1. You are designing a serverless machine learning application that will be deployed on AWS Lambda. The application will require access to a large amount of data stored in an S3 bucket. What is the most efficient way to provide access to the S3 data from Lambda?

A) Use Amazon Kinesis Firehose to stream the data to the Lambda function
B) Create an S3 bucket policy to grant access to the Lambda function
C) Use Amazon SQS to queue the data for the Lambda function to process
D) Use IAM roles to grant access to the S3 bucket



2. You are working on a project where you need to visualize and analyze large amounts of customer transaction data to identify trends and patterns. Which AWS service can you use to perform complex data analysis and visualization tasks efficiently and securely?

A) Amazon Redshift
B) Amazon QuickSight
C) Amazon RDS
D) Amazon Athena
E) Amazon SageMaker


3. Which AWS service can be used to collect and process large amounts of data from multiple sources, providing real-time data streaming, data transformation, and integration with AWS Machine Learning services?

A) AWS Glue
B) AWS Data Pipeline
C) Amazon Kinesis Data Analytics
D) Amazon Redshift



4. You are working on a project to forecast sales for a retail company. You have historical sales data for the past three years and want to use Amazon Forecast to build a forecasting model. Which of the following is a valid approach to prepare the data for use with Amazon Forecast?

A) Convert the data into a tabular dataset and upload it to Amazon S3, with each row containing customer ID, item ID, and the corresponding sales value.
B) Convert the data into a dataset with item metadata and upload it to Amazon S3, with each row containing the item ID, item category, and the corresponding sales value.
C) Convert the data into a dataset with customer metadata and upload it to Amazon S3, with each row containing customer ID, customer segment, and the corresponding sales value.
D) Convert the data into a time series dataset and upload it to Amazon S3, with each row containing a timestamp, item ID, and the corresponding sales value.



5. Which AWS service can be used to orchestrate and automate ETL workflows for large datasets?

A) Amazon Redshift
B) Amazon RDS
C) AWS Glue
D) Amazon S3
E) Amazon Kinesis