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

Question 1

An engineer runs: aws sagemaker describe-endpoint --endpoint-name my-endpoint and receives the exhibit output. The engineer wants to update the endpoint to use a new model version stored in ECR with tag ':2'. Which step is necessary to perform the update?

Accepted Answer

Create a new endpoint configuration (my-endpoint-config-v2) referencing the new image, then call update-endpoint with the new config name.. Option A is correct because SageMaker endpoints are immutable with respect to their configuration; you cannot modify an existing endpoint configuration in place. To update an endpoint to use a new model version, you must create a new endpoint configuration (e.g., my-endpoint-config-v2) that points to the new ECR image tag ':2', then call update-endpoint with the new configuration name. This triggers a zero-downtime deployment where SageMaker gradually shifts traffic to the new variant.

Answer

Modify the existing endpoint configuration (my-endpoint-config-v1) to use the new image, then update the endpoint.

Answer

Use the update-endpoint command directly with the new image ARN.

Answer

Delete the endpoint and recreate it with the new model image.

Question 2

A SageMaker endpoint is failing with the exhibited error. What is the most likely cause of this error?

Accepted Answer

The model archive uploaded to S3 does not contain the 'classes.txt' file.. The error indicates that 'classes.txt' is missing from /opt/ml/model. Most likely, the file was not included in the model archive or the archive was not extracted properly.

Answer

The Docker container does not have the necessary IAM role to read the model artifacts.

Answer

The inference script is referencing the wrong path for the model directory.

Answer

The SageMaker endpoint does not have internet access to download the model from S3.

Question 3

An ML engineer runs the CLI command shown in the exhibit. However, the training job fails immediately with an error: 'Unable to assume role'. What is the most likely cause?

Accepted Answer

The IAM role's trust policy does not grant SageMaker permission to assume the role.. The 'Unable to assume role' error indicates that SageMaker cannot assume the IAM role specified in the CLI command. This is a trust policy issue: the role's trust policy must include SageMaker as a trusted service (i.e., `"Service": "sagemaker.amazonaws.com"`). Without this, SageMaker is not authorized to assume the role, regardless of the role's permissions.

Answer

The IAM role 'SageMakerExecutionRole' does not have permission to create the training job.

Answer

The training image in ECR does not exist.

Answer

The S3 bucket 'my-bucket' does not exist.

Question 4

A data science team has trained a model using SageMaker and wants to deploy it to a production endpoint with automatic scaling based on request volume. Which SageMaker feature should they use to configure scaling?

Accepted Answer

SageMaker Endpoint Autoscaling. SageMaker Endpoint Autoscaling is the correct feature because it automatically adjusts the number of instances behind a SageMaker hosted endpoint based on a target metric (e.g., requests per minute, CPU utilization) using Application Auto Scaling. This allows the endpoint to handle varying request volumes without manual intervention, ensuring cost efficiency and performance.

Answer

SageMaker Debugger

Answer

SageMaker Model Registry

Answer

SageMaker Pipelines

Question 5

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 6

An ML team uses SageMaker Pipelines to automate retraining. After a pipeline failure, they need to reprocess only the failed step without rerunning the entire pipeline. What should they do?

Accepted Answer

Use SageMaker Pipelines Cache with step-level caching.. SageMaker Pipelines Cache with step-level caching allows you to reuse outputs from previous successful runs of unchanged steps. When a pipeline fails, only the failed step and any downstream steps that depend on it need to be re-executed, because cached results from prior successful steps are automatically retrieved. This avoids rerunning the entire pipeline, saving time and compute resources.

Answer

Create a new pipeline version for each run.

Answer

Use SageMaker Model Monitor to detect drift and trigger retraining.

Answer

Manually rerun the pipeline with updated parameters.

Question 7

A company wants to deploy a PyTorch model on SageMaker for real-time inference. Which two steps are required? (Select TWO.)

Accepted Answer

Package the model artifacts into a tar.gz file.. Option C is correct because SageMaker requires model artifacts to be packaged as a single tar.gz file (containing the model weights, serialized PyTorch model, and any dependencies) for deployment. This compressed archive is uploaded to S3 and referenced when creating the model object for real-time inference.

Answer

Upload the training data to an S3 bucket.

Answer

Register the model in the SageMaker Model Registry.

Answer

Set up a SageMaker Notebook instance.

Question 8

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 9

An organization is deploying a large language model on SageMaker and needs to optimize inference costs while maintaining low latency. Which three strategies should they consider? (Select THREE.)

Accepted Answer

Use SageMaker Inference Recommender to find optimal instance and configuration.. A is correct because SageMaker Inference Recommender runs load tests against your model to recommend the most cost-effective instance type and configuration (e.g., instance count, container parameters) that meets your latency and throughput requirements. This eliminates guesswork and ensures you are not over-provisioning or under-provisioning resources, directly optimizing inference costs.

Answer

Enable SageMaker Model Parallelism for inference.

Answer

Use SageMaker Batch Transform for real-time requests.

Question 10

A team used the above config to create an endpoint. However, the endpoint fails to invoke because of a "ModelError". What is the most likely cause?

Accepted Answer

The IAM role does not have permission to access the S3 bucket.. The most likely cause of a ModelError when invoking a SageMaker endpoint is that the IAM role associated with the endpoint does not have the necessary permissions to access the S3 bucket containing the model artifacts. SageMaker downloads the model data from S3 during endpoint creation, and if the role lacks s3:GetObject permission on the bucket, the model fails to load, resulting in a ModelError.

Answer

The instance type is not available in the region.

Answer

The model data URL points to a non-existent file.

Answer

The ECR image URI is incorrect for the region.

Question 11

A data scientist runs this pipeline but the Train step fails with "ResourceLimitExceeded". What is the most likely cause?

Accepted Answer

The account has a limit of 0 for ml.p3.2xlarge instances.. The 'ResourceLimitExceeded' error indicates that the requested instance type (ml.p3.2xlarge) exceeds the account's service quota for that specific instance family. In AWS SageMaker, each account has a default limit of 0 for certain GPU instance types like ml.p3.2xlarge unless a quota increase has been requested and approved. This error occurs at the Train step because SageMaker attempts to launch the training job with an instance type that is not allowed by the current quota.

Answer

The volume size is too small for training.

Answer

The Preprocess step did not complete successfully.

Answer

The training image is not accessible.

Question 12

During deployment of a Hugging Face model, the endpoint logs show this error. Which step was likely missed?

Accepted Answer

The inference container does not include the transformers library; the team should use a pre-built Hugging Face container.. The error indicates that the inference container cannot find the `transformers` library, which is required to load and run the Hugging Face model. By using a pre-built Hugging Face container from AWS, the team ensures that all necessary dependencies (like `transformers`, `tokenizers`, and `torch`) are pre-installed and compatible with the SageMaker inference environment. Option A is correct because the most likely missed step was selecting a generic container instead of the purpose-built Hugging Face container.

Answer

The IAM role does not have permissions to download additional libraries.

Answer

The model artifact was not packaged correctly; the inference script is missing.

Answer

The endpoint configuration specifies the wrong instance type.

Question 13

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 14

A team has a large number of models that need to be deployed for batch inference weekly. They want to minimize cost and management overhead. Which approach is MOST efficient?

Accepted Answer

Use SageMaker Batch Transform with separate jobs for each model.. SageMaker Batch Transform is the most efficient approach for weekly batch inference because it automatically provisions and terminates compute resources for each job, minimizing cost and management overhead. Running separate jobs for each model allows independent scaling and avoids the complexity of managing persistent endpoints or multi-model hosting for batch workloads.

Answer

Use SageMaker Pipelines to run inference as part of the pipeline.

Answer

Create a single SageMaker endpoint for all models and update the model periodically.

Answer

Deploy each model to a separate SageMaker endpoint and delete after use.

Question 15

An ML team uses SageMaker Model Registry to manage model versions. They want to automatically deploy a model to a staging endpoint when a new version is approved. Which AWS service can orchestrate this?

Accepted Answer

AWS Step Functions. AWS Step Functions is the correct choice because it can orchestrate a workflow that triggers on a Model Registry event (e.g., model version approval) and then deploys the model to a staging endpoint using SageMaker SDK calls. Step Functions provides built-in integration with SageMaker via service integrations, allowing you to chain approval checks, model creation, and endpoint deployment without custom code.

Answer

Amazon EventBridge

Answer

AWS Lambda

Answer

SageMaker Pipelines

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

Exhibit

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

Exhibit