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

 To configure the notebook instance placement to meet the requirements, the Data Science team should associate the Amazon SageMaker notebook with a private subnet in a VPC. A VPC is a virtual network that is logically isolated from other networks in AWS. A private subnet is a subnet that has no internet gateway attached to it, and therefore cannot communicate with the internet. By placing the notebook instance in a private subnet, the team can ensure that it stays within a secured VPC with no internet access.

However, to access data stored in Amazon S3 buckets and other AWS services, the team needs to ensure that the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it. A VPC endpoint is a gateway that enables private connections between the VPC and supported AWS services. A VPC endpoint does not require an internet gateway, a NAT device, or a VPN connection, and ensures that the traffic between the VPC and the AWS service does not leave the AWS network. By using VPC endpoints, the team can access Amazon S3 and Amazon SageMaker from the notebook instance without compromising data privacy or security.

What Is Amazon VPC? - Amazon Virtual Private Cloud

Subnet Routing - Amazon Virtual Private Cloud

VPC Endpoints - Amazon Virtual Private Cloud

A collaborative filtering recommendation engine is a type of machine learning system that can improve sales for a company by using the large amount of information the company has on users’ behavior and product preferences to predict which products users would like based on the users’ similarity to other users. A collaborative filtering recommendation engine works by finding the users who have similar ratings or preferences for the products, and then recommending the products that the similar users have liked but the target user has not seen or rated. A collaborative filtering recommendation engine can leverage the collective wisdom of the users and discover the hidden patterns and associations among the products and the users. A collaborative filtering recommendation engine can be implemented using Apache Spark ML on Amazon EMR, which are two services that can handle large-scale data processing and machine learning tasks. Apache Spark ML is a library that provides various tools and algorithms for machine learning, such as classification, regression, clustering, recommendation, etc. Apache Spark ML can run on Amazon EMR, which is a service that provides a managed cluster platform that simplifies running big data frameworks, such as Apache Spark, on AWS. Apache Spark ML on Amazon EMR can build a collaborative filtering recommendation engine using the Alternating Least Squares (ALS) algorithm, which is a matrix factorization technique that can learn the latent factors that represent the users and the products, and then use them to predict the ratings or preferences of the users for the products. Apache Spark ML on Amazon EMR can also support both explicit feedback, such as ratings or reviews, and implicit feedback, such as views or clicks, for building a collaborative filtering recommendation engine12

A VPC endpoint is a logical device that enables private connections between a VPC and supported AWS services. A VPC endpoint can be either a gateway endpoint or an interface endpoint. A gateway endpoint is a gateway that is a target for a specified route in the route table, used for traffic destined to a supported AWS service. An interface endpoint is an elastic network interface with a private IP address that serves as an entry point for traffic destined to a supported service1

In this case, the Machine Learning Specialist can create a gateway endpoint for Amazon S3, which is a supported service for gateway endpoints. A gateway endpoint for Amazon S3 enables the VPC to access Amazon S3 privately, without requiring an internet gateway, NAT device, VPN connection, or AWS Direct Connect connection. The traffic between the VPC and Amazon S3 does not leave the Amazon network2

To restrict access to the dataset stored in Amazon S3, the Machine Learning Specialist can apply a bucket access policy that allows access only from the given VPC endpoint and the VPC. A bucket access policy is a resource-based policy that defines who can access a bucket and what actions they can perform. A bucket access policy can use various conditions to control access, such as the source IP address, the source VPC, the source VPC endpoint, etc. In this case, the Machine Learning Specialist can use the aws:sourceVpce condition to specify the ID of the VPC endpoint, and the aws:sourceVpc condition to specify the ID of the VPC. This way, only the requests that originate from the VPC endpoint or the VPC can access the bucket that contains the dataset34

The other options are not valid or secure ways to satisfy the requirements. Creating a VPC endpoint and applying a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance is not a good option, as it does not restrict access to the VPC. An Amazon EC2 instance is a virtual server that runs in the AWS cloud. An Amazon EC2 instance can have a public IP address or a private IP address, depending on the network configuration. Allowing access from an Amazon EC2 instance does not guarantee that the instance is in the same VPC as the VPC endpoint, and may expose the dataset to unauthorized access. Creating a VPC endpoint and using Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance is not a good option, as it does not restrict access to the VPC. NACLs are stateless firewalls that can control inbound and outbound traffic at the subnet level. NACLs can use rules to allow or deny traffic based on the protocol, port, and source or destination IP address. However, NACLs do not support VPC endpoints as a source or destination, and cannot filter traffic based on the VPC endpoint ID or the VPC ID. Therefore, using NACLs does not guarantee that the traffic is from the VPC endpoint or the VPC, and may expose the dataset to unauthorized access. Creating a VPC endpoint and using security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance is not a good option, as it does not restrict access to the VPC. Security groups are stateful firewalls that can control inbound and outbound traffic at the instance level. Security groups can use rules to allow or deny traffic based on the protocol, port, and source or destination. However, security groups do not support VPC endpoints as a source or destination, and cannot filter traffic based on the VPC endpoint ID or the VPC ID. Therefore, using security groups does not guarantee that the traffic is from the VPC endpoint or the VPC, and may expose the dataset to unauthorized access.

According to the Amazon Kinesis Data Streams documentation, the maximum size of data blob (the data payload before Base64-encoding) per record is 1 MB. The maximum number of records that can be sent to a shard per second is 1,000. Therefore, the maximum throughput of a shard is 1 MB/sec for input and 2 MB/sec for output. In this case, the input throughput is 100 transactions per second * 100 KB per transaction = 10 MB/sec. Therefore, the minimum number of shards required is 10 MB/sec / 1 MB/sec = 10 shards. However, the question asks for the minimum number of shards in Kinesis Data Streams, not the minimum number of shards per stream. A Kinesis Data Streams account can have multiple streams, each with its own number of shards. Therefore, the minimum number of shards in Kinesis Data Streams is 1, which is the minimum number of shards per stream. References:

Amazon Kinesis Data Streams Terminology and Concepts

Amazon Kinesis Data Streams Limits