MLA-C01 Deployment and Orchestration of ML Workflows Practice Test 4 — 15 Questions

Question 1

A team uses SageMaker real-time endpoints for inference. They want to deploy a new model version and compare its performance with the current version under live traffic without affecting user experience. Which method should they use?

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

Accepted Answer

Shadow testing with SageMaker. Shadow testing (or shadow deployment) sends a copy of live traffic to the new model variant while the current variant serves the actual response. The shadow variant's performance can be monitored without impacting the user.

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A/B testing with production variant traffic splitting

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Batch transform on a holdout test set

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Blue/green deployment

Question 2

A company wants to deploy a machine learning model using infrastructure as code to ensure reproducibility. They need to define the SageMaker Studio domain, user profiles, and the endpoint configuration. Which tool should they use?

Accepted Answer

AWS CloudFormation or AWS CDK. AWS CloudFormation and AWS CDK are infrastructure-as-code (IaC) tools that allow you to define, provision, and manage AWS resources declaratively. For this use case, they can model the entire SageMaker Studio domain, user profiles, and endpoint configuration in templates or code, ensuring reproducibility and version control. This aligns directly with the requirement to deploy ML infrastructure as code.

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SageMaker Pipelines

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AWS Step Functions

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SageMaker Studio

Question 3

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.)

Accepted Answer

Pruning (removing redundant weights). SageMaker Neo performs hardware-specific optimizations including quantisation (reducing precision), pruning (removing redundant weights), and operator fusion (combining operations). Knowledge distillation is a training-time technique, not part of Neo. Hyperparameter tuning is done by SageMaker Tuning jobs, not Neo.

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Knowledge distillation

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Hyperparameter tuning

Question 4

A company uses SageMaker Pipelines to automate their ML workflow. They need to add model versioning and approval workflow. Which THREE steps should they include in their pipeline to achieve this? (Choose THREE.)

Accepted Answer

RegisterModel step. The RegisterModel step is correct because it creates a model package in SageMaker Model Registry, which enables versioning and approval workflows. This step registers the trained model artifact along with metadata, allowing the pipeline to track model versions and trigger approval processes for deployment.

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Training step

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Transform step

Question 5

A machine learning team has a model that needs to serve predictions with very low latency (under 10 ms) for a real-time web application. The model is a small ensemble of three neural networks that fits in memory. Which SageMaker inference option is MOST appropriate?

Accepted Answer

SageMaker real-time endpoint. SageMaker real-time endpoints are designed for low-latency, synchronous inference, making them the best fit for a model that must serve predictions in under 10 ms. Since the ensemble of three neural networks fits in memory, a real-time endpoint can keep the model loaded and respond to each request with minimal overhead, typically using HTTPS and the SageMaker InvokeEndpoint API.

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SageMaker batch transform

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SageMaker asynchronous inference

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SageMaker serverless inference

Question 6

A company wants to deploy a single model that processes images from a production line. The images are uploaded to an S3 bucket every few minutes, and the inference results must be stored back to S3. The team wants to avoid paying for idle compute and prefers a fully managed, on-demand solution. Which SageMaker inference option should they use?

Accepted Answer

SageMaker asynchronous inference. Asynchronous inference is designed for this use case: it processes images from S3 input, writes results to S3 output, scales to zero when idle, and is fully managed. Real-time endpoints are always running and incur cost when idle. Batch transform is not event-driven. Serverless inference is event-driven but has a payload limit and cold start that may not be suitable for image payloads.

Answer

SageMaker batch transform

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SageMaker real-time endpoint with auto scaling

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SageMaker serverless inference

Question 7

A company is using SageMaker Pipelines to orchestrate their ML workflow. They have a Condition step that checks if a model's accuracy exceeds 0.9. If true, they want to register the model in the model registry; otherwise, they want to run a retraining step. Which step type should they use for the decision?

Accepted Answer

Condition step. The Condition step in SageMaker Pipelines allows you to choose between two branches based on a condition. The other options are not designed for branching: Transform is for batch inference, Tuning is for hyperparameter optimization, and Processing is for data processing.

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Transform step

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Processing step

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Tuning step

Question 8

A company uses SageMaker Pipelines to automate their ML workflow. They notice that the pipeline reruns all steps even when the input data has not changed. Which feature should they enable to avoid unnecessary recomputation?

Accepted Answer

Enable pipeline caching. Pipeline caching in SageMaker Pipelines automatically reuses the output of a step if its inputs (including parameters, data, and code) have not changed since the last successful execution. This avoids recomputation by comparing a hash of the step's dependencies against previous runs, making it the correct feature to prevent unnecessary reruns when input data remains identical.

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Use a Lambda step to check input changes

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Use a Conditional step to skip steps

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Set the pipeline execution mode to 'Parallel'

Question 9

A company needs to update a model in production without any downtime. They currently have a single real-time endpoint serving traffic. Which approach allows them to deploy a new model version and switch traffic gradually while being able to roll back quickly?

Accepted Answer

Use a canary deployment by creating a new production variant with the new model and shifting traffic incrementally. SageMaker supports production variants with traffic splitting. By creating a new variant with the new model and shifting traffic gradually, the old variant remains available for rollback. Blue/green deployment with a new endpoint and endpoint configuration swap also allows quick rollback. The key is to have both variants active during the transition.

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Use a multi-model endpoint and replace the model file

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Stop the endpoint, update the model, and restart the endpoint

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Update the existing endpoint's model directly using UpdateEndpoint

Question 10

Which SageMaker feature compiles a trained model into an optimized binary for a specific hardware target (e.g., Intel, ARM, NVIDIA, or edge devices) to improve inference performance?

Accepted Answer

SageMaker Neo. SageMaker Neo is a model compilation service that optimizes models for specific hardware targets. Amazon Elastic Inference attaches GPU acceleration to endpoints, but does not compile models. Model Monitor monitors quality. SageMaker Clarify explains predictions.

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SageMaker Model Monitor

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Amazon Elastic Inference

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SageMaker Clarify

Question 11

A team has a SageMaker Pipeline that trains a model and registers it in the Model Registry. They want to automate the deployment of the approved model to a staging environment. Which event-driven approach should they use?

Accepted Answer

Use Amazon EventBridge to listen for Model Registry approval events and trigger an AWS Lambda function that deploys the model. The Amazon EventBridge integration with SageMaker can trigger on Model Registry status changes (e.g., when a model version is approved). A Lambda function can then deploy the model to a staging endpoint. Step Functions can be used, but the trigger should be EventBridge. CloudWatch alarms are for monitoring metrics.

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Use an SQS queue to store approval messages and have a cron job process them

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Set up a CloudWatch alarm on the Model Registry's ApprovalStatus metric

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Configure an AWS Step Functions state machine to poll the Model Registry every minute

Question 12

An ML engineer is designing a SageMaker Pipeline for a computer vision model. The pipeline includes steps for data processing, training, evaluation, and registration. The engineer wants to enable caching to avoid reprocessing when step inputs have not changed. For which steps is caching supported? (Select TWO.)

Accepted Answer

Processing step. Caching is supported for the following step types: Processing, Training, Tuning, Transform, and AutoML. Condition steps and Lambda steps do not support caching because they are control flow steps.

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Condition step

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Lambda step

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RegisterModel step

Question 13

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.)

Accepted Answer

Amazon Elastic Inference. Amazon Elastic Inference (Option A) allows you to attach a smaller, configurable GPU acceleration resource to a SageMaker endpoint, enabling GPU-accelerated inference without the cost of a full GPU instance. This directly meets the requirement of minimizing costs by using a smaller attached GPU.

Answer

SageMaker Neo compilation

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Quantize the model to INT8 precision

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Use SageMaker serverless inference with GPU

Question 14

A team is migrating their ML infrastructure to AWS and wants to use infrastructure as code to manage SageMaker Studio domains, user profiles, and associated resources. Which services can they use for this purpose? (Select THREE.)

Accepted Answer

AWS CDK (Cloud Development Kit). AWS CDK (Cloud Development Kit) is correct because it allows you to define AWS infrastructure, including SageMaker Studio domains and user profiles, using familiar programming languages like Python or TypeScript. CDK synthesizes these definitions into CloudFormation templates, enabling infrastructure as code (IaC) for SageMaker resources. This approach provides type safety and high-level abstractions, making it suitable for managing complex ML environments.

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SageMaker Python SDK

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Boto3

Question 15

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?

Accepted Answer

SageMaker real-time endpoint with Multi-Model Endpoint (MME) on an ml.g4dn instance. SageMaker Multi-Model Endpoint (MME) allows multiple PyTorch models to share a single GPU instance (e.g., ml.g4dn), reducing cost while meeting sub-100ms latency requirements. MME dynamically loads and unloads models into GPU memory based on traffic, enabling real-time inference with GPU acceleration without dedicating a full instance per model.

Answer

SageMaker real-time endpoint with a single model per ml.g4dn instance

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SageMaker Serverless Inference

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SageMaker Asynchronous Inference