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AWS Certified Solutions Architect - Associate Updated Torrent & SAA-C03 Training Vce & AWS Certified Solutions Architect - Associate Pdf Exam

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Amazon SAA-C03 (Amazon AWS Certified Solutions Architect - Associate) exam is a certification offered by Amazon Web Services (AWS) for individuals who want to validate their skills and knowledge in designing and deploying scalable, highly available, and fault-tolerant systems on AWS. AWS Certified Solutions Architect - Associate certification is designed for those who have experience in designing distributed applications and systems on the AWS platform using various AWS services and best practices.

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Amazon AWS Certified Solutions Architect - Associate Sample Questions (Q315-Q320):

NEW QUESTION # 315
A company recently launched a variety of new workloads on Amazon EC2 instances in its AWS account. The company needs to create a strategy to access and administer the instances remotely and securely. The company needs to implement a repeatable process that works with native AWS services and follows the AWS Well-Architected Framework.
Which solution will meet these requirements with the LEAST operational overhead?

A. Establish an AWS Site-to-Site VPN connection. Instruct administrators to use their local on-premises machines to connect directly to the instances by using SSH keys across the VPN tunnel.
B. Attach the appropriate IAM role to each existing instance and new instance. Use AWS Systems Manager Session Manager to establish a remote SSH session.
C. Use the EC2 serial console to directly access the terminal interface of each instance for administration.
D. Create an administrative SSH key pair. Load the public key into each EC2 instance. Deploy a bastion host in a public subnet to provide a tunnel for administration of each instance.

Answer: B

Explanation:
Explanation
https://docs.aws.amazon.com/systems-manager/latest/userguide/setup-launch-managed-instance.html

NEW QUESTION # 316
[Design Secure Architectures]
A company has several web servers that need to frequently access a common Amazon RDS MySQL Multi-AZ DB instance The company wants a secure method for the web servers to connect to the database while meeting a security requirement to rotate user credentials frequently.
Which solution meets these requirements?

A. Store the database user credentials in AWS Secrets Manager Grant the necessary IAM permissions to allow the web servers to access AWS Secrets Manager
B. Store the database user credentials in AWS Systems Manager OpsCenter Grant the necessary IAM permissions to allow the web servers to access OpsCenter
C. Store the database user credentials in a secure Amazon S3 bucket Grant the necessary IAM permissions to allow the web servers to retrieve credentials and access the database.
D. Store the database user credentials in files encrypted with AWS Key Management Service (AWS KMS) on the web server file system. The web server should be able to decrypt the files and access the database

Answer: A

Explanation:
AWS Secrets Manager helps you protect secrets needed to access your applications, services, and IT resources. The service enables you to easily rotate, manage, and retrieve database credentials, API keys, and other secrets throughout their lifecycle.
https://docs.aws.amazon.com/secretsmanager/latest/userguide/intro.html
Secrets Manager enables you to replace hardcoded credentials in your code, including passwords, with an API call to Secrets Manager to retrieve the secret programmatically. This helps ensure the secret can't be compromised by someone examining your code, because the secret no longer exists in the code. Also, you can configure Secrets Manager to automatically rotate the secret for you according to a specified schedule. This enables you to replace long-term secrets with short-term ones, significantly reducing the risk of compromise.

NEW QUESTION # 317
[Design Cost-Optimized Architectures]
A company uses Amazon S3 to store high-resolution pictures in an S3 bucket. To minimize application changes, the company stores the pictures as the latest version of an S3 object The company needs to retain only the two most recent versions ot the pictures.
The company wants to reduce costs. The company has identified the S3 bucket as a large expense.
Which solution will reduce the S3 costs with the LEAST operational overhead?

A. Deactivate versioning on the S3 bucket and retain the two most recent versions.
B. Use S3 Batch Operations to delete noncurrent object versions and retain only the two most recent versions
C. Use an AWS Lambda function to check for older versions and delete all but the two most recent versions
D. Use S3 Lifecycle to delete expired object versions and retain the two most recent versions.

Answer: D

Explanation:
S3 Lifecycle is a feature that allows you to automate the management of your S3 objects based on predefined rules. You can use S3 Lifecycle to delete expired object versions and retain the two most recent versions by creating a lifecycle configuration rule that applies to all objects in the bucket and specifies the expiration action for noncurrent versions. This way, you can reduce the storage costs of your S3 bucket without requiring any application changes or manual intervention. S3 Lifecycle runs once a day and marks the eligible object versions for deletion. You are no longer charged for the objects that are marked for deletion. S3 Lifecycle is the most cost-effective and simple solution among the options.
B . Use an AWS Lambda function to check for older versions and delete all but the two most recent versions. This option is not optimal because it requires you to write, test, and maintain a custom Lambda function that scans the S3 bucket for older versions and deletes them. This can incur additional costs for Lambda invocations and increase the operational complexity and overhead. Moreover, you need to ensure that your Lambda function has the appropriate permissions and error handling mechanisms to perform the deletion operation.
C . Use S3 Batch Operations to delete noncurrent object versions and retain only the two most recent versions. This option is not ideal because S3 Batch Operations is designed for performing large-scale operations on S3 objects, such as copying, tagging, restoring, or invoking a Lambda function. To use S3 Batch Operations to delete noncurrent object versions, you need to provide a manifest file that lists the object versions that you want to delete. This can be challenging and time-consuming to generate and update. Moreover, S3 Batch Operations charges you for each operation that you perform, which can increase your costs.
D . Deactivate versioning on the S3 bucket and retain the two most recent versions. This option is not feasible because deactivating versioning on an S3 bucket does not delete the existing object versions. Instead, it prevents new versions from being created. Therefore, you still need to delete the older versions manually or use another method to do so. Additionally, deactivating versioning can compromise the data protection and recovery capabilities of your S3 bucket.
Reference:
1 Considering four different replication options for data in Amazon S3 | AWS Storage Blog
2 Using AWS Lambda with Amazon S3 batch operations - AWS Lambda
3 Empty an Amazon S3 bucket with a lifecycle configuration rule
4 Amazon S3 - Lifecycle Management - GeeksforGeeks

NEW QUESTION # 318
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 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
B. 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
C. 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
D. 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

Answer: D

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 # 319
[Design Resilient Architectures]
A company wants to build a logging solution for its multiple AWS accounts. The company currently stores the logs from all accounts in a centralized account. The company has created an Amazon S3 bucket in the centralized account to store the VPC flow logs and AWS CloudTrail logs. All logs must be highly available for 30 days for frequent analysis, retained tor an additional 60 days tor backup purposes, and deleted 90 days after creation.
Which solution will meet these requirements MOST cost-effectively?

A. Transition objects to the S3 One Zone-Infrequent Access (S3 One Zone-IA) storage class 30 days after creation. Move all objects to the S3 Glacier Flexible Retrieval storage class after 90 days. Write an expiration action that directs Amazon S3 to delete objects after 90 days.
B. Transition objects lo the S3 Standard-Infrequent Access (S3 Standard-IA) storage class 30 days after creation Move all objects to the S3 Glacier FlexibleRetrieval storage class after 90 days. Write an expiration action that directs Amazon S3 to delete objects after 90 days.
C. Transition objects to the S3 Standard storage class 30 days after creation. Write an expiration action that directs Amazon S3 to delete objects after 90 days.
D. Transition objects to the S3 Glacier Flexible Retrieval storage class 30 days after creation. Write an expiration action that directs Amazon S3 to delete objects alter 90 days.

Answer: A

Explanation:
Understanding the Requirement: The company needs logs to be highly available for 30 days for frequent analysis, retained for an additional 60 days for backup, and deleted after 90 days.
Analysis of Options:
Transition to S3 Standard after 30 days: Keeps logs in the same high-availability storage, not cost-effective.
Transition to S3 Standard-IA, then Glacier Flexible Retrieval after 90 days: Adds unnecessary cost and complexity since objects need to be accessible for only 30 days and then retained for 60 days.
Transition to Glacier Flexible Retrieval after 30 days: Not suitable for frequent access required in the first 30 days.
Transition to S3 One Zone-IA after 30 days, then Glacier Flexible Retrieval: Provides cost-effective storage for infrequently accessed logs after the initial 30-day period, then moves to the cheapest long-term storage before deletion.
Best Solution:
Transition to S3 One Zone-IA after 30 days, then Glacier Flexible Retrieval: This solution meets the requirements for high availability, cost-effective storage for backup, and scheduled deletion with the least cost.
Reference:
Amazon S3 Storage Classes
Managing your storage lifecycle

NEW QUESTION # 320
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