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 141–150 of 486
What happens when you deploy a model in Amazon SageMaker with an improper endpoint configuration?
A
Endpoint fails to create
B
Model is automatically retrained
C
Endpoint operates with degraded performance
D
Invocation errors occur only under load
An improper configuration will result in the endpoint failing to create, while the other options do not accurately depict the outcomes.
Which AWS service provides a fully managed machine learning model deployment platform?
A
Amazon SageMaker
B
AWS Lambda
C
Amazon EC2
D
AWS CloudFormation
Amazon SageMaker is designed for deploying machine learning models; the others focus on compute or infrastructure management.
A company needs to predict equipment failures. Which technique should they use for this time-series data problem?
A
Regression Analysis
B
Classification
C
Time Series Forecasting
D
Clustering
Time Series Forecasting specifically addresses predicting future values based on past data; the others do not focus on time dependency.
You are configuring an IAM Role for an ML application. What happens if the role lacks permissions for Amazon S3 access?
A
S3 access will be denied
B
ML model will fail to load
C
No effect on application performance
D
Data will be cached locally
If permissions are lacking, S3 access is denied; the other options do not accurately describe the impact of permission issues.
Which service should you use for real-time data streaming?
A
Amazon Kinesis
B
Amazon S3
C
Amazon RDS
D
AWS Lambda
Amazon Kinesis is specifically built for real-time streaming, while others serve different purposes.
A company needs to automate model training with minimal human intervention. What should they implement?
A
SageMaker Automation
B
AWS Config
C
CloudFormation
D
AWS Step Functions
SageMaker Automation allows for streamlined automated model training processes, unlike other options.
What happens when you use a low training dataset with a complex model?
A
Model underfitting occurs
B
Model is prone to overfitting
C
Models produce perfect accuracy
D
Training time decreases significantly
A complex model with insufficient data tends to learn noise, causing overfitting.
Which service is best for real-time data streaming?
A
Amazon Kinesis
B
AWS Lambda
C
Amazon S3
D
AWS Glue
Amazon Kinesis is designed for real-time data streaming; the others are used for different purposes.
A company needs to deploy a model that recognizes images. Which AWS service should they use?
A
AWS Sagemaker
B
AWS CodeDeploy
C
Amazon RDS
D
AWS Batch
AWS Sagemaker provides tools for deploying machine learning models, unlike the other options.
You are configuring model monitoring. What happens if you enable automatic monitoring in SageMaker?
A
Models are automatically retrained
B
Alerts are generated for data drift
C
All data is stored in S3
D
Model inputs are discarded
Automatic monitoring alerts you for data drift; the other options are incorrect actions taken by the service.