MLA-C01 Practice Test 34 — 15 Questions

Question 1

A company is deploying a multi-model endpoint using SageMaker to serve multiple models from a single endpoint. They notice that one model consumes excessive memory and impacts others. What is the BEST practice to isolate resource usage?

Accepted Answer

Use separate endpoints for each model.. Option B is correct because using separate endpoints for each model ensures complete resource isolation at the instance level. When one model consumes excessive memory, it cannot impact others because each model runs on its own dedicated endpoint with its own compute resources. This is the best practice for isolating resource usage in production environments where memory-intensive models are deployed.

Answer

Configure instance type with more memory.

Answer

Use SageMaker Model Parallelism.

Answer

Use multi-model endpoint with model cache size limit.

Question 2

A company uses Amazon SageMaker Pipelines to automate its ML workflow. The pipeline includes a training step and a model evaluation step. If the evaluation step fails, the pipeline should stop and notify the team. How should the company configure the pipeline?

Accepted Answer

Define a ConditionStep that checks the evaluation metric and fail the pipeline if the metric is below a threshold.. Option A is correct because SageMaker Pipelines natively supports a ConditionStep that can evaluate a metric (e.g., model accuracy) and branch the pipeline execution. By configuring the ConditionStep to check if the evaluation metric falls below a threshold, you can explicitly fail the pipeline and trigger a notification (e.g., via SNS) when the condition is not met. This is the idiomatic, pipeline-native way to halt execution on evaluation failure without external dependencies.

Answer

Use Amazon SageMaker Model Monitor to detect failures in the evaluation step.

Answer

Create an AWS Step Function state machine that monitors the pipeline and stops it on failure.

Answer

Configure an Amazon CloudWatch alarm on the evaluation step's execution time to stop the pipeline.

Question 3

An ML team is using SageMaker Model Registry to manage model versions. After training a new model version, they register it with an 'Approved' status. The CI/CD pipeline automatically deploys the latest approved model to a staging endpoint. However, the pipeline fails with an error: 'Cannot deploy model because the model version is not approved.' The model version is clearly approved in the registry. What is the most likely cause?

Accepted Answer

The approval status was set on the model package group, not on the specific model version.. Option D is correct because in SageMaker Model Registry, approval is a property of a specific model version within a model package group, not of the model package group itself. The error indicates the pipeline is likely referencing the model package group ARN or a version that lacks explicit approval, even though the team believes the model is approved. The CI/CD pipeline must use the exact model version ARN that has the 'Approved' status to deploy successfully.

Answer

The pipeline is using the model package ARN instead of the model version ARN.

Answer

The model version is approved but the pipeline uses a different version that is still pending.

Answer

The SageMaker endpoint configuration does not have the necessary IAM permissions to read the registry.

Question 4

A machine learning team is building a CI/CD pipeline to train and deploy models using Amazon SageMaker. They want to ensure that the deployment step only proceeds if the model evaluation metrics exceed a certain threshold. Which THREE components should the team include in the pipeline? (Choose THREE.)

Accepted Answer

The SageMaker Model Registry to approve and store the model after evaluation.. Option C is correct because the SageMaker Model Registry is the central component for approving and storing model versions after evaluation. It enables governance by allowing you to set approval statuses (e.g., Approved, Rejected) and track model lineage, ensuring only validated models proceed to deployment.

Answer

An AWS Lambda function for manual approval.

Answer

An AWS CodeBuild project to compile the model artifacts.

Question 5

A company uses SageMaker to orchestrate a training pipeline with multiple steps including preprocessing, training, and evaluation. They want to ensure that each step can be reused and tracked. Which three SageMaker features support this? (Select THREE.)

Accepted Answer

SageMaker Pipelines. SageMaker Pipelines is correct because it provides a directed acyclic graph (DAG) of steps that can be defined, parameterized, and reused across different runs. Each step (preprocessing, training, evaluation) is a distinct, versioned component that can be independently tracked and re-executed, enabling modular orchestration of ML workflows.

Answer

SageMaker Clarify

Answer

SageMaker Model Monitor

Question 6

A company wants to use SageMaker to deploy a model that requires GPU acceleration for inference but also needs to keep costs low when traffic is low. Which SageMaker feature should they use?

Accepted Answer

SageMaker Elastic Inference. SageMaker Elastic Inference (EI) allows you to attach a fraction of a GPU to a SageMaker endpoint for inference, providing GPU acceleration at a lower cost than using a full GPU instance. This is ideal for scenarios with variable traffic because you can scale the EI accelerator independently of the instance, and pay only for the accelerator when it's used, keeping costs low during low-traffic periods.

Answer

SageMaker Debugger

Answer

SageMaker Managed Spot Training

Answer

SageMaker Model Monitor

Question 7

A company wants to use SageMaker to serve real-time predictions with a model that has a large memory footprint. They need to ensure the endpoint can handle traffic spikes. Which scaling policy should they use?

Accepted Answer

Target tracking policy. Target tracking scaling policy is the correct choice because it automatically adjusts the number of instances in the SageMaker endpoint based on a target metric, such as InvocationsPerInstance or ModelLatency, to handle traffic spikes without manual intervention. This policy is ideal for real-time inference with large memory models because it dynamically scales resources up or down to maintain the target metric, ensuring consistent performance during unpredictable traffic bursts.

Answer

Simple scaling policy

Answer

Scheduled scaling policy

Answer

Step scaling policy

Question 8

A data science team wants to deploy a real-time inference endpoint on Amazon SageMaker for a model that requires low latency (under 100 ms). The model is a small ensemble of three tree-based models, each about 50 MB. The team expects around 1000 requests per minute, with occasional spikes to 5000 requests per minute. Which instance type and deployment strategy would be MOST cost-effective while meeting the latency requirement?

Accepted Answer

Deploy a single model endpoint on an ml.c5.large instance with Auto Scaling configured using a target tracking policy based on invocations per minute. Option A is correct because deploying a single model endpoint on an ml.c5.large instance with Auto Scaling based on invocations per minute provides the necessary compute capacity for the expected 1000 requests per minute while scaling up to handle spikes up to 5000 requests per minute. The ml.c5.large instance offers sufficient memory (4 GB) and compute for three 50 MB tree-based models, and the target tracking policy ensures low latency by maintaining a buffer of capacity without over-provisioning, keeping inference under 100 ms.

Answer

Deploy a single model endpoint on an ml.c5.large instance with a Multi-Model endpoint

Answer

Use SageMaker batch transform with multiple ml.c5.large instances to process all requests offline

Answer

Deploy a single model endpoint on an ml.c5.xlarge instance with provisioned concurrency

Question 9

A company uses SageMaker endpoint with production variants for canary deployments. The team wants to gradually shift traffic from the old model variant (variant A) to the new model variant (variant B) over a period of 10 minutes. After the shift, if the new variant's error rate increases by more than 5%, they want to roll back automatically. Which solution meets these requirements with minimal manual intervention?

Accepted Answer

Use the SageMaker UpdateEndpoint API with a linear traffic shift from variant A to variant B over 10 minutes, and configure a CloudWatch alarm on the new variant's error rate that triggers a Lambda function to revert the traffic weights.. Option C is correct because the SageMaker UpdateEndpoint API supports a linear traffic shift between production variants, allowing you to gradually route traffic from variant A to variant B over a specified time period (here, 10 minutes). By attaching a CloudWatch alarm on the new variant's error rate that triggers a Lambda function to revert the traffic weights, you achieve automatic rollback with minimal manual intervention when the error rate exceeds the 5% threshold.

Answer

Use AWS Cloud Map to register the new variant and perform a slow rollout.

Answer

Deploy variant B as a separate endpoint and use Route 53 weighted routing to shift traffic.

Answer

Use AWS CodeDeploy with a deployment group to shift traffic and automatically roll back if CloudWatch alarms trigger.

Question 10

A company has a SageMaker endpoint that was deployed successfully and is in service. However, when the team sends test inferences using the InvokeEndpoint API, they receive a 500 internal server error. The endpoint logs in CloudWatch show a stack trace indicating 'OutOfMemoryError: Java heap space'. The model is a large XGBoost model (2 GB) and the endpoint is using an ml.m5.large instance with 8 GB of memory. What is the MOST likely cause and solution?

Accepted Answer

The instance type has insufficient memory for the model size; use a larger instance type like ml.m5.xlarge (16 GB) or ml.m5.2xlarge.. The OutOfMemoryError in Java heap space indicates that the model (2 GB) plus the runtime overhead of the XGBoost container and Java-based inference code exceed the available memory on the ml.m5.large instance (8 GB total, but not all is available for the Java heap). The most direct fix is to use a larger instance type, such as ml.m5.xlarge (16 GB) or ml.m5.2xlarge, to provide sufficient heap space for the model and inference operations.

Answer

The endpoint needs to have a smaller batch size configured in the real-time inference request.

Answer

The model is a Transformer model and requires a GPU instance; use ml.g4dn.xlarge instead.

Answer

The SageMaker container is not compatible with XGBoost; switch to a framework container.

Question 11

A data scientist needs to version and manage multiple models for a team of five. The team frequently experiments with different algorithms and hyperparameters. They need a centralized registry to store, deploy, and compare model versions. Which AWS service should the data scientist use?

Accepted Answer

Use Amazon SageMaker Model Registry.. Amazon SageMaker Model Registry is the correct choice because it provides a centralized repository specifically designed for cataloging, versioning, approving, and deploying machine learning models. It integrates natively with SageMaker pipelines and endpoints, enabling the team to compare model versions, manage metadata (e.g., hyperparameters, metrics), and promote models through stages (e.g., from staging to production) with approval workflows.

Answer

Store each model artifact in Amazon S3 with manual versioning in the key name.

Answer

Use AWS Config to track model version changes.

Answer

Use AWS CodeArtifact to store model packages.

Question 12

A company is running multiple SageMaker endpoints for different models, each serving a separate business unit. The total cost is growing rapidly. The ML engineering team wants to reduce costs without sacrificing performance or isolation. They are considering either consolidating models into a Multi-Model Endpoint (MME) or onto a Multi-Container Endpoint (MCE). The models vary in size from 100 MB to 5 GB, and traffic patterns are unpredictable. Which recommendation is MOST appropriate?

Accepted Answer

Use a Multi-Model Endpoint with a single large instance type to host all models, and enable SageMaker inference pipelines if pre-processing is needed.. A Multi-Model Endpoint (MME) is the most appropriate choice because it allows hosting multiple models on a single instance while keeping them isolated in separate memory spaces, which reduces cost by sharing the underlying infrastructure. MME dynamically loads and unloads models based on traffic, making it ideal for unpredictable patterns and model sizes ranging from 100 MB to 5 GB. Inference pipelines can be added for pre-processing without breaking the multi-model architecture, preserving performance and isolation.

Answer

Use Multi-Container Endpoints to deploy multiple models on a single instance.

Answer

Migrate all models to AWS Lambda functions for serverless inference.

Answer

Keep individual endpoints but switch to Graviton-based instances for cost savings.

Question 13

A data scientist is training a binary classification model using a dataset that has a severe class imbalance (90% negative, 10% positive). Which technique should be used to address the imbalance during model training?

Accepted Answer

Apply random oversampling of the minority class. Random oversampling of the minority class (Option C) directly addresses class imbalance by duplicating or synthesizing examples from the positive class, which balances the training distribution and prevents the model from becoming biased toward the majority class. This technique is specifically designed to mitigate the skewed gradient updates that occur when the minority class is underrepresented, leading to better recall and precision for the positive class in binary classification tasks.

Answer

Use a larger batch size

Answer

Use L2 regularization

Answer

Increase the learning rate

Question 14

A company uses SageMaker Pipelines to automate model retraining. The pipeline runs daily but sometimes fails due to data quality issues. What is the best design to handle this?

Accepted Answer

Add a data quality check step with Conditional to skip training if data fails.. Option A is correct because SageMaker Pipelines supports a data quality check step that can be integrated with a ConditionStep. If the data quality check fails, the ConditionStep can skip the training step entirely, preventing the pipeline from failing due to bad data. This design ensures the pipeline completes successfully (or exits gracefully) without wasting compute resources on training with invalid data.

Answer

Use SageMaker Debugger to monitor training.

Answer

Use SageMaker Model Registry to track model versions.

Answer

Increase the instance size for the training step.

Question 15

A company uses Amazon SageMaker to train and deploy machine learning models. They need to run batch predictions on 10 TB of data stored in Amazon S3 every night. The model is a PyTorch neural network that fits in GPU memory. The predictions are not time-sensitive, but the job must complete within 8 hours. Which approach would be the MOST cost-effective?

Accepted Answer

Use SageMaker batch transform with a single ml.p3.2xlarge instance.. Option D is the most cost-effective because SageMaker batch transform with a single ml.p3.2xlarge instance provides GPU acceleration for the PyTorch neural network, which fits in GPU memory, and can process 10 TB of data within 8 hours. Batch transform automatically handles data partitioning and inference, eliminating the need for custom orchestration, and the single instance avoids the overhead and cost of multiple instances. The ml.p3.2xlarge offers a balance of GPU compute and cost, making it ideal for non-time-sensitive nightly batch jobs.

Answer

Use SageMaker processing job with a script to load the model and run inference.

Answer

Create a real-time endpoint and send all data as a large batch.

Answer

Use multiple ml.c5.4xlarge instances in a batch transform job with custom partitioning.