1. Which of the following is a benefit of using Amazon SageMaker Autopilot for model building?

A) Autopilot can generate an optimized machine learning model with no human intervention required.
B) Autopilot supports custom machine learning models written in any programming language.
C) Autopilot provides a complete end-to-end machine learning development environment, including data preparation and deployment.
D) Autopilot allows for fine-grained control over model tuning and hyperparameter optimization.



2. A company wants to collect and store real-time streaming data from social media platforms for sentiment analysis. Which AWS service can be used for this purpose?

A) Amazon S3
B) Amazon Kinesis Data Firehose
C) Amazon Elasticsearch Service
D) Amazon RDS
E) AWS Batch


3. Which AWS service can be used for ETL processing of large-scale data sets in near real-time?

A) AWS Glue
B) Amazon Kinesis Firehose
C) AWS Data Pipeline
D) AWS Step Functions
E) Amazon S3


4. You have trained a machine learning model using Amazon SageMaker and want to deploy it to a production environment that requires a high level of security and compliance. Which of the following deployment options would be the best fit for your use case?

A) Amazon SageMaker Hosting Services with VPC Endpoints
B) Amazon SageMaker Neo
C) Amazon Elastic Kubernetes Service (EKS)
D) Amazon Elastic Container Registry (ECR)



5. A company wants to build a natural language chatbot that can answer customer inquiries in real-time. The chatbot should be able to understand the intent behind customer messages and provide accurate responses. Which AWS ML application service would be most appropriate for this use case?

A) Amazon Comprehend
B) Amazon Lex
C) Amazon SageMaker
D) Amazon Polly
E) Amazon Transcribe