MLA-C01 Practice Test 22 — 15 Questions

Question 1

A data science team wants to track the lineage of models, including datasets, training jobs, and endpoints, for reproducibility and audit. They need a solution that captures relationships between artifacts automatically during training and deployment. Which service should they use?

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

Accepted Answer

SageMaker ML Lineage Tracking. SageMaker ML Lineage Tracking automatically records the relationships between artifacts (datasets, jobs, models, endpoints) and tracks the provenance of ML workflows.

Answer

Amazon S3 object versioning

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SageMaker Experiments

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SageMaker Model Registry

Question 2

A company notices that the prediction distribution of their deployed model has shifted significantly from the training data distribution, but the input data distribution remains unchanged. Which type of drift is occurring, and what is the MOST likely cause?

Accepted Answer

Concept drift; the underlying relationship between features and target has changed. Concept drift occurs when the relationship between input features and target variable changes, causing prediction distribution shift. Data drift would be input distribution change. The scenario specifically says input distribution unchanged but predictions shifted, indicating concept drift. Possible cause: change in customer behavior or market conditions.

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Data drift; the training data is no longer representative of the current environment

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Bias drift; the model is making unfair predictions against a protected group

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Model drift; the model has degraded due to software issues

Question 3

A financial services company must deploy a SageMaker endpoint that only accepts traffic from within a VPC and encrypts all data at rest and in transit using customer-managed KMS keys. They also need to prevent inter-container traffic from being visible to other users. Which combination of settings fulfills these requirements?

Accepted Answer

Configure the endpoint with VPC-only mode, enable inter-container traffic encryption, and specify a KMS key for endpoint data encryption. VPC-only mode restricts traffic to the VPC. Inter-container traffic encryption ensures data in transit between containers is encrypted. KMS key specified in the endpoint configuration encrypts data at rest.

Answer

Deploy the endpoint in a private subnet and use SageMaker Model Monitor to detect unauthorized access

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Attach a security group that only allows inbound traffic from the VPC CIDR and enable data encryption using a KMS key

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Enable network isolation mode and use a VPC configuration with no public internet access

Question 4

A machine learning engineer is monitoring a deployed model for data drift. The input features are a mix of categorical and numerical columns. The baseline is from the training data. Which SageMaker Model Monitor feature should they enable to detect changes in the distribution of each feature over time?

Accepted Answer

Data quality monitoring. Data quality monitoring in SageMaker Model Monitor detects schema and statistical drift (including distribution changes) for input features. Model quality monitors predictions vs. ground truth, not input features.

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Bias drift monitoring

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Model quality monitoring

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Feature attribution drift monitoring

Question 5

A machine learning model is deployed on SageMaker and its predictions are used in a production application. The model's accuracy has degraded over time. What is the most likely cause?

Accepted Answer

The model experienced concept drift.. Concept drift occurs when the statistical properties of the target variable change over time, causing the model's predictions to become less accurate. In production ML systems on SageMaker, this is a common issue as real-world data distributions evolve, and the model does not automatically adapt without retraining.

Answer

The training data was not shuffled properly.

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The model was not compiled for inference.

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The endpoint instance type is too small.

Question 6

A financial services company uses SageMaker Studio. They require that all Studio traffic remains within the corporate network and that user notebooks cannot access the internet. Which TWO configurations should they implement? (Select TWO.)

Accepted Answer

Create the SageMaker Studio domain in a VPC with VPC endpoints for SageMaker and other services. To keep all traffic within the corporate network, SageMaker Studio must be configured with a VPC-only mode (no public internet access). Additionally, creating the Studio domain with a VPC and optionally using VPC endpoints for Studio and other services ensures traffic stays within AWS network. Network isolation mode applies to training jobs, not Studio. Security groups control traffic but do not prevent internet access. KMS encryption is for data at rest, not network.

Answer

Use security groups to block outbound traffic from Studio notebooks

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Enable network isolation mode for all training jobs

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Enable KMS encryption for Studio data

Question 7

A team uses SageMaker ML Lineage Tracking to capture the metadata of their ML workflow. They want to query the lineage to see which model version was trained from a specific dataset. Which Lineage Tracking entity represents the dataset?

Accepted Answer

Artifact. In SageMaker ML Lineage Tracking, datasets are represented as Artifacts. Actions represent processes like training, and Contexts group related entities.

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Association

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Action

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Context

Question 8

A security team requires that all data used by a SageMaker training job be encrypted at rest using a customer-managed KMS key. The data is stored in an S3 bucket that is already encrypted with SSE-KMS. What additional configuration is needed on the SageMaker training job?

Accepted Answer

Specify the KMS key as the VolumeKmsKeyId and OutputKmsKeyId in the training job configuration. When the input data is encrypted with a customer-managed KMS key, you must specify the same KMS key in the VolumeKmsKeyId parameter of the training job to encrypt the ML storage volume, and also set the OutputKmsKeyId for output encryption.

Answer

Enable inter-container traffic encryption

Answer

No additional configuration is required because S3 SSE-KMS automatically applies

Answer

Use network isolation mode

Question 9

A company deploys a model in a different AWS account for production. They want to allow the production account to invoke the model endpoint from a SageMaker notebook in the same account, while keeping the model in the original account. Which configuration is required?

Accepted Answer

Attach a resource policy to the SageMaker model in the model account that grants invoke permissions to the production account's IAM role. Cross-account model access requires a resource policy on the model in Account A that grants invoke permissions to Account B. The production account's execution role must also have permission to invoke the model. SageMaker Model Registry does not handle cross-account inference. VPC peering is not sufficient for IAM permissions. IAM role cross-account trust is needed but the model resource policy is also necessary.

Answer

Create an IAM role in the production account with cross-account trust to assume a role in the model account

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Use SageMaker Model Registry to share the model across accounts

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Set up VPC peering between the two accounts and use private DNS

Question 10

A company wants to reduce costs for a SageMaker real-time endpoint that has variable traffic. Which feature allows the endpoint to automatically adjust instance count based on demand?

Accepted Answer

Auto Scaling for SageMaker endpoints. Application Auto Scaling for SageMaker endpoints allows dynamic adjustment of instance count based on CloudWatch metrics such as CPU utilization or invocations per instance.

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SageMaker Savings Plans

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SageMaker Inference Recommender

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SageMaker Model Monitor

Question 11

A data science team uses SageMaker to train models. They need to track the lineage of each model, including the dataset used, training job, and hyperparameters. Which TWO SageMaker features can they use together? (Select TWO.)

Accepted Answer

SageMaker SDK. SageMaker ML Lineage Tracking captures the relationships between artifacts, actions, and contexts. SageMaker SDK automates lineage tracking when using the SDK for training jobs. SageMaker Experiments can also be used to track runs, but lineage tracking is specifically for provenance.

Answer

SageMaker Model Registry

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SageMaker Pipelines

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SageMaker Experiments

Question 12

A team wants to monitor the number of requests and latency of their SageMaker endpoint using a unified dashboard. Which AWS service should they use to create a custom dashboard with these metrics?

Accepted Answer

Amazon CloudWatch Dashboards. Amazon CloudWatch Dashboards allow you to create custom views of metrics from any source, including SageMaker endpoint metrics like Invocations and Latency. SageMaker itself does not provide a dashboard for these metrics.

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AWS CloudTrail

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AWS Config

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SageMaker Studio

Question 13

A company uses SageMaker endpoints for real-time inference. They want to automatically scale the number of instances based on the number of outstanding requests. Which auto-scaling policy type should they choose?

Accepted Answer

Target tracking scaling. Target tracking scaling adjusts the instance count to maintain a target metric value (e.g., average invocation count per instance). Step scaling uses predefined scaling adjustments based on alarm breaches but does not directly track a target. Simple scaling is not recommended for production. Scheduled scaling is for predictable patterns, not dynamic.

Answer

Scheduled scaling

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Step scaling

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Simple scaling

Question 14

An ML engineer wants to be notified when the average inference latency of a SageMaker endpoint exceeds 500 ms for 2 consecutive evaluation periods. Which AWS service combination should they use?

Accepted Answer

CloudWatch Alarm + SNS. CloudWatch collects the 'Latency' metric from SageMaker endpoints. A CloudWatch Alarm can be set on this metric with a threshold of 500 ms for 2 consecutive periods. SNS then sends notification (email, SMS, etc.). Lambda is for automated remediation, not required for notification only.

Answer

SageMaker Model Monitor + SNS

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EventBridge + Lambda

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SageMaker Clarify + SNS

Question 15

A team has deployed a real-time inference endpoint and wants to automatically scale based on CPU utilization. Which scaling policy type should they use with Application Auto Scaling for SageMaker endpoints?

Accepted Answer

Target tracking scaling. Target tracking scaling adjusts the number of instances based on a target metric value (e.g., CPU utilization at 50%). Step scaling uses step adjustments, and simple scaling is deprecated. Predictive scaling is not supported for SageMaker endpoints.

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Step scaling

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Predictive scaling

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Simple scaling