MLA-C01 Deployment and Orchestration of ML Workflows Practice Test 5 — 15 Questions

Question 1

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?

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

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.

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SageMaker Pipelines native S3 trigger

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AWS Step Functions

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Amazon CloudWatch Logs

Question 2

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 batch inference on large datasets stored in S3, processing data in chunks and writing results to S3. It is cost-effective for non-real-time scenarios. Real-time endpoints are for low-latency inference. Serverless is for on-demand, not batch. Asynchronous is for near-real-time with S3 input/output but still not ideal for large batch jobs.

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SageMaker real-time endpoint

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SageMaker Asynchronous Inference

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SageMaker Serverless Inference

Question 3

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.

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SageMaker Serverless Inference with a custom container

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SageMaker Multi-Model Endpoint with a custom container

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SageMaker Asynchronous Inference with a custom container

Question 4

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.

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

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Use the CreatePipelineExecution API with a flag to ignore failures

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

Question 5

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. Production variants with traffic splitting allow routing a percentage of inference requests to different model versions. By updating the initial variant weights, the data scientist can gradually shift traffic from V1 to V2. Shadow testing mirrors traffic but does not affect real responses. Blue/green is a deployment pattern but not a SageMaker feature for gradual traffic splitting. Canary deployments are a pattern but SageMaker implements it via production variants.

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SageMaker shadow testing

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SageMaker blue/green deployment

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SageMaker canary deployment

Question 6

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.

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AWS Step Functions

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Amazon Elastic Inference

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SageMaker Triton Inference Server

Question 7

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.

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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 8

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). Multi-Model Endpoint (MME) allows hosting multiple models on the same instance, sharing resources. Since the models are small, MME is cost-effective. Real-time endpoints would require separate instances. Serverless is for on-demand but may incur cold starts. Asynchronous is for batch-like workloads.

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SageMaker Serverless Inference

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SageMaker Asynchronous Inference

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SageMaker real-time endpoint with one instance per model

Question 9

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. A solution: Amazon EventBridge detects S3 events (new data), invokes a Lambda function that checks model performance (e.g., via SageMaker Model Monitor or custom metrics), and then starts a Step Functions workflow if degradation is detected. The other services: SageMaker Pipelines could replace Step Functions but is not listed as an option; SageMaker Model Monitor can track performance but is not an event source; CloudWatch Logs is not directly involved in the trigger logic.

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Amazon CloudWatch Logs

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SageMaker Model Registry

Question 10

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.

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Serverless inference

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Batch transform

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Asynchronous inference endpoint

Question 11

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. Multi-model endpoints (MME) allow hosting multiple models on a single endpoint, loading models dynamically based on the target model in the request. This reduces cost for many small, infrequently used models by sharing the underlying instance.

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Multi-container endpoint

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Batch transform job

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Real-time endpoint with 50 production variants

Question 12

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?

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 13

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 uses production variants with weighted traffic allocation. By setting the new model variant to 5% and the current to 95%, and later adjusting weights, the team can gradually shift traffic. Blue/green is a full switch, and shadow testing duplicates traffic without affecting live responses.

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Shadow testing

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Blue/green deployment

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A/B testing with production variants

Question 14

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 15

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