Amazon AWS
AWS Certified Machine Learning Engineer – Associate
MLA-C01
The AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam validates your skills in building, training, and deploying machine learning models on AWS. It is ideal for those looking to specialize in machine learning.
Questions 181–190 of 486
Which AWS service is used for managing machine learning workflows seamlessly?
A
AWS SageMaker
B
AWS Batch
C
AWS Lambda
D
Amazon EC2
AWS SageMaker provides tools for building, training, and deploying ML models, while the others do not primarily focus on ML workflows.
A company needs to extract text from images for analysis. Which AWS service should they use?
A
Rekognition
B
Transcribe
C
Textract
D
Comprehend
Amazon Textract is specifically designed for extracting text and data from scanned documents and images.
What happens when an S3 object is deleted but versioning is enabled?
A
The object is permanently deleted
B
The previous version is restored
C
A delete marker is created
D
Nothing happens to the object
A delete marker is created with versioning enabled, allowing access to previous versions.
Which service is best for deploying machine learning models at scale?
A
Amazon SageMaker
B
AWS Lambda
C
Amazon RDS
D
Amazon S3
Amazon SageMaker is designed for deploying ML models, while the others serve different purposes.
A company needs to adjust its Amazon S3 bucket policy. What should they do first?
A
Change bucket region
B
Review IAM roles
C
Audit current permissions
D
Check cost model
Auditing current permissions is essential to understand what changes are needed.
What happens when a data preprocessing step is skipped in a machine learning pipeline?
A
Model accuracy improves
B
Data becomes less relevant
C
Training time is reduced
D
Model performance can degrade
Skipping data preprocessing often leads to poorer model performance due to unhandled noise or inconsistencies in the data.
Which AWS service is best for building and deploying ML models at scale?
A
SageMaker
B
CloudFormation
C
Lambda
D
Athena
SageMaker is designed specifically for ML model development, while other options serve different purposes.
A company needs real-time data processing for an IoT application. Which combination of services would you recommend?
A
Kinesis and Lambda
B
SQS and RDS
C
DynamoDB and EMR
D
S3 and Athena
Kinesis and Lambda are optimized for real-time processing, whereas the other options are better suited for batch processing or storage.
You are configuring a machine learning model using Keras on SageMaker. What happens if you forget to set the 'epochs' parameter?
A
Training will not occur.
B
It defaults to zero.
C
It defaults to one.
D
Training will complete with warnings.
If 'epochs' is not set, Keras defaults it to one, allowing a single pass through the training dataset.
Which service helps you automate model tuning?
A
Amazon SageMaker HyperParameter Tuning
B
AWS Batch
C
Amazon EC2
D
AWS Lambda
Amazon SageMaker HyperParameter Tuning automates tuning; others do not specialize in automation.