Amazon Web Services MLS-C01 Exam With Confidence Using Practice Dumps

AWS IoT Greengrass is a service that extends AWS to edge devices, such as sensors and machines, so they can act locally on the data they generate, while still using the cloud for management, analytics, and durable storage. AWS IoT Greengrass enables local device messaging, secure data transfer, and local computing using AWS Lambda functions and machine learning models. AWS IoT Greengrass can run machine learning inference locally on devices using models that are created and trained in the cloud. This allows devices to respond quickly to local events, even when they are offline or have intermittent connectivity. Therefore, option B is the best deployment architecture for the model to address the business requirements of the manufacturer.

Option A is incorrect because deploying the model in Amazon SageMaker would require sending the sensor data to the cloud for inference, which would not work well for factory locations that do not have reliable or high-speed internet connectivity. Moreover, this option would not provide near-real-time inference capabilities, as there would be latency and bandwidth issues involved in transferring the data to and from the cloud. Option C is incorrect because deploying the model to an Amazon SageMaker batch transformation job would not provide near-real-time inference capabilities, as batch transformation is an asynchronous process that operates on large datasets. Batch transformation is not suitable for streaming data that requires low-latency responses. Option D is incorrect because deploying the model in Amazon SageMaker and using an IoT rule to write data to an Amazon DynamoDB table would also require sending the sensor data to the cloud for inference, which would have the same drawbacks as option A. Moreover, this option would introduce additional complexity and cost by involving multiple services, such as IoT Core, DynamoDB, and Lambda.

AWS Greengrass Machine Learning Inference - Amazon Web Services

Machine learning components - AWS IoT Greengrass

What is AWS Greengrass? | AWS IoT Core | Onica

GitHub - aws-samples/aws-greengrass-ml-deployment-sample

AWS IoT Greengrass Architecture and Its Benefits | Quick Guide - XenonStack

The Data Scientist needs to migrate an existing on-premises ETL process to the cloud, using a solution that can combine multiple data sources, reuse existing PySpark logic, run on the existing schedule, and minimize the number of servers that need to be managed. The best architecture for this scenario is to use AWS Glue, which is a serverless data integration service that can create and run ETL jobs on AWS.

AWS Glue can perform the following tasks to meet the requirements:

Combine multiple data sources: AWS Glue can access data from various sources, such as Amazon S3, Amazon RDS, Amazon Redshift, Amazon DynamoDB, and more. AWS Glue can also crawl the data sources and discover their schemas, formats, and partitions, and store them in the AWS Glue Data Catalog, which is a centralized metadata repository for all the data assets.

Reuse existing PySpark logic: AWS Glue supports writing ETL scripts in Python or Scala, using Apache Spark as the underlying execution engine. AWS Glue provides a library of built-in transformations and connectors that can simplify the ETL code. The Data Scientist can write the ETL job in PySpark and leverage the existing logic to perform the data processing.

Run the solution on the existing schedule: AWS Glue can create triggers that can start ETL jobs based on a schedule, an event, or a condition. The Data Scientist can create a new AWS Glue trigger to run the ETL job based on the existing schedule, using a cron expression or a relative time interval.

Minimize the number of servers that need to be managed: AWS Glue is a serverless service, which means that it automatically provisions, configures, scales, and manages the compute resources required to run the ETL jobs. The Data Scientist does not need to worry about setting up, maintaining, or monitoring any servers or clusters for the ETL process.

Therefore, the Data Scientist should use the following architecture to build the cloud solution:

Write the raw data to Amazon S3: The Data Scientist can use any method to upload the raw data from the on-premises sources to Amazon S3, such as AWS DataSync, AWS Storage Gateway, AWS Snowball, or AWS Direct Connect. Amazon S3 is a durable, scalable, and secure object storage service that can store any amount and type of data.

Create an AWS Glue ETL job to perform the ETL processing against the input data: The Data Scientist can use the AWS Glue console, AWS Glue API, AWS SDK, or AWS CLI to create and configure an AWS Glue ETL job. The Data Scientist can specify the input and output data sources, the IAM role, the security configuration, the job parameters, and the PySpark script location. The Data Scientist can also use the AWS Glue Studio, which is a graphical interface that can help design, run, and monitor ETL jobs visually.

Write the ETL job in PySpark to leverage the existing logic: The Data Scientist can use a code editor of their choice to write the ETL script in PySpark, using the existing logic to transform the data. The Data Scientist can also use the AWS Glue script editor, which is an integrated development environment (IDE) that can help write, debug, and test the ETL code. The Data Scientist can store the ETL script in Amazon S3 or GitHub, and reference it in the AWS Glue ETL job configuration.

Create a new AWS Glue trigger to trigger the ETL job based on the existing schedule: The Data Scientist can use the AWS Glue console, AWS Glue API, AWS SDK, or AWS CLI to create and configure an AWS Glue trigger. The Data Scientist can specify the name, type, and schedule of the trigger, and associate it with the AWS Glue ETL job. The trigger will start the ETL job according to the defined schedule.

Configure the output target of the ETL job to write to a “processed” location in Amazon S3 that is accessible for downstream use: The Data Scientist can specify the output location of the ETL job in the PySpark script, using the AWS Glue DynamicFrame or Spark DataFrame APIs. The Data Scientist can write the output data to a “processed” location in Amazon S3, using a format such as Parquet, ORC, JSON, or CSV, that is suitable for downstream processing.

What Is AWS Glue?

AWS Glue Components

AWS Glue Studio

AWS Glue Triggers

The services that are integrated with Amazon SageMaker to track the information that the Machine Learning Specialist needs are AWS CloudTrail and Amazon CloudWatch. AWS CloudTrail is a service that records the API calls and events for AWS services, including Amazon SageMaker. AWS CloudTrail can track the actions performed by the Data Scientists, such as creating notebooks, training models, and deploying endpoints. AWS CloudTrail can also provide information such as the identity of the user, the time of the action, the parameters used, and the response elements returned. AWS CloudTrail can help the Machine Learning Specialist to monitor the usage and activity of Amazon SageMaker, as well as to audit and troubleshoot any issues1 Amazon CloudWatch is a service that collects and analyzes the metrics and logs for AWS services, including Amazon SageMaker. Amazon CloudWatch can track the performance and utilization of the Amazon SageMaker endpoints, such as the CPU and GPU utilization, the inference latency, the number of invocations, etc. Amazon CloudWatch can also track the errors and alarms that are generated when an endpoint is invoked, such as the model errors, the throttling errors, the HTTP errors, etc. Amazon CloudWatch can help the Machine Learning Specialist to optimize the operational performance and reliability of Amazon SageMaker, as well as to set up notifications and actions based on the metrics and logs