MLA-C01 Practice Test 47 — 15 Questions

Question 1

A machine learning team is deploying a fraud detection model using SageMaker. They use the SageMaker Model Registry to track model versions. They want to automatically deploy the latest approved model to a production endpoint whenever a new model version is approved. The team uses a CI/CD pipeline with AWS CodePipeline. The pipeline currently includes a source stage (S3), a build stage (CodeBuild), and a deploy stage (manual approval). They want to automate the deployment of approved models. Which solution will meet these requirements with the least operational overhead?

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

Accepted Answer

Configure an EventBridge rule to trigger a CodePipeline execution when the model approval status changes.. It directly integrates SageMaker Model Registry approval events with CodePipeline via EventBridge, enabling fully automated deployment of the latest approved model to a production endpoint with minimal operational overhead. This approach avoids custom code or additional pipeline stages, leveraging native AWS event-driven architecture to trigger the pipeline only when a model version is approved.

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Add a custom action to CodePipeline that uses a SageMaker deployment step.

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Create a Lambda function that triggers on Model Registry approval events and updates the endpoint using the boto3 SDK.

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Use SageMaker Pipelines to deploy the model directly upon training completion.

Question 2

A machine learning engineer needs to select features for a regression model. The dataset contains 50 numeric features, and the target variable is continuous. The engineer wants to reduce dimensionality by selecting features that have the strongest linear relationship with the target. Which feature selection method is MOST appropriate?

Accepted Answer

Correlation analysis. Correlation analysis (e.g., Pearson correlation) measures the linear relationship between each feature and the target. Features with high absolute correlation can be selected. Mutual information captures non-linear relationships but is more appropriate when non-linear relationships are expected. Recursive feature elimination and Lasso are valid but more computationally expensive for initial screening.

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Lasso regularization

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Mutual information

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Recursive feature elimination (RFE)

Question 3

A data scientist is using SageMaker Autopilot to automatically build a model. Which TWO aspects does Autopilot handle? (Choose TWO.)

Accepted Answer

Feature engineering. SageMaker Autopilot automatically performs feature engineering, which includes data preprocessing, feature transformation, and selection of the most relevant features to improve model performance. This is a core capability of Autopilot, as it analyzes the dataset and applies techniques like one-hot encoding, scaling, and imputation without manual intervention.

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Data ingestion

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Model deployment

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Data labeling

Question 4

A company uses Amazon SageMaker to deploy a model for real-time inference. They want to perform A/B testing between two model versions. Which TWO actions should the company take to set up A/B testing? (Choose TWO.)

Accepted Answer

Create an endpoint configuration with multiple production variants, each with a different model.. In SageMaker, A/B testing between two model versions is achieved by creating an endpoint configuration with multiple production variants, each pointing to a different model. This allows the endpoint to host both models simultaneously and route traffic between them based on assigned weights.

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Use Amazon CloudWatch Evidently to split traffic between models.

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Enable auto scaling for each production variant individually.

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Set the second production variant's weight to 0 and update later to 100.

Question 5

A financial services company needs to deploy a SageMaker endpoint that processes sensitive customer data. The security policy requires that all data in transit between the endpoint and the application must be encrypted, and that the endpoint cannot be accessed from the public internet. Additionally, model containers must not be able to initiate outbound internet requests. Which combination of settings meets these requirements?

Accepted Answer

Enable VPC-only mode for the endpoint and set EnableInterContainerTrafficEncryption to True. Enabling VPC-only mode for the SageMaker endpoint ensures the endpoint is not accessible from the public internet, and setting EnableInterContainerTrafficEncryption to True encrypts data in transit between containers within the endpoint. This combination directly satisfies the requirements for no public internet access and encrypted data in transit, while the model containers are isolated from outbound internet requests by the VPC configuration.

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Attach a public endpoint with an SSL certificate and restrict access via IAM

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Use a private subnet with a NAT Gateway and set EnableNetworkIsolation to True

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Deploy on a multi-model endpoint with encryption at rest using KMS

Question 6

A machine learning engineer deploys a multi-model endpoint using SageMaker. They need to track which model version was used for each inference request for compliance purposes. Which service should they integrate to capture this lineage?

Accepted Answer

SageMaker ML Lineage Tracking. SageMaker ML Lineage Tracking is the correct service because it is specifically designed to capture and query the lineage of machine learning artifacts, including model versions, datasets, and inference requests. By integrating with SageMaker endpoints, it automatically records the model version used for each inference, enabling compliance auditing without custom code.

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AWS CloudTrail

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SageMaker Model Monitor

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Amazon DynamoDB with custom logging

Question 7

A data engineer is designing a pipeline to process customer reviews for sentiment analysis. The text data contains punctuation, common words like 'the' and 'and', and emojis. Which sequence of preprocessing steps should they apply in Amazon SageMaker Data Wrangler?

Accepted Answer

Lowercase conversion → Remove punctuation → Remove stop words → Tokenization. Standard text preprocessing: lowercase, remove punctuation, remove stop words, then tokenize. Emoji handling could be additional, but the basic sequence is as described.

Answer

Tokenization → Lowercase conversion → Remove punctuation

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Remove stop words → Tokenization → Remove punctuation

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Tokenization → Remove stop words → Lowercase conversion

Question 8

A company wants to maintain multiple versions of a trained model in a central repository and track metadata such as training metrics, hyperparameters, and approval status. Which SageMaker feature should they use?

Accepted Answer

SageMaker Model Registry. SageMaker Model Registry is the correct choice because it is specifically designed to serve as a central repository for managing multiple versions of trained models, tracking metadata such as training metrics, hyperparameters, and approval status. It integrates with SageMaker Pipelines and Experiments to automate model governance, enabling versioning, approval workflows, and lineage tracking.

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SageMaker Pipelines

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SageMaker Feature Store

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SageMaker Experiments

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SageMaker Studio

Question 9

A data scientist needs to ensure that the same train/test split is used across multiple experiments for reproducibility in SageMaker. Which approach should they take?

Accepted Answer

Set a random seed in the training script. Setting a random seed in the training script ensures that the pseudo-random number generator used for splitting the dataset produces the same sequence of random indices across runs. This guarantees an identical train/test split regardless of instance type, hyperparameters, or dataset version, which is essential for reproducibility in SageMaker experiments.

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Use the same SageMaker instance type

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Use the same hyperparameter values

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Use the same dataset version

Question 10

A model deployed on SageMaker is returning inaccurate predictions for certain customer segments. The team suspects data drift. Which SageMaker feature should they use to continuously monitor input data distribution?

Accepted Answer

SageMaker Model Monitor. SageMaker Model Monitor is the correct choice because it is specifically designed to continuously monitor the input data distribution of a deployed model and detect data drift over time. It automatically captures and analyzes the statistical properties of incoming inference requests against a baseline, alerting you when significant deviations occur.

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SageMaker Clarify

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SageMaker Debugger

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SageMaker Feature Store

Question 11

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 12

A team wants to deploy a single SageMaker real-time endpoint that serves both a PyTorch model for NLP and a TensorFlow model for image classification. Each model requires a different inference container. Which two features can they use together to achieve this? (Select TWO.)

Accepted Answer

Multi-container endpoint. A multi-container endpoint allows running multiple containers (e.g., PyTorch and TensorFlow) on the same endpoint. With inference components, each container can be associated with a specific model, and the routing logic directs requests to the appropriate container based on the model name.

Answer

Multi-model endpoint

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Production variants

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SageMaker Neo compilation

Question 13

A company is building a real-time fraud detection system using Amazon Kinesis Data Streams. The data must be joined with a reference table (e.g., customer profile) that is stored in Amazon DynamoDB and updated frequently. The enriched data will be used for ML predictions. Which THREE AWS services should the company use to build this streaming pipeline? (Select THREE.)

Accepted Answer

Amazon Kinesis Data Analytics for Apache Flink. Kinesis Data Streams ingests streaming data. Kinesis Data Analytics for Apache Flink can perform stream-stream joins and enrich data with DynamoDB lookups. The enriched output can be sent to a Kinesis Data Stream or Firehose. Alternatively, using Lambda for enrichment is also valid, but the question asks for three services. The combination of Kinesis Data Streams, Kinesis Data Analytics (Flink), and DynamoDB covers ingestion, enrichment, and reference data. Kinesis Data Firehose is for delivery, not enrichment.

Answer

Amazon Kinesis Data Firehose

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AWS Glue ETL

Question 14

A model has high training accuracy but low validation accuracy. Which action is least likely to reduce overfitting?

Accepted Answer

Increase model complexity. Increasing model complexity (e.g., adding more layers or parameters) makes the model more flexible, which typically exacerbates overfitting by allowing it to memorize noise in the training data. Since the goal is to reduce overfitting, this action is counterproductive and therefore the least likely to help.

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Use dropout

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Increase regularization strength

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Add more training data

Question 15

A company uses SageMaker Model Registry to manage model versions. They have a cross-account deployment requirement: models approved in the development account must be deployed to a production account. Which approach is the MOST secure and recommended?

Accepted Answer

Share the model package group from the development account to the production account using AWS RAM, then create a model version in the production account. Cross-account deployment can be achieved by sharing the model package across accounts using AWS Resource Access Manager (RAM) or by exporting the model artifact to an S3 bucket with appropriate cross-account permissions, then creating the model in the target account.

Answer

Export the model from Model Registry to a tar.gz file and upload to the production account manually

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Copy the model artifact to a public S3 bucket and then create the model in the production account

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Use a Lambda function in the development account to call CreateEndpoint in the production account using cross-account IAM roles