THREE EASY-TO-USE AMAZON SAA-C03 EXAM DUMPS FORMATS

Three Easy-to-Use Amazon SAA-C03 Exam Dumps Formats

Three Easy-to-Use Amazon SAA-C03 Exam Dumps Formats

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Amazon SAA-C03 exam is a certification exam that validates the technical expertise of individuals in designing and deploying scalable and fault-tolerant systems on AWS. SAA-C03 exam tests the candidate's knowledge of AWS services and their ability to design and deploy highly available, cost-effective, and scalable systems on AWS. Candidates who Pass SAA-C03 Exam will demonstrate their ability to design and deploy robust and scalable systems on AWS, which is highly valued by employers. To prepare for the exam, candidates should enroll in AWS training courses, read AWS documentation, and practice with AWS services.

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Individuals who pass the Amazon SAA-C03 Exam will receive a certification that demonstrates their expertise in AWS solutions. Amazon AWS Certified Solutions Architect - Associate (SAA-C03) Exam certification is highly valued in the industry and can lead to better job opportunities and higher salaries. It is also a great way for individuals to stay up-to-date with the latest AWS technologies and best practices.

Amazon AWS Certified Solutions Architect - Associate (SAA-C03) Exam Sample Questions (Q355-Q360):

NEW QUESTION # 355
A company uses a legacy application to produce data in CSV format The legacy application stores the output data In Amazon S3 The company is deploying a new commercial off-the-shelf (COTS) application that can perform complex SQL queries to analyze data that is stored Amazon Redshift and Amazon S3 only However the COTS application cannot process the csv files that the legacy application produces The company cannot update the legacy application to produce data in another format The company needs to implement a solution so that the COTS application can use the data that the legacy applicator produces.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Use Amazon EventBridge (Amazon CloudWatch Events) to launch an Amazon EMR cluster on a weekly schedule. Configure the EMR cluster to perform an extract, tractform, and load (ETL) job to process the .csv files and store the processed data in an Amazon Redshift table.
  • B. Develop a Python script that runs on Amazon EC2 instances to convert the. csv files to sql files invoke the Python script on cron schedule to store the output files in Amazon S3.
  • C. Create an AWS Lambda function and an Amazon DynamoDB table. Use an S3 event to invoke the Lambda function. Configure the Lambda function to perform an extract transform, and load (ETL) job to process the .csv files and store the processed data in the DynamoDB table.
  • D. Create a AWS Glue extract, transform, and load (ETL) job that runs on a schedule. Configure the ETL job to process the .csv files and store the processed data in Amazon Redshit.

Answer: D

Explanation:
This solution meets the requirements of implementing a solution so that the COTS application can use the data that the legacy application produces with the least operational overhead. AWS Glue is a fully managed service that provides a serverless ETL platform to prepare and load data for analytics. AWS Glue can process data in various formats, including .csv files, and store the processed data in Amazon Redshift, which is a fully managed data warehouse service that supports complex SQL queries. AWS Glue can run ETL jobs on a schedule, which can automate the data processing and loading process.
Option B is incorrect because developing a Python script that runs on Amazon EC2 instances to convert the .csv files to sql files can increase the operational overhead and complexity, and it may not provide consistent data processing and loading for the COTS application. Option C is incorrect because creating an AWS Lambda function and an Amazon DynamoDB table to process the .csv files and store the processed data in the DynamoDB table does not meet the requirement of using Amazon Redshift as the data source for the COTS application. Option D is incorrect because using Amazon EventBridge (Amazon CloudWatch Events) to launch an Amazon EMR cluster on a weekly schedule to process the .csv files and store the processed data in an Amazon Redshift table can increase the operational overhead and complexity, and it may not provide timely data processing and loading for the COTS application.
Reference:
https://aws.amazon.com/glue/
https://aws.amazon.com/redshift/


NEW QUESTION # 356
A company has an Amazon S3 data lake The company needs a solution that transforms the data from the data lake and loads the data into a data warehouse every day The data warehouse must have massively parallel processing (MPP) capabilities.
Data analysts then need to create and train machine learning (ML) models by using SQL commands on the data The solution must use serverless AWS services wherever possible Which solution will meet these requirements?

  • A. Run a daily AWS Glue job to transform the data and load the data into Amazon Athena tables Use Amazon Athena ML to create and train the ML models
  • B. Run a daily Amazon EMR job to transform the data and load the data into Amazon Aurora Serverless Use Amazon Aurora ML to create and train the ML models
  • C. Run a daily AWS Glue job to transform the data and load the data into Amazon Redshift Serverless Use Amazon Redshift ML to create and tram the ML models
  • D. Run a daily Amazon EMR job to transform the data and load the data into Amazon Redshift Use Amazon Redshift ML to create and train the ML models

Answer: C

Explanation:
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.
Reference:
AWS Glue
Amazon Redshift
Amazon Redshift Serverless
Amazon Redshift ML
Amazon EMR
Amazon Aurora Serverless
Amazon Athena


NEW QUESTION # 357
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. Use an Amazon DynamoDB table to store customer session information
  • B. Use AWS Systems Manager Application Manager in the application to manage user session information
  • C. Deploy an Amazon Cognito user pool to manage user session information
  • D. Deploy an Amazon ElastiCache for Redis cluster to store customer session information
  • E. Turn on the sticky sessions feature (session affinity) on the ALB

Answer: D,E

Explanation:
Explanation
https://aws.amazon.com/caching/session-management/


NEW QUESTION # 358
A company wants to run big data workloads on Amazon EMR. The workloads need to process terabytes of data in memory.
A solutions architect needs to identify the appropriate EMR cluster instance configuration for the workloads.
Which solution will meet these requirements?

  • A. Use a general purpose instance for the primary node. Use memory optimized instances for core nodes and task nodes.
  • B. Use a storage optimized instance for the primary node. Use compute optimized instances for core nodes and task nodes.
  • C. Use general purpose instances for the primary, core, and task nodes.
  • D. Use a memory optimized instance for the primary node. Use storage optimized instances for core nodes and task nodes.

Answer: A

Explanation:
Big data workloads that need to process terabytes of data in memory require memory-optimized instances for the core and task nodes to ensure sufficient memory for processing data efficiently.
* Primary Node: A general purpose instance is suitable because it manages cluster operations, including coordination and monitoring, and does not process data directly.
* Core and Task Nodes: These nodes handle data storage and processing. Memory-optimized instances are ideal because they provide high memory-to-CPU ratios, which is critical for in-memory big data workloads.
Why Other Options Are Incorrect:
* Option A: Storage optimized and compute optimized instances are not suitable for workloads that rely heavily on in-memory processing.
* Option B: A memory-optimized primary node is unnecessary because the primary node does not process data.
* Option D: General purpose instances for all nodes will not provide sufficient memory for processing terabytes of data in memory.
AWS Documentation References:
* Amazon EMR Instance Types
* Memory-Optimized Instances


NEW QUESTION # 359
A company has migrated an application to Amazon EC2 Linux instances. One of these EC2 instances runs several 1-hour tasks on a schedule. These tasks were written by different teams and have no common programming language. The company is concerned about performance and scalability while these tasks run on a single instance. A solutions architect needs to implement a solution to resolve these concerns.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Convert the EC2 instance to a container. Use AWS App Runner to create the container on demand to run the tasks as jobs.
  • B. Copy the tasks into AWS Lambda functions. Schedule the Lambda functions by using Amazon EventBridge (Amazon CloudWatch Events).
  • C. Use AWS Batch to run the tasks as jobs. Schedule the jobs by using Amazon EventBridge (Amazon CloudWatch Events).
  • D. Create an Amazon Machine Image (AMI) of the EC2 instance that runs the tasks. Create an Auto Scaling group with the AMI to run multiple copies of the instance.

Answer: C

Explanation:
AWS Batch is a fully managed service that enables users to run batch jobs on AWS. It can handle different types of tasks written in different languages and run them on EC2 instances. It also integrates with Amazon EventBridge (Amazon CloudWatch Events) to schedule jobs based on time or event triggers. This solution will meet the requirements of performance, scalability and low operational overhead12.
1. Convert the EC2 instance to a container. Use AWS App Runner to create the container on demand to run the tasks as jobs. This solution will not meet the requirement of low operational overhead, as it involves converting the EC2 instance to a container and using AWS App Runner, which is a service that automatically builds and deploys web applications and load balances traffic2. This is not necessary for running batch jobs.
2. Copy the tasks into AWS Lambda functions. Schedule the Lambda functions by using Amazon EventBridge (Amazon CloudWatch Events). This solution will not meet the requirement of performance, as AWS Lambda has a limit of 15 minutes for execution time and 10 GB for memory allocation3. These limits may not be sufficient for running 1-hour tasks.
3. Create an Amazon Machine Image (AMI) of the EC2 instance that runs the tasks. Create an Auto Scaling group with the AMI to run multiple copies of the instance. This solution will not meet the requirement of low operational overhead, as it involves creating and maintaining AMIs and Auto Scaling groups, which are additional resources that need to be configured and managed2.
Reference URL: https://docs.aws.amazon.com/whitepapers/latest/aws-overview/compute-services.html


NEW QUESTION # 360
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