Amazon AWS Certified Solutions Architect – Associate SAA-C03 Exam Dumps and Practice Test Questions Set 4 Q61-80

Visit here for our full Amazon AWS Certified Solutions Architect – Associate SAA-C03 exam dumps and practice test questions.

Question 61:

A company wants to host a multi-region, highly available web application with minimal latency for users across the globe. Which combination of services should be recommended?

Answer:

A) Amazon Route 53 with latency-based routing, CloudFront, and EC2 instances across multiple regions
B) Single EC2 instance in one region
C) S3 hosting only
D) AWS Direct Connect

Explanation:

Option A is correct. Multi-region deployment ensures high availability and resilience against regional failures. Route 53 provides DNS-based routing strategies, including latency-based routing, which directs users to the AWS region that provides the lowest latency, improving user experience. CloudFront further reduces latency by caching static and dynamic content at edge locations globally. Deploying EC2 instances in multiple regions ensures redundancy, fault tolerance, and the ability to serve localized content efficiently.

A single EC2 instance represents a single point of failure and cannot deliver low-latency global access. S3 alone supports static content but cannot handle dynamic web applications or application logic. AWS Direct Connect provides private connectivity but does not improve global latency or availability.

Latency-based routing in Route 53 evaluates the latency from the user’s location to AWS regions in real-time and directs traffic accordingly. CloudFront integrates with origin EC2 instances or S3 buckets, caching static assets at edge locations, reducing repeated requests to the origin. This combination also supports HTTPS, signed URLs, and integration with AWS WAF for security against attacks.

Deploying EC2 instances across multiple regions also enables disaster recovery strategies, with cross-region replication of data via services like RDS Multi-AZ, S3 Cross-Region Replication, or DynamoDB Global Tables. CloudWatch monitoring and alarms can be configured to observe performance, health, and scaling metrics, enabling automated failover and proactive maintenance.

The architecture also supports cost optimization by offloading content delivery to CloudFront, reducing cross-region data transfer costs and scaling automatically without overprovisioning compute resources. Operational complexity is managed through infrastructure as code using CloudFormation or Terraform, ensuring consistent deployments across regions.

This design aligns with AWS Well-Architected Framework principles, providing reliability, operational excellence, performance efficiency, and security. It is ideal for e-commerce platforms, SaaS applications, and globally distributed services, delivering low-latency, highly available, and resilient user experiences worldwide.

Question 62:

A company wants to process large volumes of streaming IoT data in real-time, trigger events, and store results in a highly available data store. Which AWS services should be used together?

Answer:

A) Amazon Kinesis Data Streams, Lambda, and DynamoDB
B) S3 only
C) EC2 with cron jobs
D) RDS Multi-AZ

Explanation:

Option A is correct. Kinesis Data Streams captures streaming IoT data in real-time, partitioning it across shards for parallel processing. Lambda can process incoming data immediately, applying transformations, filtering, or enrichment, and then store processed results in DynamoDB for high-availability, low-latency access.

S3 alone is suitable for batch storage but does not provide real-time processing. EC2 with cron jobs introduces latency and operational overhead, lacking scalability for high-volume streams. RDS Multi-AZ provides a relational database but is not optimized for rapid ingestion of streaming data and may require sharding or partitioning for large-scale workloads.

Kinesis ensures durability by storing incoming records for a configurable retention period, allowing retries or reprocessing. Lambda scales automatically with stream volume, handling varying workloads efficiently without server management. DynamoDB provides single-digit millisecond latency, automatic scaling, and seamless integration with Lambda for serverless architecture.

Security is enforced using IAM roles for fine-grained permissions, ensuring only authorized services can interact with the stream or the database. CloudWatch metrics monitor throughput, latency, and processing errors. Dead-letter queues capture failed events for investigation, ensuring reliability.

This architecture supports fault tolerance, elasticity, and operational simplicity, following AWS best practices for IoT and event-driven applications. By using managed services, organizations minimize operational overhead while maintaining high performance and reliability. Additional integrations with SNS or SQS allow further event-driven workflows, alerts, or downstream processing.

The system provides scalability for millions of events per second, ensures near-real-time insights, supports automated response to anomalies, and enables efficient storage and querying of processed IoT data for analytics or dashboards. This design balances cost, performance, and availability, making it ideal for smart devices, telemetry, or industrial IoT solutions.

Question 63:

A company wants to implement serverless data processing for files uploaded to S3, ensuring automated transformations and storage of results. Which combination of services should be recommended?

Answer:

A) S3, Lambda, and DynamoDB
B) EC2, RDS, and S3
C) Elastic Beanstalk and RDS
D) S3 only

Explanation:

Option A is correct. S3 can trigger Lambda functions on object creation events, enabling automated processing, transformation, and validation. Lambda functions can then store results in DynamoDB for fast, highly available, and scalable access.

EC2, RDS, and S3 would require provisioning, scaling, monitoring, and maintenance, increasing operational complexity. Elastic Beanstalk relies on managed EC2 instances, not serverless computing. S3 alone stores objects but does not process or transform them.

This serverless architecture eliminates server management, automatically scales with the number of uploaded files, and charges only for actual execution time of Lambda. Lambda integrates with CloudWatch for logging and monitoring, enabling visibility into function execution, errors, and performance.

DynamoDB provides low-latency storage and scales seamlessly to handle large volumes of processed data. Security best practices include using IAM roles to allow Lambda access only to the required S3 buckets and DynamoDB tables. Versioning and encryption at rest ensure compliance and data integrity.

The architecture is ideal for ETL pipelines, media processing, analytics workflows, or automated document handling. Integration with Step Functions can orchestrate complex workflows, retries, and error handling. CloudWatch Alarms and SNS notifications allow proactive monitoring and operational visibility.

By combining S3, Lambda, and DynamoDB, organizations achieve a fully serverless, resilient, and scalable solution for file-based processing. This design aligns with AWS Well-Architected principles, optimizing cost, operational efficiency, and performance while maintaining security, compliance, and reliability.

Question 64:

A company wants to provide secure access to sensitive data stored in S3 for multiple applications with minimal management overhead. Which combination of services is most appropriate?

Answer:

A) S3 with IAM policies, bucket policies, and SSE-KMS encryption
B) S3 Standard without encryption
C) Public S3 buckets
D) EC2 instances

Explanation:

Option A is correct. SSE-KMS encrypts data at rest, ensuring confidentiality and compliance with regulations. IAM policies enforce fine-grained access to users and roles, and bucket policies provide additional controls for cross-account or application-specific access.

S3 Standard without encryption leaves data vulnerable. Public buckets expose sensitive data to unauthorized users. EC2 instances require manual access management and encryption configuration, increasing operational complexity.

Using SSE-KMS, administrators can manage keys centrally, rotate them automatically, and monitor usage through CloudTrail. IAM policies define precise permissions, adhering to the principle of least privilege. Bucket policies can enforce encryption requirements, deny public access, and restrict actions based on conditions like IP address or MFA authentication.

CloudWatch integration allows monitoring of API calls, storage usage, and access patterns. This architecture supports regulatory compliance such as HIPAA, PCI DSS, and GDPR. Lifecycle policies, versioning, and MFA delete further enhance security and durability.

By combining S3, IAM, bucket policies, and SSE-KMS, organizations achieve a secure, highly available, and manageable data storage solution. This approach reduces operational overhead, improves security posture, and ensures data protection across multiple applications or departments. Automated monitoring and centralized key management streamline governance, auditing, and operational excellence.

The most appropriate combination of services for providing secure access to sensitive data stored in Amazon S3 for multiple applications with minimal management overhead is S3 with IAM policies, bucket policies, and server-side encryption using AWS Key Management Service (SSE-KMS). SSE-KMS ensures that data at rest is encrypted using keys managed by AWS, providing confidentiality, compliance, and the ability to rotate keys automatically. IAM policies allow administrators to define fine-grained permissions for individual users, groups, or roles, ensuring that only authorized entities can access the data. Bucket policies offer additional access controls, enabling administrators to restrict access across accounts, enforce encryption requirements, deny public access, or require conditions such as IP address restrictions or multi-factor authentication. This combination reduces management complexity while maintaining strong security controls.

Option B, S3 Standard without encryption, exposes sensitive data to potential risks because it does not provide protection at rest. Without encryption, data confidentiality is compromised, and organizations may fail to meet compliance requirements. Option C, public S3 buckets, is even more insecure, as it allows anyone on the internet to access the stored data, which is unsuitable for sensitive or regulated information. Option D, using EC2 instances, requires manual management of access, encryption, and patching, increasing operational overhead and the likelihood of misconfiguration.

Using SSE-KMS, administrators can centrally manage encryption keys, monitor their usage through AWS CloudTrail, and enforce automated key rotation. Integration with CloudWatch enables monitoring of API calls, access patterns, and storage usage, providing real-time visibility into security and operational metrics. Additional S3 features such as lifecycle policies, versioning, and MFA delete further enhance data durability and protection. This architecture aligns with regulatory requirements such as HIPAA, PCI DSS, and GDPR, ensuring that sensitive data is securely managed across multiple applications or departments.

By combining S3, IAM policies, bucket policies, and SSE-KMS, organizations achieve a highly secure, scalable, and manageable storage solution. The approach minimizes operational overhead, improves security posture, ensures data confidentiality, and provides centralized governance and auditing capabilities. It allows multiple applications to access sensitive data safely while maintaining compliance and operational efficiency, demonstrating best practices for secure cloud storage on AWS.

Question 65:

A company wants to distribute dynamic and static content globally with low latency, DDoS protection, and HTTPS encryption. Which solution is optimal?

Answer:

A) CloudFront with S3/EC2 origin, WAF, and HTTPS
B) S3 only
C) EC2 in one region
D) Direct Connect

Explanation:

Option A is correct. CloudFront caches static content and accelerates dynamic content using global edge locations. HTTPS ensures encryption in transit, protecting sensitive data. AWS WAF provides DDoS protection and shields applications from common web exploits.

S3 alone cannot handle dynamic content or global acceleration. EC2 in one region increases latency for distant users. Direct Connect is private connectivity but does not provide CDN, caching, or DDoS protection.

CloudFront supports caching strategies, cache invalidation, and Lambda@Edge for content personalization or request modification at the edge. Origin failover ensures availability if the primary source fails. CloudWatch metrics provide insights into request patterns, errors, and latency.

This architecture improves user experience, reduces load on origin servers, and minimizes operational complexity. Integration with WAF and Shield Advanced enhances security posture against sophisticated attacks. By combining managed services, organizations can deliver a secure, low-latency, globally available application efficiently, aligning with AWS best practices for performance, reliability, and security.

The optimal solution for distributing both dynamic and static content globally with low latency, DDoS protection, and HTTPS encryption is Amazon CloudFront with S3 or EC2 as the origin, integrated with AWS WAF and HTTPS. CloudFront is a content delivery network that caches static content at edge locations worldwide, allowing users to access data from locations geographically close to them, which reduces latency and improves the overall user experience. For dynamic content, CloudFront forwards requests to the origin servers while optimizing the delivery path to minimize response times. HTTPS ensures that all data in transit is encrypted, protecting sensitive information from interception or tampering. AWS WAF adds a layer of security by providing protection against common web exploits, SQL injections, and cross-site scripting attacks, while also supporting DDoS mitigation when integrated with AWS Shield Advanced.

Option B, using S3 alone, is insufficient because while S3 can host static content, it does not provide global acceleration, caching at edge locations, or dynamic content optimization. Users located far from the S3 bucket region may experience high latency, and S3 alone cannot handle security features such as DDoS protection or advanced request filtering. Option C, deploying EC2 instances in a single region, introduces latency for users located far from the deployment region and lacks built-in mechanisms for edge caching or global distribution. Scaling EC2 to meet global demand increases operational complexity and cost. Option D, AWS Direct Connect, provides private network connectivity between on-premises environments and AWS but does not serve as a content delivery network, cannot cache content, and does not provide DDoS mitigation or HTTPS encryption for public users.

CloudFront also supports caching strategies, cache invalidation, and Lambda@Edge, which allows developers to modify requests and responses at edge locations for personalization or request routing. Origin failover capabilities ensure high availability in case the primary origin becomes unavailable. CloudWatch metrics provide visibility into request volumes, latency, error rates, and cache efficiency, enabling operational monitoring and performance tuning. This architecture reduces load on origin servers, enhances security, minimizes latency, and simplifies operational management. By combining CloudFront, WAF, Shield Advanced, and HTTPS, organizations can deliver a globally available, secure, and low-latency application that follows AWS best practices for performance, reliability, and security.

Question 66:

A company wants to provide a multi-region, highly available database solution that supports both read and write operations with minimal latency. Which AWS service is most appropriate?

Answer:

A) Amazon DynamoDB Global Tables
B) RDS Multi-AZ
C) Single RDS instance
D) S3

Explanation:

Option A is correct. DynamoDB Global Tables replicate data across multiple AWS regions automatically, supporting both read and write operations with low latency for globally distributed applications. This allows applications to read and write data from the nearest region, improving performance and availability.

RDS Multi-AZ provides high availability and automatic failover but is limited to a primary region for writes; cross-region reads require read replicas, which adds complexity and potential replication lag. A single RDS instance introduces a single point of failure. S3 is object storage and does not provide database capabilities.

Global Tables automatically handle data replication and conflict resolution between regions, maintaining consistency and durability. Each region operates independently, supporting continuous read and write operations. DynamoDB supports fine-grained access control with IAM policies, ensuring security across regions.

CloudWatch provides metrics to monitor throughput, latency, and errors. DynamoDB Accelerator (DAX) can be added for further performance improvements by caching frequently accessed data, reducing response times to microseconds. Automated backups and point-in-time recovery protect against accidental deletion or corruption, while encryption with KMS ensures data confidentiality.

This architecture is ideal for applications requiring global scale, such as e-commerce, gaming, or IoT platforms. By leveraging DynamoDB Global Tables, organizations achieve high availability, low latency, and seamless multi-region operations with minimal administrative overhead. This design aligns with AWS Well-Architected Framework principles, including operational excellence, reliability, performance efficiency, security, and cost optimization.

Question 67:

A company wants to migrate its legacy on-premises data warehouse to AWS and enable fast analytical queries with minimal administrative overhead. Which service combination is recommended?

Answer:

A) Amazon Redshift with Spectrum and S3
B) RDS Multi-AZ
C) DynamoDB
D) S3 only

Explanation:

Option A is correct. Redshift is a fully managed data warehouse designed for large-scale analytical workloads. Redshift Spectrum enables querying data stored directly in S3 without loading it into the Redshift cluster, reducing storage costs and operational complexity.

RDS is optimized for transactional workloads, not analytics. DynamoDB is NoSQL and unsuitable for complex analytical queries. S3 alone does not provide query capabilities or analytics features.

Redshift provides columnar storage, compression, and massively parallel processing (MPP), ensuring high performance for complex queries. Features like concurrency scaling and automatic workload management allow handling variable query loads efficiently. Security is enforced via VPC isolation, IAM, KMS encryption, and audit logging via CloudTrail.

Using Redshift Spectrum allows a hybrid approach: frequently queried data resides in Redshift, while historical or infrequently queried data stays in S3. This optimizes cost and performance while maintaining flexibility. CloudWatch monitoring and automated snapshots reduce administrative overhead. Integration with BI tools like QuickSight provides interactive dashboards and visual analytics.

Organizations can automate ETL processes using AWS Glue to prepare data for analysis. Redshift’s workload management queues ensure priority queries do not impact critical operations. Partitioning and distribution strategies improve performance for complex analytical workloads.

This architecture provides a secure, highly available, and cost-efficient solution for large-scale analytics. Organizations achieve scalability, operational simplicity, and fast query performance without managing servers or clusters manually. By leveraging Redshift and Spectrum, enterprises can modernize legacy data warehouses and support business intelligence at a global scale.

Question 68:

A company wants to implement a highly available, fault-tolerant application with multiple layers: web, application, and database. Which architecture is most suitable?

Answer:

A) Multi-AZ EC2 deployment with Auto Scaling, ALB, and RDS Multi-AZ
B) Single EC2 instance with EBS
C) S3 static hosting only
D) Lambda only

Explanation:

Option A is correct. Multi-AZ deployment ensures that EC2 instances in multiple Availability Zones can withstand failures in one AZ. Auto Scaling adjusts the number of instances based on load, providing elasticity. An Application Load Balancer distributes traffic across healthy instances, improving availability and performance. RDS Multi-AZ provides a highly available relational database with automatic failover.

A single EC2 instance creates a single point of failure. S3 hosting supports only static websites. Lambda is serverless and suitable for stateless applications, not multi-tier web applications requiring relational databases.

This architecture also supports security best practices: private subnets for EC2 and RDS, IAM roles, security groups, encryption at rest, and CloudTrail logging. CloudWatch provides monitoring for performance, availability, and alarms for operational incidents. Auto Scaling reduces operational overhead and ensures fault tolerance.

Additionally, this setup supports disaster recovery planning: automated snapshots, cross-region backups, and read replicas improve resilience. CloudFormation or Terraform can manage deployment as infrastructure as code, ensuring consistency. This design aligns with AWS Well-Architected Framework for operational excellence, reliability, performance efficiency, cost optimization, and security.

Organizations benefit from reduced downtime, high performance, and predictable scaling while focusing on application development rather than infrastructure management. Integration with CloudFront can further optimize global content delivery, providing low-latency access to users worldwide.

The most suitable architecture for implementing a highly available, fault-tolerant application with multiple layers—web, application, and database—is a multi-AZ EC2 deployment with Auto Scaling, an Application Load Balancer (ALB), and RDS Multi-AZ. Deploying EC2 instances across multiple Availability Zones ensures that the application can continue operating even if one AZ becomes unavailable. Auto Scaling dynamically adjusts the number of running instances based on real-time demand, providing elasticity and cost efficiency while maintaining performance under varying traffic loads. The ALB distributes incoming traffic across healthy instances, ensuring even load distribution and minimizing the risk of downtime due to a single point of failure. RDS Multi-AZ provides a highly available relational database that automatically fails over to a standby instance in another AZ if the primary instance becomes unavailable, further enhancing fault tolerance.

Option B, using a single EC2 instance with EBS, creates a single point of failure, making it unsuitable for applications that require high availability. Option C, S3 static hosting, supports only static websites and cannot run multi-tier applications or relational databases. Option D, Lambda only, is ideal for serverless and stateless applications but does not support multi-tier architectures requiring persistent storage and relational database connectivity.

This architecture also adheres to security best practices. EC2 and RDS instances can reside in private subnets, limiting exposure to the public internet. Security groups and IAM roles enforce least-privilege access, while encryption at rest protects sensitive data stored in EBS and RDS. CloudTrail provides logging for auditing and compliance, and CloudWatch enables monitoring of application performance, resource utilization, and alarms for operational incidents. Auto Scaling reduces operational overhead by maintaining the desired instance count without manual intervention.

Disaster recovery and resilience are enhanced through automated snapshots, cross-region backups, and read replicas for the database. Infrastructure-as-code tools like CloudFormation or Terraform allow consistent deployment across environments. This architecture aligns with the AWS Well-Architected Framework by promoting operational excellence, reliability, performance efficiency, security, and cost optimization. Organizations benefit from predictable scaling, minimal downtime, high performance, and the ability to focus on application development rather than managing infrastructure. Integrating CloudFront can further optimize global content delivery, ensuring low-latency access for end users worldwide.

Question 69:

A company wants to centralize logging from multiple AWS accounts and regions while enabling analytics and monitoring. Which solution is recommended?

Answer:

A) CloudWatch Logs with cross-account and cross-region aggregation
B) S3 only
C) EC2 instances only
D) Direct Connect

Explanation:

Option A is correct. CloudWatch Logs allows aggregation of logs from multiple AWS accounts and regions into a centralized account, supporting analytics, monitoring, and alerting. Logs can be analyzed in near real-time using CloudWatch Logs Insights.

S3 only stores logs but does not provide real-time analytics or alerting. EC2 instances would require manual log aggregation and processing. Direct Connect provides network connectivity but does not support logging.

Cross-account aggregation ensures logs from all accounts are accessible centrally. CloudWatch Logs Insights enables querying logs for operational or security events. Alarms can trigger notifications via SNS for critical events. Encryption via KMS ensures log confidentiality, and retention policies optimize storage costs.

This architecture provides observability, operational intelligence, and compliance across complex AWS environments. It supports automated troubleshooting, security auditing, and business reporting. By leveraging managed services, organizations reduce operational overhead while maintaining high visibility and governance.

Integration with Lambda allows automated responses to detected events, enhancing operational efficiency. Organizations can visualize logs using Amazon QuickSight or integrate with third-party SIEM solutions. This approach ensures operational excellence, security, and compliance in multi-account AWS environments.

Question 70:

A company wants to implement real-time alerts when objects in S3 meet specific criteria. Which AWS services should be used together?

Answer:

A) S3, Lambda, and SNS
B) EC2, S3, and CloudWatch
C) RDS only
D) S3 only

Explanation:

Option A is correct. S3 can trigger Lambda functions on object creation or modification events. Lambda can analyze object metadata or content and then publish alerts via SNS to notify stakeholders immediately.

EC2 requires manual monitoring and processing logic. RDS does not interact with object storage events. S3 alone cannot trigger alerts.

This serverless approach ensures automated, scalable, and cost-efficient event processing. Lambda scales automatically based on event volume. SNS supports multiple notification channels such as email, SMS, or HTTP endpoints. Security is enforced with IAM roles for Lambda access to S3 and SNS. CloudWatch logs provide observability into Lambda execution, errors, and performance.

Use cases include automated compliance monitoring, image processing notifications, or sensitive file detection. Organizations can build workflows that respond immediately to business-critical events without manual intervention. Integration with Step Functions can orchestrate complex alerting and remediation processes.

This architecture reduces operational overhead, ensures reliability, and provides near-real-time notifications, aligning with AWS best practices for serverless event-driven architectures.

Question 71:

A company wants to ensure low-latency access to frequently requested data stored in DynamoDB while reducing costs. Which solution should be implemented?

Answer:

A) DynamoDB Accelerator (DAX)
B) DynamoDB alone
C) RDS Multi-AZ
D) S3

Explanation:

Option A is correct. DAX provides an in-memory caching layer for DynamoDB, reducing read latency to microseconds. This improves performance for read-heavy workloads and reduces operational costs by offloading read requests from DynamoDB.

DynamoDB alone has single-digit millisecond latency but may not meet ultra-low latency requirements for certain applications. RDS is a relational database and unsuitable for serverless NoSQL workloads. S3 is object storage, not a database.

DAX integrates seamlessly with DynamoDB without application code changes. It supports high availability with replication across multiple nodes and automatic failover. Security is enforced through IAM and encryption, and CloudWatch monitors cache metrics such as hits, misses, and latency.

This architecture benefits applications such as gaming leaderboards, session storage, or real-time analytics. It aligns with AWS Well-Architected principles for performance, cost optimization, and operational excellence.

The optimal solution for ensuring low-latency access to frequently requested data stored in DynamoDB while reducing costs is to implement DynamoDB Accelerator (DAX). DAX is a fully managed, in-memory caching service that sits in front of DynamoDB, providing microsecond response times for read-heavy workloads. By caching frequently accessed items, DAX reduces the number of read requests sent directly to DynamoDB, which lowers operational costs and improves overall performance. This is particularly beneficial for applications that require ultra-low latency, such as gaming leaderboards, real-time analytics, and session management systems.

Option B, using DynamoDB alone, provides single-digit millisecond latency for reads and writes, which is sufficient for many applications. However, for workloads that require microsecond response times or experience heavy read traffic, relying solely on DynamoDB can result in higher costs and potential performance bottlenecks. Option C, RDS Multi-AZ, is a relational database service designed for transactional workloads and does not support the serverless NoSQL patterns that DynamoDB applications require. It cannot match the scalability, low-latency access, or cost efficiency needed for high-throughput, read-intensive DynamoDB workloads. Option D, S3, is object storage rather than a database and is unsuitable for scenarios requiring frequent, low-latency reads or transactional access patterns.

DAX integrates seamlessly with DynamoDB and requires minimal changes to application code. It supports high availability by replicating data across multiple nodes and automatically failing over in the event of a node failure. Security is maintained through IAM policies and encryption at rest and in transit, ensuring that sensitive data remains protected. CloudWatch provides detailed monitoring of cache performance, including metrics such as cache hits, misses, and latency, allowing operators to optimize performance and troubleshoot issues effectively.

By implementing DAX, organizations can achieve a highly performant, cost-efficient architecture that offloads read traffic from DynamoDB while maintaining data consistency and scalability. This solution aligns with the AWS Well-Architected Framework, supporting principles of performance efficiency, cost optimization, and operational excellence. It enables applications to handle high read throughput with minimal latency, ensuring a responsive user experience and predictable performance even under demanding workloads.

Question 72:

A company wants to migrate a multi-terabyte on-premises MySQL database to AWS with minimal downtime. Which service combination is recommended?

Answer:

A) AWS DMS with RDS Multi-AZ MySQL
B) EC2 with self-managed MySQL
C) S3 only
D) DynamoDB

Explanation:

Option A is correct. AWS Database Migration Service (DMS) enables near-zero downtime migrations by continuously replicating changes from the source database. RDS Multi-AZ MySQL provides high availability and automated failover, ensuring reliability during the migration process.

EC2 with MySQL increases operational complexity, requiring manual replication, failover, and monitoring. S3 only is object storage, unsuitable for relational workloads. DynamoDB is NoSQL, incompatible with relational MySQL applications.

DMS supports validation, monitoring, and retry mechanisms. RDS Multi-AZ ensures data durability and availability. Security, encryption, and monitoring are managed through KMS, IAM, and CloudWatch. This combination minimizes downtime, simplifies migration, and supports scalability and high availability in AWS.

Question 73:

A company wants to provide secure access to an S3 bucket for multiple third-party applications without sharing AWS credentials. Which solution is most appropriate?

Answer:

A) Pre-signed URLs
B) Public S3 bucket
C) IAM user credentials shared with third parties
D) S3 Standard only

Explanation:

Option A is correct. Pre-signed URLs allow temporary, secure access to specific objects in S3 without exposing AWS credentials. Each URL has an expiration time, after which it becomes invalid, preventing unauthorized access.

Public S3 buckets are unsafe, exposing data to anyone. Sharing IAM credentials is a significant security risk, violating the principle of least privilege. S3 Standard only defines storage class, not access control.

Pre-signed URLs integrate seamlessly with applications and workflows. For instance, a web application can generate a pre-signed URL when a user requests access to a file. Lambda or API Gateway can generate these URLs programmatically. Access is audited via CloudTrail, providing traceability.

This approach supports compliance, minimizes operational overhead, and provides granular, temporary access. Combined with server-side encryption, versioning, and lifecycle policies, organizations can secure data while allowing controlled access for external applications.

By using pre-signed URLs, organizations maintain a high security posture, enforce access policies, and reduce operational risks, aligning with AWS Well-Architected principles for security, reliability, and operational excellence

Question 74:

A company wants to migrate its legacy batch processing application to AWS while minimizing operational overhead. Which architecture is most suitable?

Answer:

A) S3 for input/output storage, Lambda for processing, and Step Functions for orchestration
B) EC2 with cron jobs
C) S3 only
D) RDS only

Explanation:

Option A is correct. This architecture is fully serverless and automates batch processing workflows. S3 stores input files and processed outputs. Lambda processes each file or job in a scalable manner, and Step Functions orchestrate workflows, retries, and dependencies.

EC2 with cron jobs requires server management, scaling, and monitoring. S3 alone cannot process data. RDS is a relational database, not suitable for batch processing workloads.

Serverless architecture provides automatic scaling, fault tolerance, and cost efficiency, as you pay only for execution time. CloudWatch metrics provide monitoring and alerting. Lambda integrates with IAM for secure access to S3. Step Functions ensure reliable sequencing, error handling, and logging.

This solution simplifies operations, reduces infrastructure management, and ensures reliability and performance. Organizations can migrate legacy batch jobs to AWS while improving scalability, maintainability, and operational efficiency, adhering to AWS Well-Architected principles.

The most suitable architecture for migrating a legacy batch processing application to AWS while minimizing operational overhead is a serverless approach using S3 for input and output storage, Lambda for processing, and Step Functions for orchestration. In this architecture, S3 serves as a durable and scalable storage layer where input files or job data can be uploaded and processed results can be stored. AWS Lambda functions handle the execution of individual tasks or jobs, providing automatic scaling to accommodate varying workloads without the need for server provisioning. Step Functions orchestrate the workflow by defining the sequence of tasks, managing dependencies, and handling retries in the event of errors, ensuring reliable execution of batch processing pipelines.

Option B, using EC2 with cron jobs, introduces significant operational overhead because instances must be managed, patched, and scaled manually. Scaling workloads requires configuring Auto Scaling policies, and monitoring instance health and job execution is more complex. Option C, relying solely on S3, is insufficient as S3 is a storage service and cannot perform processing of data. Option D, using RDS only, is not appropriate for batch processing workloads because RDS is a relational database designed for transactional operations, not for orchestrating and processing large volumes of batch jobs.

The serverless architecture provides several operational advantages. It eliminates the need to manage servers, automatically scales to meet workload demands, and ensures fault tolerance. Organizations only pay for the compute time consumed by Lambda functions, improving cost efficiency compared with running constantly active EC2 instances. CloudWatch metrics can be used to monitor execution times, errors, and job completion rates, providing operational visibility and alerting for anomalies. IAM policies control access to S3 and Lambda, maintaining secure handling of input and output data. Step Functions also provide logging and tracking of workflow execution, which simplifies debugging and auditing of batch processes.

By leveraging S3, Lambda, and Step Functions, organizations can modernize legacy batch workloads while reducing infrastructure management and improving scalability. This approach enhances maintainability, operational efficiency, and reliability, aligning with AWS Well-Architected principles. It allows enterprises to focus on application logic rather than server management, while benefiting from automated retries, fault-tolerant execution, and cost-effective serverless compute resources.

Question 75:

A company wants to enforce encryption for all objects stored in S3 and audit access across multiple accounts. Which AWS services should be used together?

Answer:

A) S3 with SSE-KMS, AWS Config, and CloudTrail
B) S3 only
C) IAM policies alone
D) EC2 instances

Explanation:

Option A is correct. SSE-KMS ensures data at rest is encrypted using managed keys. AWS Config monitors S3 bucket configurations for compliance, and CloudTrail logs API calls, providing audit trails.

S3 alone cannot enforce organizational compliance. IAM policies provide access control but do not ensure encryption or logging. EC2 instances are irrelevant to object-level encryption.

Config rules can detect unencrypted buckets and trigger automated remediation. CloudTrail enables tracking of access events across multiple accounts. Combined, these services enforce encryption, provide operational visibility, and support compliance audits. Security best practices include restricting key usage with KMS key policies and ensuring least-privilege access via IAM.

This architecture minimizes operational risk, enhances security posture, and supports regulatory compliance, aligning with AWS Well-Architected Framework principles. Organizations achieve centralized encryption enforcement and logging while reducing administrative overhead.

Question 76:

A company wants to implement an event-driven workflow where files uploaded to S3 trigger multiple processing steps, including validation, transformation, and storage in a database. Which AWS architecture is recommended?

Answer:

A) S3 event notifications, Lambda functions for each processing step, and Step Functions for orchestration
B) EC2 with cron jobs
C) S3 only
D) RDS alone

Explanation:

Option A is correct. S3 generates events upon object creation, triggering Lambda functions. Each Lambda function performs a specific step, and Step Functions orchestrate the workflow, including retries and error handling.

EC2 requires manual management and scheduling. S3 alone cannot execute processing. RDS is a database, not a workflow orchestrator.

Serverless architecture ensures scalability, fault tolerance, and minimal operational overhead. Lambda functions scale with event volume and support modular, maintainable code. Step Functions provide visual workflow management, sequence tasks, handle exceptions, and enable logging.

Processed data can be stored in DynamoDB or RDS for persistent storage. Security is enforced through IAM roles for Lambda functions and encryption for S3 and database storage. CloudWatch metrics provide visibility into execution, errors, and performance.

This architecture is ideal for ETL pipelines, media processing, or automated compliance workflows. Automation reduces human error, improves operational efficiency, and aligns with AWS Well-Architected Framework principles, including operational excellence, reliability, performance efficiency, and security.

Question 77:

A company wants to deploy a web application that scales automatically in response to traffic spikes and uses relational data. Which architecture is most suitable?

Answer:

A) EC2 Auto Scaling group, ALB, and RDS Multi-AZ
B) Single EC2 instance with EBS
C) S3 static hosting only
D) DynamoDB only

Explanation:

Option A is correct. EC2 Auto Scaling ensures that the application layer scales based on load, while an Application Load Balancer distributes traffic across instances for high availability. RDS Multi-AZ provides a highly available relational database with automatic failover.

Single EC2 instances create a single point of failure. S3 is only suitable for static websites. DynamoDB is NoSQL and may not support relational queries needed by the application.

This architecture ensures fault tolerance, high availability, and performance efficiency. Auto Scaling allows dynamic scaling based on metrics such as CPU or request count. ALB integrates with CloudWatch for monitoring health and traffic. Security best practices include deploying instances in private subnets, using security groups, and enabling encryption for RDS.

Organizations benefit from reduced operational overhead, predictable scaling, and high reliability. This setup aligns with AWS Well-Architected Framework, supporting cost optimization, performance efficiency, and operational excellence.

Question 78:

A company wants to provide global low-latency access to static and dynamic content stored on AWS while securing it with HTTPS. Which solution is most appropriate?

Answer:

A) CloudFront with S3/EC2 origin and HTTPS
B) S3 only
C) EC2 in one region
D) Direct Connect

Explanation:

Option A is correct. CloudFront distributes content globally via edge locations, reducing latency for end users. It supports both static content from S3 and dynamic content from EC2 instances. HTTPS ensures encrypted communication for data in transit.

S3 alone cannot accelerate dynamic content. EC2 in one region increases latency for distant users. Direct Connect provides private connectivity but does not offer CDN functionality.

CloudFront supports caching strategies, Lambda@Edge for content personalization, signed URLs for secure access, and WAF integration for protection against attacks. Monitoring and metrics are provided via CloudWatch, enabling visibility into requests, cache hit ratios, and latency.

This architecture ensures low-latency delivery, high availability, and security. It minimizes load on origin servers, reduces operational complexity, and provides a scalable solution for global applications. It aligns with AWS best practices for performance efficiency, security, and operational excellence.

Question 79:

A company wants to automate the enforcement of tagging standards across multiple AWS accounts. Which service combination is recommended?

Answer:

A) AWS Config with AWS Organizations
B) CloudTrail only
C) EC2 instances only
D) S3 only

Explanation:

Option A is correct. AWS Config can monitor resources for compliance with organizational tagging policies. When combined with AWS Organizations, Config rules can be applied across multiple accounts, ensuring centralized governance.

CloudTrail logs API calls but cannot enforce tagging. EC2 and S3 alone do not provide centralized enforcement or auditing capabilities.

Config rules can automatically detect non-compliant resources and trigger remediation workflows via Lambda. Aggregators consolidate compliance status across accounts, and CloudWatch provides alerts for violations. This approach reduces manual auditing, enforces governance, and ensures adherence to organizational standards.

Organizations can maintain consistent metadata for cost allocation, automation, and operational visibility. Centralized enforcement aligns with AWS Well-Architected Framework principles, ensuring operational excellence, governance, and compliance across multi-account environments.

Question 80:

A company wants to analyze streaming data from IoT devices in near real-time and store processed results in a highly available, scalable database. Which solution is recommended?

Answer:

A) Amazon Kinesis Data Streams, Lambda, and DynamoDB
B) EC2 with cron jobs
C) S3 only
D) RDS Multi-AZ

Explanation:

Option A is correct. Kinesis captures streaming data from IoT devices, Lambda processes the data in real-time, and DynamoDB stores the results with low-latency access and high availability.

EC2 with cron jobs is not real-time and requires manual management. S3 is batch-oriented and not optimized for streaming. RDS Multi-AZ is relational and may not scale efficiently for high-velocity streams without additional complexity.

Kinesis shards allow parallel processing, ensuring throughput scalability. Lambda automatically scales with incoming data and handles error retries. DynamoDB provides high availability, single-digit millisecond latency, automatic scaling, and encryption with KMS.

CloudWatch monitoring captures metrics like processing latency, shard utilization, and Lambda invocation errors. Dead-letter queues capture failed events for inspection. This architecture supports fault tolerance, operational simplicity, and cost efficiency.

It is ideal for IoT analytics, real-time dashboards, anomaly detection, and automated alerting. By using managed services, organizations minimize operational overhead while maintaining scalability, reliability, and low-latency processing, aligning with AWS Well-Architected principles.