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Explore AWS Certificates

Unlock your cloud potential with AWS certifications. Click on a certificate to explore detailed exam resources and preparation tools designed for your success.

AWS Certified Cloud Practitioner

The AWS Certified Cloud Practitioner (CLF-C02) certification provides a foundational understanding of AWS Cloud concepts, services, and billing models. It is designed for individuals new to cloud computing, offering essential knowledge for various AWS roles and cloud-based projects.

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AWS Certified Solutions Architect - Associate

The AWS Certified Solutions Architect – Associate (SAA-C03) certification validates your ability to design secure, cost-effective, and scalable solutions using AWS services. It covers architectural best practices, cloud deployment, and AWS core services, making it ideal for individuals pursuing cloud architecture roles.

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AWS Certified Solutions Architect - Professional

The AWS Certified Solutions Architect – Professional (SAP-C02) certification demonstrates advanced expertise in designing complex, scalable, and secure solutions on AWS. It covers in-depth architectural strategies, cost optimization, and best practices for large-scale cloud deployments, ideal for experienced cloud architects.

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AWS Certified Developer – Associate

The AWS Certified Developer – Associate (DVA-C02) certification validates expertise in developing, testing, and deploying AWS-based applications. It is designed for developers with hands-on experience in AWS, focusing on essential services, application lifecycle management, and best practices for building secure, scalable, and cloud-native applications. This certification is ideal for individuals seeking to deepen their understanding of AWS development tools and services to support innovative cloud solutions.

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AWS Certified Cloud Practitioner

Sample Questions

Question #65 / 719

A company wants to identify IAM policies that grant excessive permissions beyond what is necessary for users and roles. Which AWS service will meet this requirement?

A

AWS Trusted Advisor

B

Amazon CloudWatch

C

Amazon GuardDuty

D

AWS Health Dashboard

Question #545 / 719

A company uses Amazon Aurora and wants to ensure that both the primary database instance and its automated backups are encrypted. According to the AWS shared responsibility model, which party is responsible for enabling encryption for the database instances and their backups?

A

AWS

B

The company

C

AWS Marketplace partners

D

Third-party partners

AWS Certified Solutions Architect - Associate

Sample Questions

Question #395 / 1019

An IAM user inadvertently modified the IAM role attached to a critical EC2 instance in their company's AWS account, causing application downtime. A solutions architect needs to identify which IAM user made the unauthorized role modification to prevent future incidents.

Which service should the solutions architect use to determine the responsible user?

A

Amazon GuardDuty

B

Amazon Inspector

C

AWS CloudTrail

D

AWS Config

Question #99 / 1019

A media production company requires a high-performance shared storage solution for their on-premises video rendering farm. The solution must support Lustre clients and be fully managed to minimize administrative overhead. Which option meets these requirements?

A

Deploy an AWS Storage Gateway volume gateway. Configure the gateway to use the Lustre protocol. Connect the rendering nodes to the gateway.

B

Provision an Amazon EC2 Linux instance, install and configure Lustre file system software manually. Mount the file system on the rendering nodes.

C

Create an Amazon Elastic File System (Amazon EFS) file system with Lustre compatibility enabled. Mount the file system on the on-premises servers.

D

Deploy an Amazon FSx for Lustre file system. Configure the on-premises servers to access the file system via Lustre clients.

AWS Certified Solutions Architect - Professional

Sample Questions

Question #18 / 529

A company operates a photo-sharing platform where users upload images. The images are stored on Amazon EBS volumes and analyzed by a third-party image recognition tool for tagging. The frontend is hosted on Amazon EC2 instances in an Auto Scaling group, and another set of EC2 instances processes an Amazon SQS queue for image analysis. The company wants to minimize operational overhead by using AWS managed services and eliminate third-party dependencies.

Which solution meets these requirements?

A

Use Amazon ECS containers for the frontend and Spot instances for the Auto Scaling group that processes the SQS queue. Replace the third-party tool with Amazon Rekognition for image tagging.

B

Store the uploaded images in Amazon EFS and mount the file system to the EC2 instances for the frontend. Process the SQS queue with an AWS Lambda function that calls the Amazon Rekognition API for image tagging.

C

Host the frontend in Amazon S3. Store the uploaded images in Amazon S3. Use S3 event notifications to publish events to the SQS queue. Process the SQS queue with an AWS Lambda function that calls the Amazon Rekognition API for image tagging.

D

Use AWS Elastic Beanstalk to launch EC2 instances in an Auto Scaling group for the frontend and launch a worker environment to process the SQS queue. Replace the third-party tool with Amazon Rekognition for image tagging.

Question #362 / 529

A company needs to monitor access patterns and object size distribution across Amazon S3 buckets in two AWS Regions to optimize storage costs. The company requires a centralized dashboard to visualize these metrics for the finance team. Which solution will meet these requirements with the LEAST operational overhead?

A

Deploy AWS Lambda functions in each Region to collect object size and access data. Store the data in Amazon DynamoDB and use Amazon QuickSight to build a custom dashboard with aggregated metrics for the finance team.

B

Create an Amazon EventBridge rule to capture S3 API events via AWS CloudTrail. Use a Lambda function to process the events and log metrics in Amazon CloudWatch. Build a CloudWatch dashboard to display the aggregated data.

C

Use the S3 Storage Lens default dashboard to track object size distribution and access patterns. Grant the finance team access to the dashboard directly in the S3 console.

D

Configure AWS Config rules in each Region to monitor S3 object sizes and access activity. Export the data to Amazon S3 and use Athena queries to generate reports for a custom QuickSight dashboard.

AWS Certified Developer – Associate

Sample Questions

Question #332 / 557

A company recently deployed a new serverless payment processing system. Users have reported latency during peak transaction times. The initial analysis identified a single Amazon API Gateway endpoint causing delays. The endpoint integrates with an AWS Lambda function, which communicates with multiple third-party payment gateways and internal AWS services.

How can a developer pinpoint the root cause of the latency using operational best practices?

A

Modify the Lambda function to include detailed logging statements with millisecond-level timestamps before and after each external API call. Deploy the updated Lambda function. After collecting sufficient log data, analyze the Amazon CloudWatch logs to identify the components contributing to the latency.

B

Instrument the Lambda function using the AWS X-Ray SDK. Configure HTTP/HTTPS interceptors and SDK client handlers. Deploy the updated Lambda function. Enable X-Ray tracing. After gathering enough data, analyze the X-Ray service map to review segment durations and identify latency sources.

C

Examine the Lambda function's Amazon CloudWatch metrics using the metrics dashboard. Apply anomaly detection to the Duration and ConcurrentExecutions metrics. Investigate the anomalies to determine potential causes of the latency.

D

Create an Amazon CloudWatch Synthetics canary with X-Ray tracing enabled. Configure the canary to simulate user transactions on the payment system. After collecting sufficient data, use the CloudWatch Synthetics dashboard to analyze latency metrics from the canary.

Question #419 / 557

A developer is deploying a serverless data processing application on AWS Lambda. The application requires a machine learning model file that is 600 MB to execute its core functionality. Which solution will meet these requirements?

A

Compress the application code and model file into a .zip file. Directly upload the .zip file as a deployment package for the Lambda function without external storage.

B

Compress the application code and model file into a .zip file. Upload the .zip file to an Amazon S3 bucket. Configure the Lambda function to reference the .zip file in S3 during execution.

C

Package the application code and model file into a container image. Upload the image to an Amazon S3 bucket. Configure the Lambda function to pull the image from S3 for deployment.

D

Package the application code and model file into a container image. Push the image to an Amazon Elastic Container Registry (Amazon ECR) repository. Deploy the image to the Lambda function.