1. Which of the following statements is true about Amazon Athena and its integration with Amazon S3 for machine learning data analysis?

A) Amazon Athena is a fully managed service that allows you to perform ad-hoc query analysis of data stored in Amazon S3, making it an ideal tool for machine learning data analysis.
B) Athena's integration with Amazon S3 allows you to create machine learning models and perform real-time analysis of data, making it an ideal tool for real-time decision-making.
C) Athena can be used to store and query machine learning data directly, allowing you to analyze large datasets for machine learning purposes without having to use any additional tools or services.
D) Athena integrates with Amazon SageMaker to provide machine learning model training and deployment capabilities directly from Athena, making it an ideal tool for end-to-end machine learning workflows.



2. You are tasked with building a machine learning model that requires distributed processing across a large number of instances in an Amazon EMR cluster. The model uses Apache Spark as the framework for data processing and requires a high level of interactivity. Which of the following EMR cluster configurations would be most suitable for this use case?

A) A cluster with all instances running on r4.xlarge with EBS-optimized instances and a single master node running on an m5.large instance.
B) A cluster with a mix of instance types, such as r5.2xlarge and m5.2xlarge, with EBS-optimized instances and a single master node running on an m5.4xlarge instance.
C) A cluster with all instances running on r5d.4xlarge with EBS-optimized instances and a single master node running on an m5.large instance.
D) A cluster with all instances running on r5.8xlarge with EBS-optimized instances and a single master node running on an m5.8xlarge instance.
E) A cluster with all instances running on m5.large with EBS-optimized instances and a single master node running on an m5.2xlarge instance.


3. You are building a real-time recommendation engine that requires processing a high volume of streaming data. You plan to use Amazon Kinesis Data Analytics to process the data. You need to ensure that your application is scalable, fault-tolerant, and provides low-latency processing. Which of the following configurations would you recommend?

A) Use a single shard for the input stream and a single shard for the output stream
B) Use multiple shards for the input stream and a single shard for the output stream
C) Use a single shard for the input stream and multiple shards for the output stream
D) Use multiple shards for the input stream and multiple shards for the output stream
E) Use Amazon Kinesis Data Streams instead of Kinesis Data Analytics


4. You are designing a data pipeline using Amazon Kinesis Data Firehose to ingest and transform streaming data. Your pipeline requires data validation and filtering to ensure that only valid records are stored in Amazon S3. Which of the following services can you use to perform data validation and filtering in Amazon Kinesis Data Firehose?

A) AWS Glue ETL jobs
B) Amazon SNS
C) Amazon Kinesis Data Analytics
D) AWS Lambda



5. Which of the following is NOT a valid feature of Amazon Kinesis Data Streams?

A) Retention period of up to 7 days for streamed data
B) Ability to store, process, and analyze real-time streaming data at scale
C) Automatic scaling of streaming capacity to accommodate changes in data volume
D) Ability to perform real-time analytics on streaming data using Kinesis Analytics
E) Ability to write data to Kinesis Data Streams from any data source, such as IoT devices or log files