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

The agency wants to analyze the census data for population segmentation, which is a type of unsupervised learning problem that aims to group similar data points together based on their attributes. The agency can use a combination of algorithms that can perform dimensionality reduction and clustering on the data to achieve this goal.

Dimensionality reduction is a technique that reduces the number of features or variables in a dataset while preserving the essential information and relationships. Dimensionality reduction can help improve the efficiency and performance of clustering algorithms, as well as facilitate data visualization and interpretation. One of the most common algorithms for dimensionality reduction is principal component analysis (PCA), which transforms the original features into a new set of orthogonal features called principal components that capture the maximum variance in the data. PCA can help reduce the noise and redundancy in the data and reveal the underlying structure and patterns.

Clustering is a technique that partitions the data into groups or clusters based on their similarity or distance. Clustering can help discover the natural segments or categories in the data and understand their characteristics and differences. One of the most popular algorithms for clustering is k-means, which assigns each data point to one of k clusters based on the nearest mean or centroid. K-means can handle large and high-dimensional datasets and produce compact and spherical clusters.

Therefore, the combination of algorithms that would provide the appropriate insights for population segmentation are PCA and k-means. The agency can use PCA to reduce the dimensionality of the census data from 500 features to a smaller number of principal components that capture most of the variation in the data. Then, the agency can use k-means to cluster the data based on the principal components and identify the segments of the population that share similar characteristics.

Amazon SageMaker Principal Component Analysis (PCA)

Amazon SageMaker K-Means Algorithm

Amazon Kinesis Data Streams is a service that can capture, store, and process streaming data in real time. Amazon Kinesis Data Firehose is a service that can deliver streaming data to various destinations, such as Amazon S3, Amazon Redshift, or Amazon Elasticsearch Service. Amazon Kinesis Data Firehose can also transform the data before delivering it, such as converting the data format, compressing the data, or encrypting the data. One of the supported data formats that Amazon Kinesis Data Firehose can convert to is Apache Parquet, which is a columnar storage format that can improve the performance and cost-efficiency of analytics queries. By using Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose, the Mobile Network Operator can ingest the .CSV data from the source systems and use Amazon Kinesis Data Firehose to convert the data into Parquet before storing it on Amazon S3. This solution takes the least effort to implement, as it does not require any additional resources, such as Amazon EC2 instances, Amazon EMR clusters, or Amazon Glue jobs. The solution can also leverage the built-in features of Amazon Kinesis Data Firehose, such as data buffering, batching, retry, and error handling.

Amazon Kinesis Data Streams - Amazon Web Services

Amazon Kinesis Data Firehose - Amazon Web Services

Data Transformation - Amazon Kinesis Data Firehose

Apache Parquet - Amazon Athena

A private subnet is a subnet that does not have a route to the internet gateway, which means that the resources in the private subnet cannot access the internet or be accessed from the internet. An S3 VPC endpoint is a gateway endpoint that allows the resources in the VPC to access the S3 service without going through the internet. By launching the notebook instances in a private subnet and accessing the data through an S3 VPC endpoint, the company can set up the job in a secure and compliant way, as the data never leaves the AWS network and is not exposed to the internet. This can also improve the performance and reliability of the data transfer, as the traffic does not depend on the internet bandwidth or availability.

Amazon VPC Endpoints - Amazon Virtual Private Cloud

Endpoints for Amazon S3 - Amazon Virtual Private Cloud

Connect to SageMaker Within your VPC - Amazon SageMaker

Working with VPCs and Subnets - Amazon Virtual Private Cloud