AWS Certified Machine Learning Engineer - Associate Exam (MLA-C01) - AWS Actual Exam Questions
Last updated on April 11, 2026
A company wants to use large language models (LLMs) supported by Amazon Bedrock to develop a chat interface for internal technical documentation. The documentation consists of dozens of text files totaling several megabytes and is updated frequently. Which solution will meet these requirements MOST cost-effectively?
Train a new LLM in Amazon Bedrock using the documentation.
Use Amazon Bedrock guardrails to integrate documentation.
Fine-tune an LLM in Amazon Bedrock with the documentation.
Upload the documentation to an Amazon Bedrock knowledge base and use it as context during inference.
to join the discussion
No discussions yet. Be the first to ask!
Delete Comment
Are you sure? This action cannot be undone.
A company is building a near real-time data analytics application to detect anomalies and failures for industrial equipment. The company has thousands of IoT sensors that send data every 60 seconds. When new versions of the application are released, the company wants to ensure that application code bugs do not prevent the application from running. Which solution will meet these requirements?
Use Amazon Managed Service for Apache Flink with the system rollback capability enabled to build the data analytics application.
Use Amazon Managed Service for Apache Flink with manual rollback when an error occurs to build the data analytics application.
Use Amazon Data Firehose to deliver real-time streaming data programmatically for the data analytics application. Pause the stream when a new version of the application is released and resume the stream after the application is deployed.
Use Amazon Data Firehose to deliver data to Amazon EC2 instances across two Availability Zones for the data analytics application.
to join the discussion
No discussions yet. Be the first to ask!
Delete Comment
Are you sure? This action cannot be undone.
An ML engineer has developed a binary classification model outside of Amazon SageMaker. The ML engineer needs to make the model accessible to a SageMaker Canvas user for additional tuning. The model artifacts are stored in an Amazon S3 bucket. The ML engineer and the Canvas user are part of the same SageMaker domain. Which combination of requirements must be met so that the ML engineer can share the model with the Canvas user? (Choose two.)
The ML engineer and the Canvas user must be in separate SageMaker domains.
The Canvas user must have permissions to access the S3 bucket where the model artifacts are stored.
The model must be registered in the SageMaker Model Registry.
The ML engineer must host the model on AWS Marketplace.
The ML engineer must deploy the model to a SageMaker endpoint.
to join the discussion
No discussions yet. Be the first to ask!
Delete Comment
Are you sure? This action cannot be undone.
A company runs an Amazon SageMaker AI domain in a public subnet of a newly created VPC. The network is configured properly, and ML engineers can access the SageMaker AI 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?
Create a security group inbound rule to deny traffic from the specific IP address. Assign the security group to the domain.
Create a network ACL inbound rule to deny traffic from the specific IP address. Assign the rule to the default network ACL for the subnet where the domain is located.
Create a shadow variant for the domain. Configure SageMaker Inference Recommender to send traffic from the specific IP address to the shadow endpoint.
Create a VPC route table to deny inbound traffic from the specific IP address. Assign the route table to the domain.
to join the discussion
No discussions yet. Be the first to ask!
Delete Comment
Are you sure? This action cannot be undone.
An ML engineer needs to process thousands of existing CSV objects and new CSV objects that are uploaded. The CSV objects are stored in a central Amazon S3 bucket and have the same number of columns. One of the columns is a transaction date. The ML engineer must query the data based on the transaction date. Which solution will meet these requirements with the LEAST operational overhead?
Use an Amazon Athena CREATE TABLE AS SELECT (CTAS) statement to create a table based on the transaction date from data in the central S3 bucket. Query the objects from the table.
Create a new S3 bucket for processed data. Set up S3 replication from the central S3 bucket to the new S3 bucket. Use S3 Object Lambda to query the objects based on transaction date.
Create a new S3 bucket for processed data. Use AWS Glue for Apache Spark to create a job to query the CSV objects based on transaction date. Configure the job to store the results in the new S3 bucket. Query the objects from the new S3 bucket.
Create a new S3 bucket for processed data. Use Amazon Data Firehose to transfer the data from the central S3 bucket to the new S3 bucket. Configure Firehose to run an AWS Lambda function to query the data based on transaction date.
to join the discussion
No discussions yet. Be the first to ask!
Delete Comment
Are you sure? This action cannot be undone.
Finish Practice?
Are you sure you want to finish? This will end your practice session.