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Amazon SAA-C03 Exam Syllabus Topics:

Topic	Details
Topic 1	Storage types with associated characteristics Design High-Performing Architectures
Topic 2	The AWS shared responsibility model Access controls and management across multiple accounts
Topic 3	Encryption and appropriate key management Determine appropriate data security controls
Topic 4	Design cost-optimized database solutions Design cost-optimized storage solutions
Topic 5	Control ports, protocols, and network traffic on AWS Design secure workloads and applications
Topic 6	Design cost-optimized compute solutions Design Cost-Optimized Architectures
Topic 7	Threat vectors external to AWS AWS federated access and identity services
Topic 8	How to appropriately use edge accelerators AWS managed services with appropriate use cases
Topic 9	Distributed computing concepts supported by AWS global infrastructure and edge services Serverless technologies and patterns
Topic 10	Database engines with appropriate use cases Determine high-performing database solutions

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Amazon AWS Certified Solutions Architect - Associate (SAA-C03) Exam Sample Questions (Q144-Q149):

NEW QUESTION # 144
A company has a highly dynamic batch processing job that uses many Amazon EC2 instances to complete it. The job is stateless in nature, can be started and stopped at any given time with no negative impact, and typically takes upwards of 60 minutes total to complete. The company has asked a solutions architect to design a scalable and cost-effective solution that meets the requirements of the job.
What should the solutions architect recommend?

• A. Implement the processing on AWS Lambda
• B. Implement EC2 Spot Instances
• C. Implement EC2 On-Demand Instances
• D. Purchase EC2 Reserved Instances

Answer: B
NEW QUESTION # 145
A company is looking for a way to analyze the calls between customers and service agents. Each conversation is transcribed, JSON-formatted, and saved to an Amazon S3 bucket. The company's solutions architect is tasked to design a solution for extracting and visualizing sentiments from the transcribed files.
Which solution meets the requirements while minimizing the amount of operational overhead?

• A. Analyze the JSON files with Amazon Textract. Index the sentiment along with the transcript to an Amazon OpenSearch cluster. Visualize the results using Amazon Managed Grafana.
• B. Create an Amazon Comprehend analysis job. Index the sentiment along with the transcript to an Amazon OpenSearch cluster. Visualize the results using Amazon Managed Grafana.
• C. Create an Amazon Comprehend analysis job. Index the sentiment along with the transcript to an Amazon OpenSearch cluster. Visualize the results using the OpenSearch Dashboard.
• D. Train a custom Natural Language Processing (NLP) model using Amazon SageMaker. Index the sentiment along with the transcript to an Amazon OpenSearch cluster. Visualize the results using the OpenSearch Dashboard.

Answer: C Explanation:
Amazon Comprehend uses machine learning to help you uncover the insights and relationships in your unstructured data. The service identifies the language of the text; extracts key phrases, places, people, brands, or events; understands how positive or negative the text is; analyzes text using tokenization and parts of speech, and automatically organizes a collection of text files by topic. You can also use AutoML capabilities in Amazon Comprehend to build a custom set of entities or text classification models that are tailored uniquely to your organization's needs.

In this scenario, you can build the application with the help of Amazon Comprehend. You could expose the application through a RESTful endpoint, have it invoke a Lambda function that will call Amazon Comprehend for sentiment analysis, and index data into an Amazon OpenSearch cluster.
Hence, the correct answer is: Create an Amazon Comprehend analysis job. Index the sentiment along with the transcript to an Amazon OpenSearch cluster. Visualize the results using the OpenSearch Dashboard.
The option that says: Analyze the JSON files with Amazon Textract. Index the sentiment along with the transcript to an Amazon OpenSearch cluster. Visualize the results using Amazon Managed Grafana is incorrect. Amazon Textract is just an AI service used to extract text data from scanned documents in PNG, JPEG, TIFF, PDF formats and is not capable of running sentiment analysis. Furthermore, Grafana is more suited for the visualization of time-series data such as system metrics (CPU load, disk storage, memory utilization, temperature, etc). While there are hacks you can use to visualize non-time series like the one in the scenario, they come with additional overhead on your part. The built-in OpenSearch dashboard is enough to do the job.
The option that says: Create an Amazon Comprehend analysis job. Index the sentiment along with the transcript to an Amazon OpenSearch cluster. Visualize the results using Amazon Managed Grafana is incorrect. The Amazon OpenSearch dashboard is a more suitable service to use than Grafana since the sentiment data is already processed by an Amazon OpenSearch cluster. This solution needs a separate AWS data source configuration in the Grafana workspace console to integrate and read the sentiment data, which entails an additional operational overhead.
The option that says: Train a custom Natural Language Processing (NLP) model using Amazon SageMaker. Index the sentiment along with the transcript to an Amazon OpenSearch cluster. Visualize the results using the Amazon QuickSight Dashboard is incorrect. Although this may be a viable option, training your own ML model rather than using the readily available Amazon Comprehend service requires more time and effort. The same is true in using the Amazon QuickSight dashboard instead of the OpenSearch Dashboard. It takes a lot of steps to properly integrate Amazon QuickSight and an OpenSearch cluster which might cause delays in the project implementation.
References:
https://aws.amazon.com/solutions/implementations/text-analysis-with-amazon-opensearch-service-and-a mazon-comprehend/
https://docs.aws.amazon.com/opensearch-service/latest/developerguide/walkthrough.html#walkthrough- analysis Check out this Amazon Comprehend Cheat Sheet:
https://tutorialsdojo.com/amazon-comprehend/
NEW QUESTION # 146
A company uses 50 TB of data for reporting. The company wants to move this data from on premises to AWS A custom application in the company's data center runs a weekly data transformation job. The company plans to pause the application until the data transfer is complete and needs to begin the transfer process as soon as possible.
The data center does not have any available network bandwidth for additional workloads A solutions architect must transfer the data and must configure the transformation job to continue to run in the AWS Cloud Which solution will meet these requirements with the LEAST operational overhead?

• A. Order an AWS Snowcone device to move the data Deploy the transformation application to the device
• B. Use AWS DataSync to move the data Create a custom transformation job by using AWS Glue
• C. Order an AWS Snowball Edge Storage Optimized device that includes Amazon EC2 compute Copy the data to the device Create a new EC2 instance on AWS to run the transformation application
• D. Order an AWS Snowball Edge Storage Optimized device. Copy the data to the device. Create a custom transformation job by using AWS Glue

Answer: D
NEW QUESTION # 147
A solutions architect needs to help a company optimize the cost of running an application on AWS. The application will use Amazon EC2 instances, AWS Fargate, and AWS Lambda for compute within the architecture.
The EC2 instances will run the data ingestion layer of the application. EC2 usage will be sporadic and unpredictable. Workloads that run on EC2 instances can be interrupted at any time. The application front end will run on Fargate, and Lambda will serve the API layer. The front-end utilization and API layer utilization will be predictable over the course of the next year.
Which combination of purchasing options will provide the MOST cost-effective solution for hosting this application? (Choose two.)

• A. Use On-Demand Instances for the data ingestion layer
• B. Purchase a 1-year Compute Savings Plan for the front end and API layer.
• C. Use Spot Instances for the data ingestion layer
• D. Purchase 1-year All Upfront Reserved instances for the data ingestion layer.
• E. Purchase a 1-year EC2 instance Savings Plan for the front end and API layer.

Answer: B,C
NEW QUESTION # 148
A company is running a multi-tier web application farm in a virtual private cloud (VPC) that is not connected to their corporate network. They are connecting to the VPC over the Internet to manage the fleet of Amazon EC2 instances running in both the public and private subnets. The Solutions Architect has added a bastion host with Microsoft Remote Desktop Protocol (RDP) access to the application instance security groups, but the company wants to further limit administrative access to all of the instances in the VPC.
Which of the following bastion host deployment options will meet this requirement?

• A. Deploy a Windows Bastion host on the corporate network that has RDP access to all EC2 instances in the VPC.
• B. Deploy a Windows Bastion host with an Elastic IP address in the private subnet, and restrict RDP access to the bastion from only the corporate public IP addresses.
• C. Deploy a Windows Bastion host with an Elastic IP address in the public subnet and allow SSH access to the bastion from anywhere.
• D. Deploy a Windows Bastion host with an Elastic IP address in the public subnet and allow RDP access to bastion only from the corporate IP addresses.

Answer: D Explanation:
The correct answer is to deploy a Windows Bastion host with an Elastic IP address in the public subnet and allow RDP access to bastion only from the corporate IP addresses.
A bastion host is a special purpose computer on a network specifically designed and configured to withstand attacks. If you have a bastion host in AWS, it is basically just an EC2 instance. It should be in a public subnet with either a public or Elastic IP address with sufficient RDP or SSH access defined in the security group. Users log on to the bastion host via SSH or RDP and then use that session to manage other hosts in the private subnets.

Deploying a Windows Bastion host on the corporate network that has RDP access to all EC2 instances in the VPC is incorrect since you do not deploy the Bastion host to your corporate network. It should be in the public subnet of a VPC.
Deploying a Windows Bastion host with an Elastic IP address in the private subnet, and restricting RDP access to the bastion from only the corporate public IP addresses is incorrect since it should be deployed in a public subnet, not a private subnet.
Deploying a Windows Bastion host with an Elastic IP address in the public subnet and allowing SSH access to the bastion from anywhere is incorrect. Since it is a Windows bastion, you should allow RDP access and not SSH as this is mainly used for Linux-based systems.
Reference:
https://docs.aws.amazon.com/quickstart/latest/linux-bastion/architecture.html Check out this Amazon VPC Cheat Sheet:
https://tutorialsdojo.com/amazon-vpc/
NEW QUESTION # 149
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