1. Which of the following is a correct statement about Amazon Comprehend's Custom Classification API?

A) Custom Classification API only works with predefined categories and it does not allow you to add new categories.
B) Custom Classification API only supports text classification in English language.
C) Custom Classification API requires you to provide labeled training data in JSON format only.
D) Custom Classification API enables you to train your own document classifier using your own training data.



2. In Amazon Machine Learning, what is the difference between binary classification and multi-class classification, and which algorithm is suitable for each of them?

A) Binary classification is used when the target variable has only two possible outcomes, and it can be solved using the k-means algorithm. Multi-class classification is used when the target variable has more than two possible outcomes, and it can be solved using the logistic regression algorithm.
B) Binary classification is used when the target variable has only two possible outcomes, and it can be solved using the logistic regression algorithm. Multi-class classification is used when the target variable has more than two possible outcomes, and it can be solved using the k-means algorithm.
C) Binary classification is used when the target variable has only two possible outcomes, and it can be solved using the decision tree algorithm. Multi-class classification is used when the target variable has more than two possible outcomes, and it can be solved using the random forest algorithm.
D) Binary classification is used when the target variable has only two possible outcomes, and it can be solved using the support vector machine algorithm. Multi-class classification is used when the target variable has more than two possible outcomes, and it can be solved using the neural network algorithm.



3. In AWS IoT Greengrass, what is the difference between a core and a device?

A) A core is an AWS IoT Greengrass component that processes data from the devices, while a device is a physical machine that sends data to the core.
B) A core and a device are the same thing in AWS IoT Greengrass.
C) A device is an AWS IoT Greengrass component that executes AWS Lambda functions, while a core is a physical machine that runs the device.
D) A core is an AWS IoT Greengrass component that executes AWS Lambda functions, while a device is a physical machine that runs the core.



4. A company has a dataset of sales transactions that need to be analyzed using Amazon Redshift. They want to create a Redshift cluster that will have high performance for querying large datasets and support real-time data ingestion. Which of the following options should the company choose?

A) Use Amazon Redshift Spectrum to query data directly from Amazon S3 and Amazon Kinesis Data Firehose to stream real-time data into Redshift.
B) Use Amazon Redshift Concurrency Scaling to automatically add or remove nodes to the Redshift cluster as needed to support concurrent queries.
C) Use Amazon Redshift RA3 nodes with managed storage to get better performance for large datasets and support real-time data ingestion.
D) Use Amazon Redshift Enhanced VPC routing to improve the performance of data transfers between the Redshift cluster and the company's Amazon VPC.



5. Which of the following AWS services can be used to collect and preprocess unstructured data for machine learning purposes? (Select 2)(Select 2answers)

A) Amazon MQ
B) AWS Batch
C) Amazon Elasticsearch
D) Amazon Connect
E) Amazon S3