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[General] SAA-C03 New Dumps Files | Pdf SAA-C03 Files

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【General】 SAA-C03 New Dumps Files | Pdf SAA-C03 Files

Posted at 2/15/2026 19:16:16      View：47 | Replies：1        Print      Only Author   [Copy Link] 1#
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Amazon AWS Certified Solutions Architect - Associate Sample Questions (Q371-Q376):NEW QUESTION # 371
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. 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.
  • B. 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.
  • C. 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.
  • 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: B
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 ... earch-service-and-a mazon-comprehend/
https://docs.aws.amazon.com/open ... h.html#walkthrough- analysis Check out this Amazon Comprehend Cheat Sheet:
https://tutorialsdojo.com/amazon-comprehend/

NEW QUESTION # 372
A web application requires a minimum of six Amazon Elastic Compute Cloud (EC2) instances running at all times. You are tasked to deploy the application to three availability zones in the EU Ireland region (eu-west-1a, eu-west-1b, and eu-west-1c). It is required that the system is fault-tolerant up to the loss of one Availability Zone.
Which of the following setup is the most cost-effective solution which also maintains the fault-tolerance of your system?
  • A. 3 instances in eu-west-1a, 3 instances in eu-west-1b, and 3 instances in eu-west-1c
  • B. 6 instances in eu-west-1a, 6 instances in eu-west-1b, and no instances in eu-west-1c
  • C. 6 instances in eu-west-1a, 6 instances in eu-west-1b, and 6 instances in eu-west-1c
  • D. 2 instances in eu-west-1a, 2 instances in eu-west-1b, and 2 instances in eu-west-1c
Answer: A
Explanation:
Basically, fault-tolerance is the ability of a system to remain in operation even in the event that some of its components fail, without any service degradation. In AWS, it can also refer to the minimum number of running EC2 instances or resources which should be running at all times in order for the system to properly operate and serve its consumers. Take note that this is quite different from the concept of High Availability, which is just concerned with having at least one running instance or resource in case of failure.

In this scenario, 3 instances in eu-west-1a, 3 instances in eu-west-1b, and 3 instances in eu-west-1c is the correct answer because even if there was an outage in one of the Availability Zones, the system still satisfies the requirement of having a minimum of 6 running instances. It is also the most cost-effective solution among other options.
The option that says: 6 instances in eu-west-1a, 6 instances in eu-west-1b, and 6 instances in eu-west-1 is incorrect because although this solution provides the maximum fault-tolerance for the system, it entails a significant cost to maintain a total of 18 instances across 3 AZs.
The option that says: 2 instances in eu-west-1a, 2 instances in eu-west-1b, and 2 instances in eu-west-1 is incorrect because if one Availability Zone goes down, there will only be 4 running instances available. Although this is the most cost-effective solution, it does not provide fault-tolerance.
The option that says: 6 instances in eu-west-1a, 6 instances in eu-west-1b, and no instances in eu- west-1c is incorrect because although it provides fault-tolerance, it is not the most cost-effective solution as compared with the options above. This solution has 12 running instances, unlike the correct answer which only has 9 instances.
References:
https://docs.aws.amazon.com/AWSE ... e-availability.html
https://media.amazonwebservices. ... nt_Applications.pdf

NEW QUESTION # 373
A company hosts a three-tier ecommerce application on a fleet of Amazon EC2 instances. The instances run in an Auto Scaling group behind an Application Load Balancer (ALB) All ecommerce data is stored in an Amazon RDS for ManaDB Multi-AZ DB instance The company wants to optimize customer session management during transactions The application must store session data durably Which solutions will meet these requirements? (Select TWO )
  • A. Turn on the sticky sessions feature (session affinity) on the ALB
  • B. Use AWS Systems Manager Application Manager in the application to manage user session information
  • C. Use an Amazon DynamoDB table to store customer session information
  • D. Deploy an Amazon Cognito user pool to manage user session information
  • E. Deploy an Amazon ElastiCache for Redis cluster to store customer session information
Answer: A,E
Explanation:
Explanation
https://aws.amazon.com/caching/session-management/

NEW QUESTION # 374
A company is planning to use an Amazon DynamoDB table for data storage. The company is concerned about cost optimization. The table will not be used on most mornings. In the evenings, the read and write traffic will often be unpredictable. When traffic spikes occur, they will happen very quickly.
What should a solutions architect recommend?
  • A. Create a DynamoDB table with a global secondary index
  • B. Create a DynamoDB table in provisioned capacity mode, and configure it as a global table
  • C. Create a DynamoDB table with provisioned capacity and auto scaling.
  • D. Create a DynamoDB table in on-demand capacity mode.
Answer: D
Explanation:
Explanation
Provisioned capacity is best if you have relatively predictable application traffic, run applications whose traffic is consistent, and ramps up or down gradually. On-demand capacity mode is best when you have unknown workloads, unpredictable application traffic and also if you only want to pay exactly for what you use. The on-demand pricing model is ideal for bursty, new, or unpredictable workloads whose traffic can spike in seconds or minutes, and when under-provisioned capacity would impact the user experience.
https://docs.aws.amazon.com/well ... -lens/capacity.html

NEW QUESTION # 375
A company is hosting a web application on AWS using a single Amazon EC2 instance that stores user-uploaded documents in an Amazon EBS volume. For better scalability and availability, the company duplicated the architecture and created a second EC2 instance and EBS volume in another Availability Zone placing both behind an Application Load Balancer After completing this change, users reported that, each time they refreshed the website, they could see one subset of their documents or the other, but never all of the documents at the same time.
What should a solutions architect propose to ensure users see all of their documents at once?
  • A. Configure the Application Load Balancer to send the request to both servers Return each document from the correct server.
  • B. Copy the data so both EBS volumes contain all the documents.
  • C. Copy the data from both EBS volumes to Amazon EFS Modify the application to save new documents to Amazon EFS
  • D. Configure the Application Load Balancer to direct a user to the server with the documents
Answer: B
Explanation:
Explanation
Amazon EFS provides file storage in the AWS Cloud. With Amazon EFS, you can create a file system, mount the file system on an Amazon EC2 instance, and then read and write data to and from your file system. You can mount an Amazon EFS file system in your VPC, through the Network File System versions 4.0 and
4.1 (NFSv4) protocol. We recommend using a current generation Linux NFSv4.1 client, such as those found in the latest Amazon Linux, Redhat, and Ubuntu AMIs, in conjunction with the Amazon EFS Mount Helper. For instructions, see Using the amazon-efs-utils Tools.
For a list of Amazon EC2 Linux Amazon Machine Images (AMIs) that support this protocol, see NFS Support. For some AMIs, you'll need to install an NFS client to mount your file system on your Amazon EC2 instance. For instructions, see Installing the NFS Client.
You can access your Amazon EFS file system concurrently from multiple NFS clients, so applications that scale beyond a single connection can access a file system. Amazon EC2 instances running in multiple Availability Zones within the same AWS Region can access the file system, so that many users can access and share a common data source.
https://docs.aws.amazon.com/efs/ ... ml#how-it-works-ec2

NEW QUESTION # 376
......
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