MLA-C01 Deployment and Orchestration of ML Workflows Practice Test 7 — 15 Questions

Question 1

A machine learning team uses SageMaker Pipelines to automate retraining. They want to avoid re-running data processing steps if the data has not changed since the last successful pipeline run. Which built-in feature should they enable?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Pipeline caching. Pipeline caching is the correct choice because SageMaker Pipelines can cache the outputs of each step based on a hash of the step's input parameters, configuration, and code. If the hash matches a previous successful run, the cached output is reused, avoiding redundant execution of data processing steps when the underlying data hasn't changed.

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Model lineage tracking

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Parameterized pipeline executions

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Step parallelism

Question 2

A team needs to deploy a new model version to production while minimizing risk. They want to route 5% of live traffic to the new model and 95% to the current model, and then gradually increase the new model's traffic. Which SageMaker deployment pattern should they use?

Accepted Answer

Canary deployment using production variants. Canary deployment using production variants in Amazon SageMaker allows you to route a small percentage of live traffic (e.g., 5%) to the new model version while the rest goes to the existing model. You can then gradually increase the traffic to the new model as confidence grows, minimizing risk. This pattern is specifically designed for progressive rollouts with the ability to roll back if issues arise.

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Shadow testing

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Blue/green deployment

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A/B testing with production variants

Question 3

A company wants to run inference on a large dataset stored in S3 using a pre-trained model. The inference can tolerate latency from minutes to hours, and they want a fully managed solution that autoscales to handle large volumes. Which SageMaker inference option is most suitable?

Accepted Answer

Batch transform. Batch transform is the most suitable option because the company needs to run inference on a large dataset stored in S3 with latency tolerance from minutes to hours, and requires a fully managed, autoscaling solution. SageMaker Batch Transform processes the entire dataset as a single job, automatically provisions and scales compute resources, and writes results back to S3 without the need for persistent endpoints.

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Real-time endpoint

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Asynchronous inference

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Serverless inference

Question 4

A machine learning engineer needs to optimize a trained TensorFlow model for deployment on edge devices with limited compute. Which SageMaker feature should they use to compile the model for target hardware?

Accepted Answer

SageMaker Neo. SageMaker Neo is the correct choice because it is specifically designed to compile trained machine learning models into an optimized format for target hardware architectures, such as ARM, Intel, or NVIDIA, enabling efficient inference on edge devices with limited compute resources. It uses a compiler to apply hardware-specific optimizations like operator fusion and memory layout tuning, reducing latency and memory footprint without requiring manual code changes.

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SageMaker Model Monitor

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SageMaker Debugger

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SageMaker Elastic Inference

Question 5

A company wants to deploy a PyTorch model on SageMaker using the NVIDIA Triton Inference Server for GPU acceleration. They have an existing Triton configuration. Which approach should they take?

Accepted Answer

Use the SageMaker Triton Inference Server container from the Deep Learning Containers. AWS provides a pre-built SageMaker Triton Inference Server container as part of the Deep Learning Containers (DLCs), which is optimized for GPU acceleration and supports the existing Triton configuration without modification. This container integrates directly with SageMaker hosting endpoints, enabling seamless deployment of PyTorch models with Triton's features like dynamic batching and model concurrency.

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Use SageMaker Neo to compile the model for Triton

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Package Triton as a custom container and use SageMaker batch transform

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Use the standard SageMaker PyTorch container and install Triton at runtime

Question 6

A team needs to deploy a model that has compliance requirements to log all inference requests and responses for auditing. The model will be served using a real-time endpoint. How can they achieve this without custom code?

Accepted Answer

Enable SageMaker Data Capture on the endpoint. SageMaker Data Capture is the native, no-code feature that automatically logs inference requests and responses for real-time endpoints. It captures payload data to an S3 bucket without requiring any custom code, directly meeting the compliance requirement for audit logging.

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Add a custom Lambda function using a container

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Use SageMaker Debugger to monitor inference

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Enable CloudTrail for the endpoint

Question 7

A company wants to version and track ML models, with an approval workflow for promoting models from staging to production. Which SageMaker feature should they use?

Accepted Answer

SageMaker Model Registry. SageMaker Model Registry is the correct choice because it provides a centralized repository to catalog, version, and manage ML models, and it supports approval workflows (e.g., PendingApproval, Approved, Rejected) to promote models from staging to production. This directly addresses the requirement for version tracking and an approval gate for model promotion.

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SageMaker Model Monitor

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SageMaker Experiments

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SageMaker Pipelines

Question 8

A startup wants to deploy a model that has variable traffic patterns, with some periods of no traffic and occasional spikes. They want to pay only for what they use and do not want to manage instances. Which SageMaker inference option should they choose?

Accepted Answer

Serverless inference. Serverless inference is the correct choice because it automatically scales to zero during periods of no traffic and scales up to handle spikes, charging only for the compute time used. This eliminates the need to manage underlying instances, making it ideal for variable and intermittent traffic patterns.

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Batch transform

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Real-time endpoint with auto-scaling

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Multi-model endpoint

Question 9

A company wants to deploy a new model using a canary deployment strategy on SageMaker. Which two actions should they take? (Select TWO.)

Accepted Answer

Create a new endpoint with two production variants. Option C is correct because a SageMaker canary deployment is implemented by creating an endpoint configuration with two production variants (the existing/stable model and the new model) and deploying them behind a single endpoint, which is the foundational mechanism for splitting traffic between versions. Option E is correct because the canary pattern requires assigning initial traffic weights to those variants—typically a small percentage (e.g., 5%) to the new model and the remainder (e.g., 95%) to the stable model—so the new model receives limited live traffic before being promoted. Option A is not required for the deployment itself; Model Registry 'Approved' status is a governance/approval step, not a technical prerequisite for creating canary variants. Option B is not required because Model Monitor is used for detecting data/quality drift and bias, not for comparing model performance during a canary rollout. Option D is not required because data capture is an optional monitoring feature that logs request/response data, not a necessary action to perform a canary deployment.

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Register both models in the Model Registry with 'Approved' status

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Use SageMaker Model Monitor to compare model performance

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Enable data capture on the endpoint

Question 10

A company wants to serve 200 different PyTorch models. Each model is small (under 1 GB) and only a fraction are used at any time. To minimize cost and management overhead, which SageMaker inference option should be used?

Accepted Answer

Use a multi-model endpoint. A multi-model endpoint (MME) is the correct choice because it allows you to host multiple small PyTorch models (under 1 GB each) on a single endpoint, sharing the underlying compute instance. This minimizes cost by only paying for the active instances, and reduces management overhead since you don't need to create or manage separate endpoints for each model. SageMaker dynamically loads and unloads models from the container's memory based on invocation patterns, which is ideal for a scenario where only a fraction of the 200 models are used at any time.

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Use batch transform for all models

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Create a separate real-time endpoint for each model

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Use a multi-container endpoint

Question 11

A machine learning team needs to deploy a new model version for A/B testing, gradually shifting traffic from the old version to the new version over 24 hours. Which deployment strategy should they use?

Accepted Answer

Canary deployment. Canary deployment is the correct strategy because it allows gradual traffic shifting from the old model version to the new one over a specified time period (e.g., 24 hours) while monitoring for errors or performance degradation. This approach minimizes risk by exposing only a small percentage of users to the new version initially, then incrementally increasing traffic as confidence grows, which aligns perfectly with the A/B testing requirement.

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Blue/green deployment

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Shadow testing

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Direct deployment with immediate full traffic

Question 12

A company needs to serve real-time predictions from a large ensemble of three deep learning models, each requiring different inference environments (PyTorch, TensorFlow, MXNet). Which SageMaker endpoint type supports running multiple inference containers together?

Accepted Answer

Multi-container endpoint. Amazon SageMaker multi-container endpoints allow you to run multiple inference containers (e.g., PyTorch, TensorFlow, MXNet) within a single endpoint, each handling different models or inference environments. This is achieved by deploying multiple containers behind a single endpoint with a serial or direct invocation pattern, enabling real-time predictions from the ensemble without managing separate endpoints.

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Multi-model endpoint

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Real-time endpoint with a single container

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Asynchronous endpoint

Question 13

A data scientist wants to train a model on SageMaker using a custom PyTorch script, then register the best model in the SageMaker Model Registry. The training job is part of a SageMaker Pipeline. Which pipeline step should be used to register the model?

Accepted Answer

RegisterModelStep. The `RegisterModelStep` is specifically designed to create a model resource and register it in the SageMaker Model Registry as part of a pipeline. It takes the training output (e.g., model artifacts from a `TrainingStep`) and packages it with the specified inference image and metadata, then creates a model package group version. This is the correct step for registering a model after training, as it directly integrates with the Model Registry for versioning and approval workflows.

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CreateModelStep

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TrainingStep

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TransformStep

Question 14

A company uses SageMaker Neo to compile a trained model for deployment on edge devices. What is the primary benefit of using Neo?

Accepted Answer

It reduces model size and improves inference speed on target hardware. SageMaker Neo compiles trained models into optimized executables for specific target hardware (CPU, GPU, or edge accelerators), producing smaller artifacts and faster inference by leveraging hardware-specific instruction sets. It is designed for edge and constrained deployments where latency, memory, and compute are limited. Neo does not handle monitoring, retraining, or endpoint provisioning.

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It monitors model drift in production

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It automatically retrains the model on new data

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It provides a serverless inference endpoint

Question 15

A team uses MLflow on SageMaker for experiment tracking. They want to automate the retraining of a model when new training data arrives in an S3 bucket. Which combination of services should they use?

Accepted Answer

EventBridge -> Lambda -> SageMaker Training Job. EventBridge can capture S3 events (via CloudTrail or S3 event notifications routed to EventBridge) and trigger a Lambda function that starts a SageMaker Training Job. This event-driven pattern is the standard AWS-native way to automate retraining when new data lands in S3. It avoids polling and integrates cleanly with MLflow tracking on SageMaker.

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SageMaker Pipelines scheduled trigger every hour

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S3 Event Notifications -> SQS -> SageMaker Training Job

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AWS Step Functions with S3 poller