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Amazon AWS Certified Machine Learning Engineer - Associate MLA-C01 Exam
AWS Certified Machine Learning Engineer - Associate MLA-C01 (Page 7 )

Updated On: 9-Feb-2026
Page 7 of 47
PDF with 230 Questions

A company wants to predict the success of advertising campaigns by considering the color scheme of each advertisement. An ML engineer is preparing data for a neural network model. The dataset includes color information as categorical data.

Which technique for feature engineering should the ML engineer use for the model?

  1. Apply label encoding to the color categories. Automatically assign each color a unique integer.
  2. Implement padding to ensure that all color feature vectors have the same length.
  3. Perform dimensionality reduction on the color categories.
  4. One-hot encode the color categories to transform the color scheme feature into a binary matrix.

Answer(s): D

Explanation:

One-hot encoding is the appropriate technique for transforming categorical data, such as color information, into a format suitable for input to a neural network. This technique creates a binary vector representation where each unique category (color) is represented as a separate binary column, ensuring that the model does not infer ordinal relationships between categories. This approach preserves the categorical nature of the data and avoids introducing unintended biases.



A company uses a hybrid cloud environment. A model that is deployed on premises uses data in Amazon S3 to provide customers with a live conversational engine.

The model is using sensitive data. An ML engineer needs to implement a solution to identify and remove the sensitive data.

Which solution will meet these requirements with the LEAST operational overhead?

  1. Deploy the model on Amazon SageMaker AI. Create a set of AWS Lambda functions to identify and remove the sensitive data.
  2. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate.
    Create an AWS Batch job to identify and remove the sensitive data.
  3. Use Amazon Macie to identify the sensitive data. Create a set of AWS Lambda functions to remove the sensitive data.
  4. Use Amazon Comprehend to identify the sensitive data. Launch Amazon EC2 instances to remove the sensitive data.

Answer(s): C

Explanation:

Amazon Macie is a fully managed data security and privacy service that uses machine learning to discover and classify sensitive data in Amazon S3. It is purpose-built to identify sensitive data with minimal operational overhead. After identifying the sensitive data, you can use AWS Lambda functions to automate the process of removing or redacting the sensitive data, ensuring efficiency and integration with the hybrid cloud environment. This solution requires the least development effort and aligns with the requirement to handle sensitive data effectively.



An ML engineer needs to create data ingestion pipelines and ML model deployment pipelines on AWS. All the raw data is stored in Amazon S3 buckets.

Which solution will meet these requirements?

  1. Use Amazon Data Firehose to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
  2. Use AWS Glue to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
  3. Use Amazon Redshift ML to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to create the model deployment pipelines.
  4. Use Amazon Athena to create the data ingestion pipelines. Use an Amazon SageMaker notebook to create the model deployment pipelines.

Answer(s): B

Explanation:

AWS Glue is a serverless data integration service that is well-suited for creating data ingestion pipelines, especially when raw data is stored in Amazon S3. It can clean, transform, and catalog data, making it accessible for downstream ML tasks.
Amazon SageMaker Studio Classic provides a comprehensive environment for building, training, and deploying ML models. It includes built-in tools and capabilities to create efficient model deployment pipelines with minimal setup.
This combination ensures seamless integration of data ingestion and ML model deployment with minimal operational overhead.



A company that has hundreds of data scientists is using Amazon SageMaker to create ML models. The models are in model groups in the SageMaker Model Registry.

The data scientists are grouped into three categories: computer vision, natural language processing (NLP), and speech recognition. An ML engineer needs to implement a solution to organize the existing models into these groups to improve model discoverability at scale. The solution must not affect the integrity of the model artifacts and their existing groupings.

Which solution will meet these requirements?

  1. Create a custom tag for each of the three categories. Add the tags to the model packages in the SageMaker Model Registry.
  2. Create a model group for each category. Move the existing models into these category model groups.
  3. Use SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models.
  4. Create a Model Registry collection for each of the three categories. Move the existing model groups into the collections.

Answer(s): D



A company runs an Amazon SageMaker domain in a public subnet of a newly created VPC. The network is configured properly, and ML engineers can access the SageMaker domain.

Recently, the company discovered suspicious traffic to the domain from a specific IP address. The company needs to block traffic from the specific IP address.

Which update to the network configuration will meet this requirement?

  1. Create a security group inbound rule to deny traffic from the specific IP address. Assign the security group to the domain.
  2. Create a network ACL inbound rule to deny traffic from the specific IP address. Assign the rule to the default network Ad for the subnet where the domain is located.
  3. Create a shadow variant for the domain. Configure SageMaker Inference Recommender to send traffic from the specific IP address to the shadow endpoint.
  4. Create a VPC route table to deny inbound traffic from the specific IP address. Assign the route table to the domain.

Answer(s): B

Explanation:

Network ACLs (Access Control Lists) operate at the subnet level and allow for rules to explicitly deny traffic from specific IP addresses. By creating an inbound rule in the network ACL to deny traffic from the suspicious IP address, the company can block traffic to the Amazon SageMaker domain from that IP. This approach works because network ACLs are evaluated before traffic reaches the security groups, making them effective for blocking traffic at the subnet level.






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