Actual Dumps for Amazon MLA-C01 Exam 2026

Question No: 1

MultipleChoice

An advertising company uses AWS Lake Formation to manage a data lake. The data lake contains structured data and unstructured data. The company's ML engineers are assigned to specific advertisement campaigns.

The ML engineers must interact with the data through Amazon Athena and by browsing the data directly in an Amazon S3 bucket. The ML engineers must have access to only the resources that are specific to their assigned advertisement campaigns.

Which solution will meet these requirements in the MOST operationally efficient way?

Options

AConfigure IAM policies on an AWS Glue Data Catalog to restrict access to Athena based on the ML engineers' campaigns.

BStore users and campaign information in an Amazon DynamoDB table. Configure DynamoDB Streams to invoke an AWS Lambda function to update S3 bucket policies.

CUse Lake Formation to authorize AWS Glue to access the S3 bucket. Configure Lake Formation tags to map ML engineers to their campaigns.

DConfigure S3 bucket policies to restrict access to the S3 bucket based on the ML engineers' campaigns.

Question No: 2

MultipleChoice

A company has historical data that shows whether customers needed long-term support from company staff. The company needs to develop an ML model to predict whether new customers will require long-term support.

Which modeling approach should the company use to meet this requirement?

Options

AAnomaly detection

BLinear regression

CLogistic regression

DSemantic segmentation

Question No: 3

MultipleChoice

A company needs to analyze a large dataset that is stored in Amazon S3 in Apache Parquet format. The company wants to use one-hot encoding for some of the columns.

The company needs a no-code solution to transform the data. The solution must store the transformed data back to the same S3 bucket for model training.

Which solution will meet these requirements?

Options

AConfigure an AWS Glue DataBrew project that connects to the data. Use the DataBrew interactive interface to create a recipe that performs the one-hot encoding transformation. Create a job to apply the transformation and write the output back to an S3 bucket.

BUse Amazon Athena SQL queries to perform the one-hot encoding transformation.

CUse an AWS Glue ETL interactive notebook to perform the transformation.

DUse Amazon Redshift Spectrum to perform the transformation.

Question No: 4

MultipleChoice

A company wants to use Amazon SageMaker AI to host an ML model that runs on CPU for real-time predictions. The model has intermittent traffic during business hours and periods of no traffic after business hours.

Which hosting option will serve inference requests in the MOST cost-effective manner?

Options

ADeploy the model to a real-time endpoint with scheduled auto scaling.

BDeploy the model to a SageMaker AI Serverless Inference endpoint with provisioned concurrency during business hours.

CDeploy the model to an asynchronous inference endpoint with auto scaling to zero.

DDeploy the model to a real-time endpoint and activate it only during business hours using AWS Lambda.

Question No: 5

MultipleChoice

A company wants to develop an ML model by using tabular data from its customers. The data contains meaningful ordered features with sensitive information that should not be discarded. An ML engineer must ensure that the sensitive data is masked before another team starts to build the model.

Which solution will meet these requirements?

Options

AUse Amazon Made to categorize the sensitive data.

BPrepare the data by using AWS Glue DataBrew.

CRun an AWS Batch job to change the sensitive data to random values.

DRun an Amazon EMR job to change the sensitive data to random values.

Question No: 6

MultipleChoice

A company is using an Amazon Redshift database as its single data source. Some of the data is sensitive.

A data scientist needs to use some of the sensitive data from the database. An ML engineer must give the data scientist access to the data without transforming the source data and without storing anonymized data in the database.

Which solution will meet these requirements with the LEAST implementation effort?

Options

AConfigure dynamic data masking policies to control how sensitive data is shared with the data scientist at query time.

BCreate a materialized view with masking logic on top of the database. Grant the necessary read permissions to the data scientist.

CUnload the Amazon Redshift data to Amazon S3. Use Amazon Athena to create schema-on-read with masking logic. Share the view with the data scientist.

DUnload the Amazon Redshift data to Amazon S3. Create an AWS Glue job to anonymize the data. Share the dataset with the data scientist.

Question No: 7

MultipleChoice

Case Study

A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a

central model registry, model deployment, and model monitoring.

The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.

The company must implement a manual approval-based workflow to ensure that only approved models can be deployed to production endpoints.

Which solution will meet this requirement?

Options

AUse SageMaker Experiments to facilitate the approval process during model registration.

BUse SageMaker ML Lineage Tracking on the central model registry. Create tracking entities for the approval process.

CUse SageMaker Model Monitor to evaluate the performance of the model and to manage the approval.

DUse SageMaker Pipelines. When a model version is registered, use the AWS SDK to change the approval status to 'Approved.'

Question No: 8

MultipleChoice

Case study

An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.

The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.

Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data.

Which solution will meet this requirement with the LEAST operational effort?

Options

AUse Amazon Athena to identify patterns that contribute to the imbalance. Adjust the dataset accordingly.

BUse Amazon SageMaker Studio Classic built-in algorithms to process the imbalanced dataset.

CUse AWS Glue DataBrew built-in features to oversample the minority class.

DUse the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class.

Free Amazon MLA-C01 Exam Dumps August 2026