MLA-C01 Deployment and Orchestration of ML Workflows Practice Test 2 — 15 Questions

Question 1

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?

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

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

Question 2

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?

Accepted Answer

Use MLflow tracking server on SageMaker, then use the SageMaker MLflow plugin to register the model in SageMaker Model Registry. The SageMaker MLflow plugin (sagemaker-mlflow) allows you to use an MLflow tracking server hosted on SageMaker for experiment tracking, and then directly register the best model into the SageMaker Model Registry via the plugin's integration. This avoids manual export steps and keeps the model lifecycle management within SageMaker's native registry, which is required by the team's goal.

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Use MLflow to track experiments and register models in MLflow's native registry, then export to SageMaker

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Use SageMaker Experiments for tracking, then manually register the model using SageMaker console

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MLflow cannot be used with SageMaker; use SageMaker Experiments instead

Question 3

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?

Accepted Answer

Set the model version status to 'Approved' in the Model Registry. The SageMaker Model Registry uses a status field to control the lifecycle of model versions. By setting the model version status to 'Approved', the company can enforce that only approved models are deployable, as SageMaker's deployment APIs (e.g., CreateModel, CreateEndpointConfig) can be configured to require an 'Approved' status. This integrates with the approval workflow, ensuring that unapproved or pending versions are blocked from production deployment.

Answer

Tag the model version as 'production-ready'

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Manually move the model artifact to a production S3 bucket

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Use AWS IAM policies to restrict deployment to specific model ARNs

Question 4

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.)

Accepted Answer

Enable step caching in the pipeline definition. Enabling step caching in the pipeline definition (Option E) is the correct action to reuse outputs from previous runs when input data and code have not changed. Option A is incorrect because 'Set StepStatus to Cached' is not a user action; the 'Cached' status is automatically assigned by SageMaker Pipelines when caching is enabled and a cache hit occurs. The other options do not achieve output reuse.

Answer

Set the StepStatus of successful steps to 'Cached'

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Use parallel execution of pipeline steps

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Create multiple pipeline versions for each run

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Disable caching for all steps to avoid unnecessary storage costs

Question 5

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.)

Accepted Answer

Amazon Elastic Inference attaches a dedicated GPU accelerator to a CPU instance, reducing cost compared to a full GPU instance. Option A is correct because Amazon Elastic Inference attaches a fractional GPU accelerator to an existing CPU instance (e.g., via an EI accelerator endpoint), providing GPU-accelerated inference at a lower cost than provisioning a full GPU instance. Option D is correct because SageMaker Neo compiles a trained model for a specific target hardware platform, producing an optimized executable that reduces inference latency and memory footprint. Option B is incorrect because Neo-compiled models can be deployed to Elastic Inference-enabled endpoints, so they are not mutually exclusive. Option C is incorrect because Neo is a model compilation/optimization service, not a GPU acceleration service like Elastic Inference. Option E is incorrect because Elastic Inference does not compile models; it only provides attached accelerator capacity for inference.

Answer

SageMaker Neo and Amazon Elastic Inference cannot be used together

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SageMaker Neo provides a GPU acceleration service similar to Elastic Inference

Answer

Amazon Elastic Inference compiles the model to run on GPU hardware

Question 6

A team uses SageMaker Pipelines with a Condition step to decide whether to register a model based on evaluation metrics. They want to also store the evaluation results for lineage tracking. Which step should they use to record the metrics?

Accepted Answer

RegisterModel step. The RegisterModel step in SageMaker Pipelines is designed to create a model version in the SageMaker Model Registry, and it can accept metadata such as evaluation metrics via the `InferenceSpecification` or by passing a metrics dictionary. This allows the team to store evaluation results alongside the model for lineage tracking, fulfilling the requirement to record metrics after a Condition step approves registration.

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Condition step

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Training step

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Processing step

Question 7

A team wants to implement an event-driven retraining pipeline that triggers retraining when new data arrives in an S3 bucket. The pipeline should include preprocessing, training, evaluation, and conditional registration. Which AWS services should they combine?

Accepted Answer

S3 + Lambda + SageMaker Pipeline. S3 triggers a Lambda function on new data arrival, which invokes SageMaker Pipeline for the full ML workflow (preprocessing, training, evaluation, and conditional registration). SageMaker Pipeline natively supports conditional steps for model registration, making it the only option that directly satisfies the conditional registration requirement without additional custom logic.

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S3 + SQS + SageMaker Batch Transform

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S3 + Step Functions + SageMaker Training Job

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S3 + EventBridge + SageMaker Model Registry

Question 8

A machine learning engineer is designing a SageMaker Pipeline that includes a training step, a processing step for evaluation, and a condition step to decide whether to register the model. The pipeline should support caching to avoid redundant runs when inputs haven't changed. Which three steps must have caching enabled? (Select THREE.)

Accepted Answer

Training step. In a SageMaker Pipeline, caching is configured per step via the CacheConfig property, and it applies to steps that perform actual work whose outputs can be reused when inputs, code, and configuration are unchanged. Option A (Training step) is correct because training is an expensive, deterministic job whose model artifacts can be reused, so enabling caching avoids redundant retraining. Option C (Processing step for evaluation) is correct because the evaluation processing job produces metrics/reports that can be cached and reused when its inputs and container are unchanged. Option E (RegisterModel step) is correct because it is a pipeline step that can be cached so the model package registration is skipped if the same model and metadata were already registered. Option B (Transform step) is not among the required three for this scenario, and Option D (Condition step) is a control-flow step that does not produce cacheable artifacts, so caching does not apply to it.

Answer

Transform step (if used)

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Condition step

Question 9

A team wants to deploy a single SageMaker real-time endpoint that serves both a PyTorch model for NLP and a TensorFlow model for image classification. Each model requires a different inference container. Which two features can they use together to achieve this? (Select TWO.)

Accepted Answer

Multi-container endpoint. Option B (Multi-container endpoint) is correct because SageMaker multi-container endpoints allow up to 15 different inference containers to be hosted behind a single endpoint, with each container running its own framework (here PyTorch and TensorFlow) and routing requests via a container-invocation path such as /models/<model_name>/invoke. Option D (SageMaker inference components) is correct because inference components let you package each model plus its container, resources, and scaling policy as a separately deployable unit on a shared endpoint, so the PyTorch NLP model and TensorFlow image model can coexist and scale independently. Together, multi-container endpoints provide the container-level hosting mechanism while inference components provide the per-model deployment and scaling abstraction. Option A (Multi-model endpoint) is not suitable because MMEs share a single container and are designed for many models of the same framework, not different frameworks. Option C (Production variants) is not correct because variants route traffic to different versions of a model within the same container configuration, not to distinct inference containers. Option E (SageMaker Neo compilation) is not correct because Neo is a model-compilation/optimization tool, not a hosting or routing feature.

Answer

Multi-model endpoint

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Production variants

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SageMaker Neo compilation

Question 10

A team uses SageMaker Pipelines to retrain a model nightly. They want to skip the training step if the new data is unchanged (same checksum as previous run) to save cost and time. Which pipeline configuration achieves this?

Accepted Answer

Use a ConditionStep that compares the current data checksum to the previous run's checksum, and branch to a NoOp step if unchanged. SageMaker Pipelines' ConditionStep allows you to evaluate a condition—such as comparing the current data checksum to a stored previous checksum—and branch accordingly. If the checksums match, you can route to a NoOp step (which does nothing) instead of executing the training step, thereby skipping the training and saving cost and time. This is the native, recommended pattern for conditional execution in SageMaker Pipelines.

Answer

Enable pipeline caching on the training step

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Use a Lambda step to check data before running the training step

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Set the training step's CacheConfig with a TTL of 24 hours

Question 11

A team wants to deploy a model that performs inference on large video files (up to 2 GB each) uploaded to an S3 bucket. The inference can tolerate a few minutes of latency. Which SageMaker inference option is most cost-effective?

Accepted Answer

Asynchronous inference. Asynchronous inference is the most cost-effective option for large video files (up to 2 GB) with a tolerance for a few minutes of latency because it queues incoming requests, processes them in the background, and automatically scales down to zero when idle, eliminating the cost of idle compute. It supports payloads up to 1 GB natively and can handle larger files via S3 input, making it ideal for this workload without requiring a continuously running endpoint.

Answer

Batch transform

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Serverless inference

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Real-time endpoint

Question 12

A team wants to orchestrate a multi-step ML workflow that includes data preprocessing, hyperparameter tuning, model training, evaluation, and conditional deployment to staging or production based on evaluation metrics. The workflow should run on a schedule and track lineage. Which service should they use?

Accepted Answer

SageMaker Pipelines. SageMaker Pipelines is the correct choice because it is purpose-built for orchestrating multi-step ML workflows, including data preprocessing, hyperparameter tuning, model training, evaluation, and conditional deployment. It natively supports scheduling via EventBridge or a cron expression, tracks lineage automatically through SageMaker Experiments and artifact tracking, and allows conditional branching (e.g., deploy to staging or production based on evaluation metrics) using `ConditionStep`.

Answer

Amazon MWAA (Managed Workflows for Apache Airflow)

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AWS Glue workflows

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AWS Step Functions with Lambda functions for each step

Question 13

A team deploys a model on a SageMaker real-time endpoint using an ml.m5.xlarge instance. The model has high latency due to a large neural network. The team wants to reduce latency without changing the model code. Which option should they use?

Accepted Answer

Attach Amazon Elastic Inference to the endpoint. Amazon Elastic Inference attaches a fixed amount of GPU acceleration to an EC2 instance, providing cost-effective acceleration for deep learning inference without needing a full GPU instance.

Answer

Increase the instance size to ml.m5.4xlarge

Answer

Use SageMaker Neo to compile the model

Answer

Switch to a GPU instance like ml.g4dn.xlarge

Question 14

A company uses SageMaker Model Registry to manage model versions. They want to enforce that only models with an 'Approved' status can be deployed to production endpoints. How can they enforce this?

Accepted Answer

Set IAM policies with a condition on sagemaker:ModelVersionStatus. SageMaker Model Registry supports approval workflows. By using IAM policies that conditionally allow deployment only when the model version status is 'Approved', the company can enforce governance.

Answer

Use AWS Lambda to check the model status during deployment

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Use SageMaker Pipelines to deploy only approved models

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Configure SCPs to block deployment of unapproved models

Question 15

A machine learning engineer is deploying a TensorFlow model for real-time inference. The model has high latency on CPU. Which TWO actions can reduce inference latency? (Choose two.)

Accepted Answer

Attach Amazon Elastic Inference to the endpoint. Compiling with SageMaker Neo optimizes the model for the target hardware. Attaching Elastic Inference provides GPU acceleration without moving to a full GPU instance.

Answer

Enable SageMaker Model Monitor

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Switch to a multi-model endpoint

Answer

Use a larger instance type with more vCPUs