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

Question 1

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

Answer

Create a separate real-time endpoint for each model

Answer

Use a multi-container endpoint

Question 2

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 3

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. Option C is correct because 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 4

A company needs to serve real-time predictions from a large ensemble of three deep learning models, each requiring different inference environments (PyTorch, TensorFlow, MXNet). Which SageMaker endpoint type supports running multiple inference containers together?

Accepted Answer

Multi-container endpoint. Option C is correct because Amazon SageMaker multi-container endpoints allow you to run multiple inference containers (e.g., PyTorch, TensorFlow, MXNet) within a single endpoint, each handling different models or inference environments. This is achieved by deploying multiple containers behind a single endpoint with a serial or direct invocation pattern, enabling real-time predictions from the ensemble without managing separate endpoints.

Answer

Multi-model endpoint

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Real-time endpoint with a single container

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Asynchronous endpoint

Question 5

A data scientist wants to train a model on SageMaker using a custom PyTorch script, then register the best model in the SageMaker Model Registry. The training job is part of a SageMaker Pipeline. Which pipeline step should be used to register the model?

Accepted Answer

RegisterModelStep. The `RegisterModelStep` is specifically designed to create a model resource and register it in the SageMaker Model Registry as part of a pipeline. It takes the training output (e.g., model artifacts from a `TrainingStep`) and packages it with the specified inference image and metadata, then creates a model package group version. This is the correct step for registering a model after training, as it directly integrates with the Model Registry for versioning and approval workflows.

Answer

CreateModelStep

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TrainingStep

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TransformStep

Question 6

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 7

A company uses SageMaker Neo to compile a trained model for deployment on edge devices. What is the primary benefit of using Neo?

Accepted Answer

It reduces model size and improves inference speed on target hardware. SageMaker Neo optimizes models for specific hardware architectures (e.g., ARM, Intel, NVIDIA) to achieve faster inference and lower memory footprint.

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It monitors model drift in production

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It automatically retrains the model on new data

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It provides a serverless inference endpoint

Question 8

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)

Answer

AWS Glue workflows

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AWS Step Functions with Lambda functions for each step

Question 9

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 10

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

Answer

Configure SCPs to block deployment of unapproved models

Question 11

A team uses MLflow on SageMaker for experiment tracking. They want to automate the retraining of a model when new training data arrives in an S3 bucket. Which combination of services should they use?

Accepted Answer

EventBridge -> Lambda -> SageMaker Training Job. EventBridge can detect S3 PutObject events and trigger a Lambda function that starts a SageMaker training job, possibly using MLflow for tracking.

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SageMaker Pipelines scheduled trigger every hour

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S3 Event Notifications -> SQS -> SageMaker Training Job

Answer

AWS Step Functions with S3 poller

Question 12

A company wants to deploy a model using a serverless inference endpoint that can automatically scale to zero when not in use and has a configurable maximum concurrency. Which SageMaker inference option meets these requirements?

Accepted Answer

Serverless inference. SageMaker Serverless Inference is the correct choice because it automatically scales to zero when the endpoint is idle, eliminating costs during periods of no traffic, and it allows you to configure a maximum concurrency limit per endpoint to control throughput. This fully managed, pay-per-invoke option is designed for workloads with intermittent or unpredictable traffic patterns, meeting both requirements precisely.

Answer

Real-time endpoint with auto-scaling

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Batch transform

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Asynchronous inference

Question 13

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

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Use a larger instance type with more vCPUs

Question 14

A team wants to deploy a new model using a canary deployment strategy on SageMaker. Which TWO configurations are necessary? (Choose two.)

Accepted Answer

Set the initial traffic distribution (e.g., 90% old, 10% new). A canary deployment requires two production variants (old and new) with traffic distribution. Gradual traffic shifting can be achieved by updating the endpoint's variant weights over time.

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Create a CloudWatch alarm to automatically rollback

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Enable data capture on the endpoint

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Use SageMaker Model Registry to approve the new model

Question 15

A company uses SageMaker Pipelines to automate their ML workflow. They want to ensure that pipeline steps are not re-executed if the inputs and parameters have not changed since the last successful run. Which THREE features can help achieve this? (Choose three.)

Accepted Answer

Use a ConditionStep to skip steps if data is unchanged. Pipeline caching reuses step outputs when inputs/parameters are identical. SageMaker Experiments lineage can track previous run metadata. Using a ConditionStep with checksums can skip steps based on data content.

Answer

Deploy a real-time endpoint for data validation

Answer

Use SageMaker Model Monitor