SAA-C03 Exam Dumps - Amazon Web Services AWS Certified Associate Questions and Answers

https://docs.aws.amazon.com/cli/latest/reference/transfer/describe-server.html

https://aws.amazon.com/fsx/lustre/

Amazon FSx for Lustre is a fully managed service that provides cost-effective, high-performance, scalable storage for compute workloads. Many workloads such as machine learning, high performance computing (HPC), video rendering, and financial simulations depend on compute instances accessing the same set of data through high-performance shared storage.

By purchasing a Compute Savings Plan, the company can save on the costs of running both EC2 instances and Lambda functions. The Lambda functions can be connected to the private subnet that contains the EC2 instances through a VPC endpoint for AWS services or a VPC peering connection. This provides direct network access to the EC2 instances while keeping the traffic within the private network, which helps to minimize network latency. Optimizing the Lambda functions’ duration, memory usage, number of invocations, and amount of data transferred can help to further minimize costs and improve performance. Additionally, using a private subnet helps to ensure that the EC2 instances are not directly accessible from the public internet, which is a security best practice.

The best option to improve the security of the data in transit is to configure a TLS listener and deploy the server certificate on the NLB. This will ensure that the data is encrypted and secure as it travels through the network. Additionally, you could also configure AWS Shield Advanced and enable AWS WAF on the NLB to further protect the network from malicious attacks. Alternatively, you could also change the load balancer to an Application Load Balancer (ALB) and enable AWS WAF on the ALB. Finally, you could also encrypt the Amazon Elastic Block Store (Amazon EBS) volume on the EC2 instances by using AWS Key Management Service (AWS KMS).

You must specify an SSL certificate for a TLS listener. The load balancer uses the certificate to terminate the connection and decrypt requests from clients before routing them to targets.https://docs.aws.amazon.com/elasticloadbalancing/latest/network/create-listener.html

Since the application has no local data on instances, AMIs alone can meet the RPO by restoring instances from the most recent AMI backup. When combined with automated RDS backups for the database, this provides a complete backup solution for this environment. The other options involving EBS snapshots would be unnecessary given the stateless nature of the instances. AMIs provide all the backup needed for the app tier. This uses native, automated AWS backup features that require minimal ongoing management: - AMI automated backups provide point-in-time recovery for the stateless app tier. - RDS automated backups provide point-in-time recovery for the database.

This answer is correct because it provides a resilient and durable replacement for the on-premises file share that is compatible with Windows IIS web servers. Amazon FSx for Windows File Server is a fully managed service that provides shared file storage built on Windows Server. It supports the SMB protocol and integrates with Microsoft Active Directory, which enables seamless access and authentication for Windows-based applications. Amazon FSx for Windows File Server also offers the following benefits:

Resilience: Amazon FSx for Windows File Server can be deployed in multiple Availability Zones, which provides high availability and failover protection. It also supports automatic backups and restores, as well as self-healing features that detect and correct issues.

Durability: Amazon FSx for Windows File Server replicates data within and across Availability Zones, and stores data on highly durable storage devices. It also supports encryption at rest and in transit, as well as file access auditing and data deduplication.

Performance: Amazon FSx for Windows File Server delivers consistent sub-millisecond latencies and high throughput for file operations. It also supports SSD storage, native Windows features such as Distributed File System (DFS) Namespaces and Replication, and user-driven performance scaling.

AWS Shield Advanced provides expanded DDoS attack protection for your Amazon EC2 instances, Elastic Load Balancing load balancers, CloudFront distributions, Route 53 hosted zones, and AWS Global Accelerator standard accelerators.

AWS WAF is a web application firewall that lets you monitor the HTTP and HTTPS requests that are forwarded to your protected web application resources. You can protect the following resource types:

Amazon CloudFront distribution

Amazon API Gateway REST API

Application Load Balancer

AWS AppSync GraphQL API

Amazon Cognito user pool

https://docs.aws.amazon.com/waf/latest/developerguide/what-is-aws-waf.html

https://docs.aws.amazon.com/waf/latest/developerguide/web-acl.html

A rate-based rule in AWS WAF allows the security team to configure thresholds that trigger rate-based rules, which enable AWS WAF to track the rate of requests for a specified time period and then block them automatically when the threshold is exceeded. This provides the ability to prevent HTTP flood attacks with minimal operational overhead.

The data retention period of a Kinesis data stream is the time period from when a record is added to when it is no longer accessible1. Thedefault retention period for a Kinesis data stream is 24 hours, which can be extended up to 8760 hours (365 days)1. The dataretention period can be updated by using the AWS Management Console, the AWS CLI, or the Kinesis Data Streams API1.

To meet the requirements of the scenario, the solutions architect should update the Kinesis Data Streams default settings by modifying the data retention period. The solutions architect should increase the retention period to a value that is greater than or equal to thefrequency of consuming the data and writing itto S32. This way, the company can ensure that S3 receives all the data that the application sends to Kinesis Data Streams.

• Launching instances within a single AZ and using a cluster placement group provides the lowest network latency and highest bandwidth between instances. This maximizes performance for an in-memory database and high-throughput application.

• Communications between instances in the same AZ and placement group are free, minimizing data transfer charges. Inter-AZ and public IP traffic can incur charges.

• A cluster placement group enables the instances to be placed close together within the AZ, allowing the high network throughput required. Partition groups span AZs, reducing bandwidth.

• Auto Scaling across zones could launch instances in AZs that increase data transfer charges. It may reduce network throughput, impacting performance.

https://aws.amazon.com/step-functions/#:~:text=AWS%20Step%20Functions%20is%20a,machine%20learning%20(ML)%20pipelines.

"A common use case for AWS Step Functions is a task that requires human intervention (for example, an approval process). Step Functions makes it easy to coordinate the componentsof distributed applications as a series of steps in a visual workflow called a state machine. You can quickly build and run state machines to execute the steps of your application in a reliable and scalable fashion. (https://aws.amazon.com/pt/blogs/compute/implementing-serverless-manual-approval-steps-in-aws-step-functions-and-amazon-api-gateway/) "

The best option is to use the AWS Snow Family console to order several AWS Snowball Edge Storage Optimized devices and use the devices to transfer the data to Amazon S3. Snowball Edge is a petabyte-scale data transfer device that can help transfer large amounts of data securely and quickly. Using Snowball Edge can be the most cost-effective solution for transferring large amounts of data over long distances and can help meet the requirement of transferring 600 TB of data within two weeks.

- First 30 days- data access every morning ( predictable and frequently) – S3 standard - After 30 days, accessed 4 times a year – S3 infrequently access - Data preserved- S3 Gllacier Deep Archive

This option is the most efficient because it uses Cost Explorer, which is a tool that allowsyou to visualize, understand, and manage your AWS costs and usage over time1. You can create a report in CostExplorer that lists AWS billed items by user, using the user name tag as a filter2. You can then download the report as a CSV file and use it for budget planning. Option A is less efficient because it uses Amazon Athena, which is a serverless interactive query service that allows you to analyze data in Amazon S3 using standard SQL3. You would need to set up an Athena table that points to your AWS Cost and Usage Report data in S3, and then run a query to generate the report. This would incur additional costs and complexity. Option C is less efficient because it uses the billing dashboard, which provides a high-level summary of your AWS costsand usage. You can access the bill details from the billing dashboard and download them via bill, but this would not list the billed items by user. You would need to use tags to group your costs by user name, which would require additional steps. Option D is less efficient because it uses AWS Budgets, which is a tool that allows you to plan your service usage, service costs, and instance reservations. You can modify a cost budget in AWS Budgets to alert with Amazon Simple Email Service (Amazon SES), but this would not generate a report of AWS billed items by user. This would only notify you when your actual or forecasted costs exceed or are expected to exceed your budgeted amount.

Amazon Inspector:

• Performs active vulnerability scans of EC2 instances. It looks for software vulnerabilities, unintended network accessibility, and other security issues.

• Requires installing an agent on EC2 instances to perform scans. The agent must be deployed to each instance.

• Provides scheduled scan reports detailing any findings of security risks or vulnerabilities. These reports can be used to patch or remediate issues.

• Is best suited for proactively detecting security weaknesses and misconfigurations in your AWS environment.

Understanding the Requirement: The company needs a disaster recovery solution for FSx for NetApp ONTAP in a secondary region, accessible using the same protocols (CIFS and NFS).

Analysis of Options:

Lambda Function and S3: Involves copying data to S3, which changes the access protocols and increases operational overhead.

AWS Backup: Suitable for backup and restore but not for real-time or near-real-time replication for disaster recovery.

FSx for ONTAP with SnapMirror: SnapMirror provides efficient replication between ONTAP instances, maintaining access protocols and requiring minimal operational overhead.

Amazon EFS: Does not support CIFS and requires migrating data, increasing complexity and changing access protocols.

Best Solution:

FSx for ONTAP with SnapMirror: This solution ensures seamless disaster recovery with the same access protocols and minimal operational overhead.

Understanding the Requirement: The company wants to cost-optimize their workload that uses EC2, Lambda, Fargate, and SageMaker, covering the most services with the fewest savings plans.

Analysis of Options:

EC2 Instance Savings Plan: Limited to EC2 and SageMaker, missing coverage for Lambda and Fargate.

Compute Savings Plan: Provides the most flexibility, covering a broad range of compute services, including EC2, Lambda, Fargate, and SageMaker.

SageMaker Savings Plan: Specifically for SageMaker, missing coverage for EC2, Lambda, and Fargate.

Combination of plans: The Compute Savings Plan is versatile and can be combined to cover different services efficiently.

Best Solution:

Compute Savings Plan for EC2, Lambda, and SageMaker: Covers the primary compute services efficiently.

Compute Savings Plan for Lambda, Fargate, and EC2: Covers the remaining services, ensuring broad coverage with minimal plans.

AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load your data for analytics. AWS Glue can automatically discover your data in Amazon S3 and catalog it, so you can query and search the data using SQL. AWS Glue can also run serverless ETL jobs using Apache Spark and Python to transform and load your data into various destinations, such as Amazon Redshift, Amazon Athena, or Amazon Aurora. AWS Glue is a serverless service, so you only pay for the resources consumed by the jobs, and you don’t need to provision or manage any infrastructure.

Amazon Redshift is a fully managed, petabyte-scale data warehouse service that enables you to use standard SQL and your existing business intelligence (BI) tools to analyze your data. Amazon Redshift also supports massively parallel processing (MPP), which means it can distribute and execute queries across multiple nodes in parallel, delivering fast performance and scalability. Amazon Redshift Serverless is a new option that automatically scales query compute capacity based on the queries being run, so you don’t need to manage clusters or capacity. You only pay for the query processing time and the storage consumed by your data.

Amazon Redshift ML is a feature that enables you to create, train, and deploy machine learning (ML) models using familiar SQL commands. Amazon Redshift ML can automatically discover the best model and hyperparameters for your data, and store the model in Amazon SageMaker, a fully managed service that provides a comprehensive set of tools for building, training, and deploying ML models. You can then use SQL functions to apply the model to your data in Amazon Redshift and generate predictions.

The combination of AWS Glue, Amazon Redshift Serverless, and Amazon Redshift ML meets the requirements of the question, as it provides a serverless, scalable, and SQL-based solution to transform, load, and analyze the data from the Amazon S3 data lake, and to create and train ML models on the data.

Option A is not correct, because Amazon EMR is not a serverless service. Amazon EMR is a managed service that simplifies running Apache Spark, Apache Hadoop, and other big data frameworks on AWS. Amazon EMR requires you to launch and configure clusters of EC2 instances to run your ETL jobs, which adds complexity and cost compared to AWS Glue.

Option B is not correct, because Amazon Aurora Serverless is not a data warehouse service, and it does not support MPP. Amazon Aurora Serverless is an on-demand, auto-scaling configuration for Amazon Aurora, a relational database service that is compatible with MySQL and PostgreSQL. Amazon Aurora Serverless can automatically adjust the database capacity based on the traffic, but it does not distribute the data and queries across multiple nodes like Amazon Redshift does. Amazon Aurora Serverless is more suitable for transactional workloads than analytical workloads.

Option D is not correct, because Amazon Athena is not a data warehouse service, and it does not support MPP. Amazon Athena is an interactive query service that enables you to analyze data in Amazon S3 using standard SQL. Amazon Athena is serverless, so you only pay for the queries you run, and you don’t need to load the data into a database. However, Amazon Athena does not store the data in a columnar format, compress the data, or optimize the query execution plan like Amazon Redshift does. Amazon Athena is more suitable for ad-hoc queries than complex analytics and ML.

Understanding the Requirement: The development team needs to prevent the launch of large EC2 instances across multiple AWS accounts used for development, staging, and production environments.

Analysis of Options:

IAM Policies: Would need to be applied individually to each user in every account, leading to significant operational overhead.

AWS Resource Access Manager: Used for sharing resources, not for enforcing restrictions on resource creation.

IAM Role in Each Account: Requires creating and managing roles in each account, leading to higher operational overhead compared to using a centralized approach.

Service Control Policy (SCP) with AWS Organizations: Provides a centralized way to enforce policies across multiple AWS accounts, ensuring that large EC2 instances cannot be launched in any account.

Best Solution:

Service Control Policy (SCP) with AWS Organizations: This solution offers the least operational overhead by allowing centralized management and enforcement of policies across all accounts, effectively preventing the launch of large EC2 instances.

Understanding the Requirement: The company has petabytes of data in S3 Standard across multiple buckets with varying access frequencies. They do not know the access patterns and need a cost-optimized storage solution with minimal operational effort.

Analysis of Options:

S3 Intelligent-Tiering: This storage class automatically moves data between two access tiers (frequent and infrequent) based on changing access patterns. It incurs a small monitoring and automation charge but eliminates the need to manually move data between storage classes.

S3 Storage Class Analysis Tool: While useful for determining access patterns, this tool requires manual intervention to move objects to the appropriate storage class, which increases operational overhead.

S3 Glacier Instant Retrieval: This storage class is designed for data that is rarely accessed but requires instant retrieval when needed. It may not be suitable for data with unknown and varying access patterns.

S3 One Zone-IA: This is a lower-cost option for infrequently accessed data stored in a single availability zone. It does not provide the same level of durability and availability as other options and requires knowledge of access patterns.

Best Option for Operational Efficiency:

S3 Intelligent-Tiering provides the best balance of cost savings and operational efficiency. It dynamically adjusts to access patterns without manual intervention, ensuring the company is only paying for what they need without the risk of incurring high costs for infrequent access data.