Amazon Web Services Updated MLS-C01 Exam Questions and Answers by chaim

Amazon Kinesis Data Analytics is a service that enables you to analyze streaming data in real time using SQL or Apache Flink applications. You can use Kinesis Data Analytics to process and gain insights from data streams such as web logs, clickstreams, IoT data, and more.

To use SQL to query a data stream of GZIP files, you need to first transform the data into a format that Kinesis Data Analytics can understand, such as JSON, CSV, or Apache Parquet. You can use an AWS Lambda function to perform this transformation and send the output to a Kinesis data stream that is connected to your Kinesis Data Analytics application. This way, you can use SQL to query the stream with the least latency, as Lambda functions are triggered in near real time by the incoming data and Kinesis Data Analytics can process the data as soon as it arrives.

The other options are not optimal for this scenario, as they introduce more latency or complexity. AWS Glue is a serverless data integration service that can perform ETL (extract, transform, and load) tasks on data sources, but it is not designed for real-time streaming data analysis. An Amazon Kinesis Client Library is a Java library that enables you to build custom applications that process data from Kinesis data streams, but it requires more coding and configuration than using a Lambda function. Amazon Kinesis Data Firehose is a service that can deliver streaming data to destinations such as Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk, but it does not support SQL queries on the data.

References:

What Is Amazon Kinesis Data Analytics for SQL Applications?

Using AWS Lambda with Amazon Kinesis Data Streams

Using AWS Lambda with Amazon Kinesis Data Firehose

 Area Under the ROC Curve (AUC) is a metric that measures the performance of a binary classifier across all possible thresholds. It is also known as the probability that a randomly chosen positive example will be ranked higher than a randomly chosen negative example by the classifier. AUC is a good metric to compare different classification models because it is independent of the class distribution and the decision threshold. It also captures both the sensitivity (true positive rate) and the specificity (true negative rate) of the model. References:

AWS Machine Learning Specialty Exam Guide

AWS Machine Learning Specialty Sample Questions

To fix the incorrectly skewed data, the Data Scientist can apply two feature transformations: numerical value binning and logarithmic transformation. Numerical value binning is a technique that groups continuous values into discrete bins or categories. This can help reduce the skewness of the data by creating more balanced frequency distributions. Logarithmic transformation is a technique that applies the natural logarithm function to each value in the data. This can help reduce the right skewness of the data by compressing the large values and expanding the small values. Both of these transformations can make the data more suitable for machine learning algorithms that assume normality of the data. References:

Data Transformation - Amazon SageMaker

Transforming Skewed Data for Machine Learning

The combination of steps that will meet the requirements are to create an IAM role in the development account that the integration account and production account can assume, attach IAM policies to the role that allow access to the feature repository and the S3 buckets, and share the feature repository that is associated with the S3 buckets from the development account to the integration account and the production account by using AWS Resource Access Manager (AWS RAM). This approach will enable cross-account access and sharing of the features stored in Amazon SageMaker Feature Store and Amazon S3.

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, update, search, and share curated data used in training and prediction workflows. The service provides feature management capabilities such as enabling easy feature reuse, low latency serving, time travel, and ensuring consistency between features used in training and inference workflows. A feature group is a logical grouping of ML features whose organization and structure is defined by a feature group schema. A feature group schema consists of a list of feature definitions, each of which specifies the name, type, and metadata of a feature. Amazon SageMaker Feature Store stores the features in both an online store and an offline store. The online store is a low-latency, high-throughput store that is optimized for real-time inference. The offline store is a historical store that is backed by an Amazon S3 bucket and is optimized for batch processing and model training1.

AWS Identity and Access Management (IAM) is a web service that helps you securely control access to AWS resources for your users. You use IAM to control who can use your AWS resources (authentication) and what resources they can use and in what ways (authorization). An IAM role is an IAM identity that you can create in your account that has specific permissions. You can use an IAM role to delegate access to users, applications, or services that don’t normally have access to your AWS resources. For example, you can create an IAM role in your development account that allows the integration account and the production account to assume the role and access the resources in the development account. You can attach IAM policies to the role that specify the permissions for the feature repository and the S3 buckets. You can also use IAM conditions to restrict the access based on the source account, IP address, or other factors2.

AWS Resource Access Manager (AWS RAM) is a service that enables you to easily and securely share AWS resources with any AWS account or within your AWS Organization. You can share AWS resources that you own with other accounts using resource shares. A resource share is an entity that defines the resources that you want to share, and the principals that you want to share with. For example, you can share the feature repository that is associated with the S3 buckets from the development account to the integration account and the production account by creating a resource share in AWS RAM. You can specify the feature group ARN and the S3 bucket ARN as the resources, and the integration account ID and the production account ID as the principals. You can also use IAM policies to further control the access to the shared resources3.

The other options are either incorrect or unnecessary. Using AWS Security Token Service (AWS STS) from the integration account and the production account to retrieve credentials for the development account is not required, as the IAM role in the development account can provide temporary security credentials for the cross-account access. Setting up S3 replication between the development S3 buckets and the integration and production S3 buckets would introduce redundancy and inconsistency, as the S3 buckets are already shared through AWS RAM. Creating an AWS PrivateLink endpoint in the development account for SageMaker is not relevant, as it is used to securely connect to SageMaker services from a VPC, not from another account.

References:

1: Amazon SageMaker Feature Store – Amazon Web Services

2: What Is IAM? - AWS Identity and Access Management

3: What Is AWS Resource Access Manager? - AWS Resource Access Manager