MLA-C01 Deployment and Orchestration of ML Workflows Practice Test 6 — 15 Questions

Question 1

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.

Answer

SageMaker Model Monitor

Answer

Amazon Elastic Inference

Answer

SageMaker Clarify

Question 2

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. Amazon EventBridge natively integrates with SageMaker Model Registry and emits events such as 'SageMaker Model Package State Change' when a model package's approval status changes to Approved. An EventBridge rule can match that event pattern and invoke a Lambda function (or Step Functions, CodePipeline, etc.) to deploy the model to staging. This is the canonical event-driven pattern for automating post-approval deployment in SageMaker MLOps workflows.

Answer

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

Answer

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

Answer

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

Question 3

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 (A) is correct because it is an infrastructure-as-code framework that synthesizes CloudFormation templates, and it provides L2 constructs such as CfnDomain, CfnUserProfile, and CfnApp for managing SageMaker Studio domains, user profiles, and associated resources declaratively. Terraform by HashiCorp (D) is correct because it is a third-party IaC tool with dedicated AWS provider resources like aws_sagemaker_domain, aws_sagemaker_user_profile, and aws_sagemaker_app that let teams manage the same SageMaker Studio resources in HCL state files. AWS CloudFormation (E) is correct because it is AWS's native IaC service and directly supports the AWS::SageMaker::Domain, AWS::SageMaker::UserProfile, and AWS::SageMaker::App resource types for provisioning and updating these resources. The SageMaker Python SDK (B) is not an infrastructure-as-code service; it is a high-level library for training, deploying, and interacting with models and endpoints, not for declaratively managing Studio domain infrastructure. Boto3 (C) is the AWS SDK for Python and can call SageMaker APIs imperatively, but it is not an infrastructure-as-code tool with declarative templates or state management, so it does not fit the IaC requirement.

Answer

SageMaker Python SDK

Answer

Boto3

Question 4

A machine learning engineer wants to automatically trigger a retraining pipeline whenever new training data arrives in an S3 bucket. The pipeline uses SageMaker Pipelines. Which AWS service should be used to detect the S3 event and start the pipeline?

Accepted Answer

Amazon EventBridge. Amazon EventBridge can be configured to listen for S3 events (e.g., PutObject) and then invoke a Lambda function that starts the SageMaker Pipeline execution. Step Functions could orchestrate the pipeline but is not needed to trigger on S3 events. SageMaker Pipelines does not natively listen to S3 events. CloudWatch Events is the older name for EventBridge.

Answer

SageMaker Pipelines native S3 trigger

Answer

AWS Step Functions

Answer

Amazon CloudWatch Logs

Question 5

A financial services company needs to deploy a machine learning model for real-time fraud detection. The model must be highly available across multiple Availability Zones and must support automatic scaling based on request volume. The company also needs to perform canary deployments to test new model versions with a small percentage of traffic before full rollout. Which SageMaker feature should they use?

Accepted Answer

SageMaker real-time endpoint with production variants. SageMaker real-time endpoints with production variants enable canary deployments by routing a small percentage of traffic to a new model version while the majority goes to the current version. This feature also supports multi-AZ deployment for high availability and automatic scaling based on request volume via Application Auto Scaling, meeting all the stated requirements.

Answer

SageMaker Multi-Model Endpoint

Answer

SageMaker Batch Transform

Answer

SageMaker Serverless Inference

Question 6

A company wants to deploy a trained XGBoost model for batch inference on a large dataset stored in S3. The inference job should be cost-effective and does not require real-time responses. Which SageMaker inference option should they use?

Accepted Answer

SageMaker Batch Transform. SageMaker Batch Transform is designed for offline, high-throughput inference on large datasets stored in S3, with no persistent endpoint and no real-time requirement. It is the most cost-effective option for this scenario because you pay only for the duration of the batch job.

Answer

SageMaker real-time endpoint

Answer

SageMaker Asynchronous Inference

Answer

SageMaker Serverless Inference

Question 7

A machine learning engineer needs to deploy a TensorFlow model that requires a custom inference environment with specific system libraries. The model will be used in a real-time application with variable traffic. They want to minimize cold start latency. Which SageMaker hosting option should they choose?

Accepted Answer

SageMaker real-time endpoint with a custom container. SageMaker real-time endpoints with a custom container are the correct choice because they provide persistent, always-on infrastructure that eliminates cold start latency. By packaging the TensorFlow model with required system libraries in a custom Docker image, the engineer ensures the inference environment is ready immediately, and the endpoint can scale to handle variable traffic with minimal delay.

Answer

SageMaker Serverless Inference with a custom container

Answer

SageMaker Multi-Model Endpoint with a custom container

Answer

SageMaker Asynchronous Inference with a custom container

Question 8

A company uses SageMaker Pipelines to orchestrate their ML workflow. They notice that if a pipeline step fails due to a transient error (e.g., a brief network issue), the entire pipeline fails and they must manually rerun from the beginning. They want to automatically retry failed steps a few times before failing. What should they do?

Accepted Answer

Configure a RetryPolicy in the pipeline step definition to specify the number of retry attempts and backoff. SageMaker Pipelines supports retry policies for steps. By setting a RetryPolicy in the step definition with a maximum number of retry attempts, the pipeline will automatically retry the step on failure. The other options do not achieve automatic retry within the pipeline: Step Functions would require rebuilding the pipeline, Lambda cannot retry pipeline steps, and CreatePipelineExecution does not handle retries.

Answer

Use a Lambda function to catch step failures and re-invoke the step

Answer

Use the CreatePipelineExecution API with a flag to ignore failures

Answer

Use AWS Step Functions to orchestrate the workflow instead of SageMaker Pipelines

Question 9

A data scientist wants to compare the performance of two model versions (V1 and V2) in production by splitting traffic between them. They want to gradually increase the percentage of traffic to the new version while monitoring metrics. Which SageMaker feature enables this?

Accepted Answer

SageMaker production variants with traffic splitting. SageMaker production variants with traffic splitting let you host multiple model versions behind a single endpoint and assign a percentage of invocations to each variant. To compare V1 and V2 and gradually shift traffic, you create an endpoint configuration with two production variants and set the initial traffic distribution (e.g., 90/10), then update the weights as you gain confidence. This is the native SageMaker mechanism for A/B comparison and gradual rollout.

Answer

SageMaker shadow testing

Answer

SageMaker blue/green deployment

Answer

SageMaker canary deployment

Question 10

A company is deploying a large NLP model on SageMaker for real-time inference. They want to reduce inference latency and cost by optimizing the model for the target hardware. The model is trained in PyTorch. Which SageMaker feature should they use to compile the model for best performance on the chosen instance?

Accepted Answer

SageMaker Neo. SageMaker Neo is the correct choice because it is specifically designed to compile trained models (including PyTorch models) into an optimized binary for a target hardware instance, reducing inference latency and improving throughput. Neo applies hardware-specific optimizations such as operator fusion, memory layout tuning, and quantization, which directly address the need for best performance on the chosen SageMaker instance.

Answer

AWS Step Functions

Answer

Amazon Elastic Inference

Answer

SageMaker Triton Inference Server

Question 11

A team needs to deploy a PyTorch model that uses custom CUDA kernels. They want to use NVIDIA Triton Inference Server on SageMaker for high-performance serving. Which SageMaker configuration is required to use Triton?

Accepted Answer

Use the SageMaker pre-built Triton Inference Server container available in Amazon ECR. SageMaker provides a pre-built Triton Inference Server container in Amazon ECR that is optimized for high-performance serving of models, including those with custom CUDA kernels. This container eliminates the need to build a custom image from scratch, ensuring compatibility with SageMaker's deployment infrastructure and reducing operational overhead.

Answer

Create a custom container from scratch with Triton and deploy on SageMaker

Answer

Use a Multi-Model Endpoint with Triton

Answer

Attach an Amazon Elastic Inference accelerator to the endpoint

Question 12

A company wants to deploy 50 small models (each ~100 MB) for real-time inference. They need to minimize hosting costs while maintaining low latency. Which SageMaker hosting option is most cost-effective?

Accepted Answer

SageMaker Multi-Model Endpoint (MME). SageMaker Multi-Model Endpoint (MME) hosts many models on a single endpoint and dynamically loads them into memory/disk on invocation, sharing the underlying instance. For 50 small models (~100 MB each), this dramatically reduces hosting cost versus one endpoint per model while still providing real-time, low-latency inference. Serverless Inference is pay-per-invoke but has cold starts and memory limits that can hurt latency for frequent calls.

Answer

SageMaker Serverless Inference

Answer

SageMaker Asynchronous Inference

Answer

SageMaker real-time endpoint with one instance per model

Question 13

A company is using AWS Step Functions to orchestrate their ML retraining pipeline. They want to trigger retraining when new data arrives, but only if the model's performance has degraded below a threshold. Which THREE AWS services should they use together to achieve this? (Choose three.)

Accepted Answer

AWS Step Functions. Amazon EventBridge (C) is the correct event-routing service to detect the arrival of new data (e.g., an S3 PutObject event) and trigger the pipeline, since it can match events and invoke targets such as Step Functions. AWS Step Functions (A) is correct because it orchestrates the multi-step ML retraining workflow, including the conditional logic that checks whether model performance has degraded below the threshold before proceeding. AWS Lambda (B) is correct because it provides the serverless compute to evaluate the performance metric against the threshold and return a decision that Step Functions uses in its Choice state to branch into retraining or stop. Amazon CloudWatch Logs (D) is not correct because it is a logging/monitoring service, not an event trigger or orchestration component for this pipeline. SageMaker Model Registry (E) is not correct because, while it tracks model versions and approval status, it does not itself trigger retraining based on incoming data or performance degradation.

Answer

Amazon CloudWatch Logs

Answer

SageMaker Model Registry

Question 14

A data science team has trained a PyTorch model for real-time inference and needs to deploy it on AWS with GPU acceleration while minimizing cold-start latency. Which SageMaker inference option should they choose?

Accepted Answer

Real-time endpoint with ml.g4dn instance. Real-time endpoints with GPU instances (e.g., ml.g4dn) provide low latency and support GPU acceleration, suitable for interactive inference. Serverless inference does not support GPU instances, asynchronous inference is for non-real-time, and batch transform is for offline predictions.

Answer

Serverless inference

Answer

Batch transform

Answer

Asynchronous inference endpoint

Question 15

A company has 50 small PyTorch models that are used infrequently for inference. They want to minimize costs while maintaining the ability to serve all models from a single endpoint. Which SageMaker feature should they use?

Accepted Answer

Multi-model endpoint. SageMaker multi-model endpoints allow hosting multiple models on a single endpoint, loading them on demand and sharing the same serving container. This is cost-effective for many infrequently used models because you only pay for the endpoint instance and not for separate endpoints per model. It supports hundreds of models and dynamically loads them from S3.

Answer

Multi-container endpoint

Answer

Batch transform job

Answer

Real-time endpoint with 50 production variants