MLA-C01 Deployment and Orchestration of ML Workflows Practice Test 3 — 15 Questions

Question 1

A company is deploying a machine learning model using SageMaker hosting. They need to support multiple versions of the model for A/B testing. Which TWO actions are required to set up the A/B test? (Choose two.)

Accepted Answer

Configure the endpoint to route a percentage of traffic to each variant using initial variant weight. Option C is correct because SageMaker endpoints use `initial variant weight` to distribute traffic among production variants. By setting this weight, you can route a specific percentage of inference requests to each model version, enabling A/B testing without changing the endpoint configuration.

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Enable shadow variants to capture traffic for the new model without affecting users

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Set up a batch transform job to compare performance offline

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Register both models in SageMaker Model Registry

Question 2

A company is using an AWS Step Functions state machine to orchestrate a multi-step ML deployment. The workflow includes: training a model, evaluating it, registering the model, and deploying to a staging endpoint. They need to implement an approval gate before deploying to production. Which THREE components are necessary to achieve this? (Choose three.)

Accepted Answer

A task in the state machine that pauses and waits for manual approval via SNS or Lambda. Option B is correct because Step Functions can use a task with a callback pattern (`.waitForTaskToken`) to pause the workflow and wait for external manual approval. When combined with an SNS topic or Lambda function that sends a task success or failure signal back to Step Functions, this creates a reliable approval gate. This pattern allows the state machine to halt execution until a human approves or rejects the deployment, which is essential for production deployment control.

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An AWS CodePipeline pipeline with approval stage

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An Amazon SNS topic for notification of approval status

Question 3

A company wants to deploy a model on SageMaker serverless inference. Which TWO of the following are limitations of serverless endpoints compared to real-time endpoints? (Choose two.)

Accepted Answer

No support for GPU instances. Option C is correct because SageMaker serverless inference does not support GPU instances; it only runs on CPU-based instances. This is a fundamental limitation for workloads requiring GPU acceleration, such as deep learning models. In contrast, real-time endpoints support both CPU and GPU instance types.

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Cold starts can cause increased latency for infrequent requests

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Cannot deploy multiple containers in the same endpoint

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No automatic scaling – must be configured manually

Question 4

A company trained a model using SageMaker and wants to deploy it with low latency for real-time inference. Which SageMaker feature is MOST suitable?

Accepted Answer

SageMaker Real-Time Endpoint. SageMaker Real-Time Endpoint is the most suitable feature for low-latency real-time inference because it provisions dedicated, persistent instances that respond to requests synchronously with predictable latency. This option directly meets the requirement for serving individual predictions with minimal delay, unlike batch or serverless alternatives that introduce higher latency or are designed for asynchronous processing.

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SageMaker Endpoint with Auto Scaling

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SageMaker Serverless Inference

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SageMaker Batch Transform

Question 5

A data scientist wants to automate retraining of a model weekly and deploy the new model automatically after passing validation. Which AWS service combination is best?

Accepted Answer

SageMaker Pipelines + AWS Step Functions. SageMaker Pipelines orchestrates the ML workflow including training and validation, and Step Functions can trigger deployment. SageMaker alone lacks native scheduling, and Lambda cannot orchestrate complex workflows.

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Amazon EventBridge + SageMaker training job

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Amazon SageMaker Autopilot

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AWS Lambda + SageMaker training job

Question 6

A company has a model that receives low traffic but needs to handle sudden spikes. Which deployment option is most cost-effective?

Accepted Answer

SageMaker Serverless Inference. SageMaker Serverless Inference is the most cost-effective option for low-traffic models with sudden spikes because it automatically scales to zero when not in use and scales up instantly to handle bursts, charging only for the compute time consumed per inference request. This eliminates the cost of idle provisioned infrastructure, making it ideal for unpredictable or intermittent traffic patterns.

Answer

SageMaker Real-Time Endpoint with Auto Scaling

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SageMaker Multi-Model Endpoint

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SageMaker Batch Transform

Question 7

A team notices that inference requests to their SageMaker endpoint are failing with '504 Gateway Timeout' for large payloads. What change should be made?

Accepted Answer

Increase the endpoint's invocation timeout. A 504 Gateway Timeout indicates that the SageMaker endpoint's invocation timeout (default 60 seconds) was exceeded while processing a large payload. Increasing the invocation timeout allows the endpoint more time to complete inference for large payloads, resolving the timeout error.

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Enable data capture on the endpoint

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Deploy a shadow endpoint for testing

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Switch to a multi-model endpoint

Question 8

A company is using SageMaker Model Registry to manage model versions. They want to automatically deploy the latest approved model to production after retraining. Which approach is best?

Accepted Answer

Create a SageMaker Pipeline that includes a model approval step and deployment step. Option C is correct because a SageMaker Pipeline can orchestrate the entire workflow from retraining to deployment, including a model approval step that gates deployment to production only when the model is approved. This automates the process end-to-end, ensuring that only approved models are deployed, which aligns with the requirement to automatically deploy the latest approved model after retraining.

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Manually deploy the approved model using the SageMaker console

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Use AWS Lambda to update the endpoint whenever a new model version is created

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Schedule a CloudWatch Event to invoke a SageMaker update endpoint API daily

Question 9

A company is deploying multiple models on a single endpoint to reduce costs. They need to update one model without affecting others. Which solution?

Accepted Answer

Use SageMaker Multi-Model Endpoint. SageMaker Multi-Model Endpoint (MME) allows hosting multiple models on a single endpoint, sharing the underlying compute instance. When you need to update one model, you can simply upload a new model artifact (e.g., a new `model.tar.gz`) to Amazon S3, and the endpoint will automatically load the updated version on subsequent inference requests without affecting the other models currently cached or in use.

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Use multiple single-model endpoints behind an Application Load Balancer

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Use SageMaker Batch Transform for some models

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Use a SageMaker Endpoint with multiple production variants

Question 10

A company uses SageMaker endpoints with auto-scaling based on CPU utilization. During a flash sale, latency increases despite low CPU. What should be done?

Accepted Answer

Use a custom metric such as memory utilization or request count for auto-scaling. Option A is correct because CPU utilization is a poor scaling metric for inference workloads that are I/O or memory-bound. During a flash sale, increased request concurrency can cause queuing and latency spikes even when CPU is low. Using a custom metric like request count per instance or memory utilization directly reflects the load on the inference endpoint, enabling the Application Auto Scaling target tracking policy to scale out proactively before latency degrades.

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Increase the instance size

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Disable auto-scaling and use a larger instance

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Switch to GPU instances

Question 11

A company is deploying a large model (10GB) for real-time inference. The inference latency is too high. What optimization technique can help?

Accepted Answer

Use SageMaker Neo to compile the model for the target instance. SageMaker Neo compiles the model to optimize it for the target instance hardware, reducing inference latency without sacrificing accuracy. This is especially effective for large models (e.g., 10GB) where runtime performance gains come from hardware-specific optimizations like instruction set tuning and memory access pattern improvements.

Answer

Increase the endpoint's memory allocation

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Switch to a batch transform job

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Reduce the model size by quantization

Question 12

A team uses SageMaker Pipelines for CI/CD. The training step fails due to insufficient memory. How to fix without rewriting code?

Accepted Answer

Increase the instance type in the pipeline step configuration. Option C is correct because SageMaker Pipelines allows you to specify the instance type for each training step in the pipeline definition. By increasing the instance type (e.g., from ml.m5.large to ml.m5.xlarge or a memory-optimized instance like ml.r5.large), you allocate more memory to the training container without modifying the training script or algorithm. This directly resolves the out-of-memory error while preserving the existing code.

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Modify the training algorithm to use less memory

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Reduce the batch size in the training script

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Enable managed spot training

Question 13

A company wants to deploy a trained model to a SageMaker endpoint with automatic scaling based on traffic. Which TWO configurations are required? (Choose two.)

Accepted Answer

Set up an Application Auto Scaling policy. Option C is correct because Application Auto Scaling is the AWS service that automatically adjusts the number of instances for a SageMaker endpoint based on demand. You define a scaling policy (e.g., target tracking, step scaling) that tells Auto Scaling when to add or remove instances, which is essential for handling variable traffic without manual intervention.

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Use a multi-model endpoint

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Enable data capture

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Configure a lifecycle configuration

Question 14

A company uses SageMaker Pipelines for model training and wants to incorporate model evaluation before deployment into production. Which THREE components are essential? (Choose three.)

Accepted Answer

A model registry approval step. A model registry approval step is essential because it gates the deployment of a model based on its evaluation results. In SageMaker Pipelines, you register the model to the Model Registry after training, and the approval status (e.g., Approved or Rejected) determines whether downstream deployment steps execute. This ensures only models meeting quality thresholds are promoted to production.

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A batch transform step for evaluation

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A human review step

Question 15

A company is running a SageMaker endpoint serving multiple models. They need to monitor for data drift and model quality. Which THREE actions are necessary? (Choose three.)

Accepted Answer

Enable data capture on the endpoint. Option B is correct because enabling data capture on the SageMaker endpoint is a prerequisite for monitoring data drift and model quality. Data capture automatically records input requests and output responses from the endpoint, which SageMaker Model Monitor later analyzes against a baseline to detect drift. Without data capture, there is no data to compare against the baseline constraints.

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Deploy a shadow endpoint for comparison

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Use SageMaker Debugger for monitoring