should they use?

An ML engineer needs to compile a trained TensorFlow model to run efficiently on a target edge device with an ARM CPU. Which AWS service should they use?

A data science team uses SageMaker Pipelines for automated training. They need to conditionally register a model only if evaluation metrics exceed a threshold. Which pipeline step type should they use after the evaluation step?

A company wants to serve a large ensemble of models using NVIDIA Triton Inference Server on SageMaker for high throughput GPU inference. Which SageMaker inference option supports this?

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 company needs to deploy a model that processes large payloads (up to 1 GB) asynchronously. The results should be written to S3, and the team needs SNS notifications upon completion. Which SageMaker inference option is MOST suitable?

An ML team uses AWS Step Functions to orchestrate a retraining pipeline triggered by EventBridge when new training data arrives. The pipeline includes a SageMaker training job and a model evaluation. If evaluation fails, the team wants to send an alert. How should they implement this?

A startup wants to deploy a containerized ML application that includes both a model inference server and a preprocessing component in the same endpoint. Which SageMaker endpoint type supports running multiple containers?

A company uses SageMaker to deploy a model and wants to perform A/B testing by splitting traffic between two model variants. Which TWO actions should they take? (Select TWO.)

An organization wants to automate ML retraining using an event-driven architecture. Which THREE services should they combine? (Select THREE.)

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 team built a SageMaker Pipeline that includes a training step and a model evaluation step. They want to automatically register a model in SageMaker Model Registry only if the evaluation metric (accuracy) exceeds 0.9. Which pipeline step should be used to implement this conditional logic?

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 data science team uses SageMaker Pipelines to orchestrate their ML workflow. They noticed that even when source data hasn't changed, the pipeline re-runs all steps, wasting compute time. What should they enable to avoid redundant runs?

A company wants to update an existing SageMaker real-time endpoint to serve a new model version. They need to route a small percentage of traffic to the new version initially and monitor for errors before switching fully. Which deployment pattern supports this?

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

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