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 31–40 of 486
Which service is best for real-time data analytics?
A
Amazon Kinesis
B
Amazon S3
C
AWS Lambda
D
Amazon RDS
Amazon Kinesis allows for real-time streaming data processing, while others serve different purposes.
A company needs to deploy a predictive model using SageMaker. What steps must they take first?
A
Create a Jupyter notebook instance
B
Define an IAM role
C
Launch an EC2 instance
D
Upload data to DynamoDB
Defining an IAM role is integral for SageMaker to securely access resources.
What happens when a model trained with biased data is deployed?
A
Improved accuracy on unbiased data
B
Generates fair predictions for all
C
Perpetuates existing biases in predictions
D
Requires no further monitoring
A biased model continues to reflect the biases of its training data.
Which service is best for real-time streaming analytics?
A
Kinesis Data Streams
B
S3 Batch Operations
C
RDS
D
Athena
Kinesis Data Streams is designed for real-time analytics, while the others serve different purposes.
A company needs to secure an API using IAM. What is the best approach?
A
Use an IAM policy attached to the API
B
Identity-based policies on user accounts
C
Allow public access to the API
D
Create resource-based policies for API access
Resource-based policies for API access ensure specific permissions without exposing the API to the public.
You are configuring an ML model in SageMaker. What happens when you specify 'Low' for the instance type?
A
Faster model training
B
Lower training cost
C
More data processing power
D
Longer training duration
Specifying 'Low' for the instance type typically results in less compute power, extending training durations.
Which service provides fully managed machine learning models?
A
Amazon SageMaker
B
AWS Lambda
C
Amazon S3
D
Amazon EC2
Amazon SageMaker offers managed ML model training and deployment, while others serve different purposes.
A company needs to streamline its data pipeline for real-time ML model scoring. Which AWS service should they consider?
A
AWS Batch
B
Amazon Kinesis
C
AWS Glue
D
Amazon RDS
Amazon Kinesis is ideal for processing real-time data streams, unlike the others which serve different use cases.
What happens when you enable model monitoring in Amazon SageMaker?
A
Models are deleted automatically
B
Performance metrics are collected
C
Training data gets encrypted
D
Inference is paused
Enabling model monitoring collects performance metrics to provide insights, unlike the other options which have no relevance.
Which service can automatically scale your ML model endpoints in AWS?
A
Amazon SageMaker
B
AWS Lambda
C
Amazon EC2
D
AWS Fargate
Amazon SageMaker provides auto-scaling for endpoints, while the others do not specialize in ML-specific scaling.