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

Question 1

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?

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

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

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Use a Multi-Model Endpoint with Triton

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Attach an Amazon Elastic Inference accelerator to the endpoint

Question 2

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 3

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. Amazon Elastic Inference allows you to attach a fraction of a GPU accelerator to an Amazon EC2 CPU instance, providing GPU acceleration at a lower cost than using a full GPU instance. This reduces inference cost by only paying for the GPU compute you need, without the overhead of a dedicated GPU instance.

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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

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Amazon Elastic Inference compiles the model to run on GPU hardware

Question 4

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 5

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.

Answer

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 6

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.

Answer

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 7

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 8

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 9

A team uses SageMaker Pipelines to automate retraining. They want to skip the training step if the data has not changed since the last run. Which feature should they enable?

Accepted Answer

Step caching. Step caching in SageMaker Pipelines allows you to reuse the output from a previous execution of a step if its input data and configuration parameters have not changed. By enabling caching on the training step, the pipeline automatically skips re-executing that step when the data is identical, saving time and cost. This directly addresses the requirement to skip retraining when data has not changed.

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Parameterized executions

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Lineage tracking

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Condition step with a custom check

Question 10

A company wants to trigger a model retraining pipeline whenever new training data arrives in an S3 bucket. They also need to send a notification to a Slack channel when the retraining completes. Which TWO AWS services should they use to implement this event-driven workflow? (Select TWO.)

Accepted Answer

AWS Lambda. AWS Lambda is correct because it can be triggered directly by S3 events (e.g., s3:ObjectCreated) to invoke the model retraining pipeline. Amazon EventBridge is correct because it can capture completion events from the retraining pipeline (e.g., SageMaker training job state changes) and route them to a target like a Slack webhook via Lambda or SNS, enabling the notification workflow.

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Amazon SQS

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AWS CloudTrail

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SageMaker Model Registry

Question 11

A company is using SageMaker to serve a model for real-time predictions. They want to test a new model version by routing a small percentage of live traffic to it while the rest goes to the current model. They also need to compare performance metrics. Which TWO actions should they take? (Select TWO.)

Accepted Answer

Monitor the performance of both variants using SageMaker CloudWatch metrics. Amazon CloudWatch provides built-in metrics for SageMaker endpoints, including latency, invocation counts, and error rates, which can be monitored per production variant. This allows the company to compare the performance of the new model version against the current model in real time. Option E is correct because SageMaker endpoints support multiple production variants, and you can set an initial traffic weight (e.g., 5%) to route a small percentage of live traffic to the new model while the rest goes to the existing variant.

Answer

Deploy the new model to a separate endpoint and use Route 53 to split traffic

Answer

Compile the new model with SageMaker Neo before deployment

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Use SageMaker Batch Transform to evaluate the new model

Question 12

A company wants to deploy a containerized inference application that includes a custom pre-processing script and a TensorFlow model on SageMaker. They need the ability to independently scale the pre-processing and model serving components. Which TWO SageMaker features support this? (Select TWO.)

Accepted Answer

Multiple real-time endpoints with an Application Load Balancer. Multi-container endpoints allow running multiple containers (e.g., pre-processing and inference) on the same endpoint, simplifying deployment. Multiple real-time endpoints with an Application Load Balancer enable independent scaling by directing traffic to separate endpoint instances, each handling a specific component. Together, these features meet the requirements.

Answer

SageMaker inference pipeline

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SageMaker Batch Transform

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Multi-model endpoint

Question 13

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?

Accepted Answer

Real-time endpoint on ml.g4dn instances. Real-time endpoints on GPU instances (ml.g4dn) provide low latency and GPU acceleration, ideal for high-traffic, latency-sensitive workloads.

Answer

Real-time endpoint on ml.m5 instances

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Batch Transform

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Serverless Inference

Question 14

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?

Accepted Answer

Use a single multi-model endpoint (MME). Multi-model endpoints (MME) allow hosting multiple models on a single endpoint, sharing instances and reducing costs, especially for infrequently used models.

Answer

Use SageMaker Serverless Inference for each model

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Deploy each model on a separate real-time endpoint

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Use Batch Transform for all models

Question 15

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?

Accepted Answer

Pipeline caching. SageMaker Pipelines caching stores step outputs; if the step configuration and inputs are identical, the pipeline reuses the cached output, skipping execution.

Answer

Pipeline variable expressions

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Model registry approval

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Step parallelism