Amazon AWS Certified Solutions Architect – Associate SAA-C03 Exam Dumps and Practice Test Questions Set 7 Q121-140

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Question 121:

A company wants to implement a highly available, multi-region web application using a relational database with minimal downtime during regional failures. Which architecture is most suitable?

Answer:

A) RDS Multi-AZ with Read Replicas in another region and Route 53 latency-based routing
B) Single RDS instance in one region
C) DynamoDB only
D) S3 static hosting

Explanation:

Option A is correct. RDS Multi-AZ ensures each database has a synchronous standby in the same region, automatically failing over during an Availability Zone failure. Read Replicas in another region provide multi-region availability and reduce read latency for global users. Route 53 latency-based routing directs traffic to the nearest healthy region, reducing response time and improving user experience.

A single RDS instance lacks high availability and regional fault tolerance. DynamoDB is NoSQL and does not support traditional relational SQL queries without redesigning the application. S3 is object storage and only suitable for static content.

RDS Multi-AZ supports automatic failover, patching, and backup, which ensures minimal operational downtime. Cross-region Read Replicas allow read-heavy workloads to scale globally. CloudWatch monitors instance health, replica lag, and performance metrics. CloudTrail provides audit logging. Security is enforced using IAM, KMS encryption for data at rest, and network access controls through security groups and VPCs.

This architecture reduces downtime, provides a global presence, and ensures a scalable, fault-tolerant solution. It aligns with the AWS Well-Architected Framework principles for reliability, operational excellence, security, and performance efficiency, while allowing organizations to maintain compliance, reduce administrative overhead, and support business continuity across regions.

Question 122:

A company needs to process high-volume streaming data from multiple IoT devices in near real-time and store the processed results in a scalable database. Which solution is recommended?

Answer:

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

Explanation:

Option A is correct. Kinesis Data Streams provides a scalable and durable ingestion mechanism for high-volume IoT data. Shards allow parallel processing for large throughput. Lambda automatically triggers upon new events, processes data in near real-time, and writes results to DynamoDB for low-latency access.

S3 supports batch processing but is not suitable for real-time data ingestion. EC2 batch jobs require manual scaling and cannot achieve near-real-time performance. RDS Multi-AZ is relational and may require complex partitioning or sharding to scale for high-velocity streams.

Kinesis ensures reliability, durability, and reprocessing in case of failures. Lambda scales automatically with demand and supports error handling with dead-letter queues. DynamoDB’s low-latency performance ensures fast queries and global replication for multi-region access.

CloudWatch monitors throughput, latency, and Lambda execution metrics, while CloudTrail provides audit logging for security and compliance. IAM roles enforce least-privilege access between services. Encryption via KMS protects sensitive data at rest and in transit.

This architecture enables real-time analytics, monitoring, anomaly detection, and automated alerts. By leveraging serverless and managed services, operational overhead is reduced, scalability is automatic, and fault tolerance is ensured. The architecture aligns with AWS Well-Architected Framework principles for operational excellence, performance efficiency, reliability, and security.

Question 123:

A company wants to automate EBS volume backups across multiple regions while reducing operational effort. Which AWS service combination should be used?

Answer:

A) Amazon Data Lifecycle Manager (DLM) with cross-region snapshot copy
B) Manual snapshots on each volume
C) EC2 instance backup scripts
D) S3 Standard only

Explanation:

Option A is correct. Amazon Data Lifecycle Manager automates the creation, retention, and deletion of EBS snapshots based on defined policies. Cross-region snapshot copy ensures backups are available in multiple regions for disaster recovery and compliance.

Manual snapshots require manual intervention, risk errors, and lack automation for retention. EC2 scripts require management and monitoring. S3 alone cannot snapshot EBS volumes.

DLM allows policy-based automation including schedule, retention rules, and cross-region replication. Snapshots are incremental, reducing storage costs, and all snapshots are encrypted using KMS for security. CloudWatch monitors snapshot success/failure metrics, while CloudTrail provides an audit trail.

This architecture ensures high availability, fault tolerance, and operational simplicity. Organizations can restore EBS volumes quickly in another region during failures, aligning with AWS Well-Architected Framework principles for operational excellence, security, reliability, and cost optimization. Automated snapshot management reduces human errors and improves compliance readiness.

Option A is the most suitable solution for automating EBS volume backups across multiple regions while minimizing operational effort. Amazon Data Lifecycle Manager (DLM) allows organizations to define policies that automatically create, retain, and delete EBS snapshots according to predefined schedules and retention rules. This automation eliminates the need for manual intervention, reducing the risk of human error and ensuring consistent backup practices. By leveraging cross-region snapshot copy, backups can be replicated to other AWS regions, providing an additional layer of protection against regional outages and supporting disaster recovery strategies and compliance requirements.

Manual snapshots, as in option B, are labor-intensive, prone to errors, and difficult to enforce consistently across multiple accounts or regions. Option C, using EC2 instance backup scripts, requires ongoing management, monitoring, and updates, which increases operational overhead. Option D, S3 Standard, is object storage and cannot directly create or manage EBS volume snapshots, making it unsuitable for EBS backup automation.

Data Lifecycle Manager enables policy-based automation that covers scheduling snapshots at specific intervals, setting retention periods, and applying cross-region replication for redundancy. EBS snapshots are incremental, meaning that only changed blocks are stored after the first snapshot, which reduces storage costs and improves efficiency. All snapshots can be encrypted using AWS Key Management Service (KMS), ensuring data security both in transit and at rest. Organizations can monitor the success or failure of snapshot operations through CloudWatch metrics and configure alarms for proactive notifications, while CloudTrail provides a complete audit trail of snapshot creation, deletion, and access events, supporting compliance and governance requirements.

This architecture simplifies disaster recovery planning by ensuring that EBS volumes can be restored quickly in another region in the event of hardware failure, operational error, or a regional disruption. It improves operational efficiency, enhances fault tolerance, and reduces manual intervention while maintaining secure and compliant backup practices. By using DLM with cross-region snapshot copy, organizations achieve a highly available, reliable, and cost-effective backup solution that aligns with AWS Well-Architected Framework principles for operational excellence, reliability, security, and cost optimization. Automated snapshot management ensures consistency, reduces human error, and strengthens the organization’s ability to respond to failures and maintain business continuity.

Question 124:

A company wants to provide temporary, secure access to S3 objects for external partners while tracking access and ensuring compliance. Which solution is most appropriate?

Answer:

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

Explanation:

Option A is correct. Pre-signed URLs allow temporary access to S3 objects without exposing AWS credentials. The URLs include an expiration and limit operations to read or write. CloudTrail logging ensures all access is audited for compliance.

Public S3 buckets are insecure. Sharing IAM credentials violates least-privilege principles and audit requirements. S3 Standard is only a storage class and does not provide access control.

Pre-signed URLs can be generated dynamically via Lambda or API Gateway, integrating with existing security policies. Data is encrypted at rest using KMS and in transit via HTTPS. CloudTrail records all API activity, enabling auditing and compliance reporting.

This solution provides cost-effective, secure, and automated temporary access. Organizations maintain governance and minimize operational overhead while allowing external partners controlled access. It aligns with AWS Well-Architected principles for security, operational excellence, and reliability. Pre-signed URLs support scalable, secure, and auditable workflows for document sharing, media delivery, and collaborative projects.

Question 125:

A company wants to deploy a serverless web application that automatically scales and only charges for execution time. Which services should be used?

Answer:

A) AWS Lambda, API Gateway, and DynamoDB
B) EC2 instances
C) Elastic Beanstalk with RDS
D) S3 only

Explanation:

Option A is correct. AWS Lambda provides serverless compute that scales automatically with demand and charges only for execution time. API Gateway handles HTTP requests, invoking Lambda functions. DynamoDB provides scalable, low-latency storage for the application’s data.

EC2 requires manual scaling and provisioning. Elastic Beanstalk is partially managed but not serverless. S3 cannot host dynamic logic and is limited to static content.

Lambda’s event-driven architecture allows seamless integration with other services, such as S3, SNS, or DynamoDB streams. CloudWatch monitors Lambda execution, errors, and performance. IAM roles enforce least-privilege access, while KMS encryption protects sensitive data.

This architecture provides fault tolerance, scalability, and cost efficiency. Organizations eliminate server management, automatically handle traffic spikes, and maintain operational simplicity. The design aligns with AWS Well-Architected principles for operational excellence, security, cost optimization, and performance efficiency.

Option A is the most suitable solution for deploying a serverless web application that automatically scales and charges only for execution time. AWS Lambda provides serverless compute, allowing developers to run application logic without managing servers. Lambda functions scale automatically in response to incoming requests or events, and pricing is based solely on execution duration and the number of requests, which makes it cost-efficient for variable workloads. API Gateway serves as the front door for the application, handling HTTP requests and invoking Lambda functions to process them. This combination enables a fully serverless, event-driven architecture for web applications.

DynamoDB complements this architecture by providing a highly available, scalable, and low-latency NoSQL database. It handles storage and retrieval of application data, automatically scaling throughput to accommodate increasing traffic without manual intervention. This combination of Lambda, API Gateway, and DynamoDB allows developers to focus on application logic rather than infrastructure, simplifying deployment and reducing operational overhead.

Other options are less suitable for a serverless deployment. EC2 instances, as in option B, require manual provisioning, scaling, and maintenance, which increases operational complexity and costs. Elastic Beanstalk with RDS, option C, is partially managed but still relies on underlying server resources, so it is not fully serverless. S3 alone, option D, can host static content but does not support dynamic logic required for a web application.

Lambda’s event-driven model allows integration with additional AWS services such as S3 for storage, SNS for messaging, or DynamoDB streams for real-time data processing. Security and compliance are maintained through IAM roles that enforce least-privilege access and AWS KMS encryption to protect sensitive data. CloudWatch provides monitoring and logging for Lambda execution, errors, and performance metrics, enabling operational visibility and proactive troubleshooting.

This architecture delivers fault tolerance, scalability, and cost efficiency. Organizations benefit from automatic scaling to handle traffic spikes, elimination of server management, and reduced operational complexity. By leveraging Lambda, API Gateway, and DynamoDB, companies can build a highly available, secure, and performant web application aligned with AWS Well-Architected Framework principles for operational excellence, performance efficiency, security, and cost optimization.

Question 126:

A company wants to implement a multi-region, highly available DynamoDB table to serve global users with low latency. Which feature should be used?

Answer:

A) DynamoDB Global Tables
B) Single DynamoDB table in one region
C) RDS Multi-AZ
D) S3 Standard

Explanation:

Option A is correct. DynamoDB Global Tables replicate data across multiple AWS regions, allowing low-latency reads and writes from the nearest region. This ensures high availability, durability, and fault tolerance for global users.

A single table in one region does not support multi-region access and introduces latency for distant users. RDS Multi-AZ is relational and requires complex replication to achieve similar global scalability. S3 is object storage and unsuitable for relational queries.

Global Tables handle replication, conflict resolution, and automatic scaling. CloudWatch monitors read/write capacity, replication lag, and performance metrics. IAM policies ensure secure access, while KMS encrypts data at rest. This architecture reduces latency, supports business continuity, and ensures global performance.

Organizations can leverage Global Tables for e-commerce, gaming, and IoT platforms. The design aligns with AWS Well-Architected principles for reliability, operational excellence, performance efficiency, and security.

Question 127:

A company wants to analyze large amounts of historical data stored in S3 without loading it into a data warehouse. Which service is most suitable?

Answer:

A) Amazon Athena
B) RDS
C) EC2 with scripts
D) DynamoDB

Explanation:

Option A is correct. Amazon Athena enables serverless SQL queries directly on S3 objects. This eliminates the need to migrate data into a data warehouse, reducing operational overhead and costs. Athena is cost-effective as charges are based on the volume of data scanned.

RDS requires data migration and manual management. EC2 with scripts is operationally intensive and does not scale automatically. DynamoDB is NoSQL and cannot efficiently query large unstructured datasets.

Athena integrates with AWS Glue for schema management and supports partitioning to optimize queries. CloudWatch monitors query execution, and IAM ensures secure access. Data encryption at rest with KMS and HTTPS in transit ensures security and compliance.

This solution allows rapid insights from historical datasets, reduces operational complexity, and aligns with AWS Well-Architected principles for cost optimization, operational excellence, performance efficiency, and security.

Option A is the most suitable solution for analyzing large amounts of historical data stored in S3 without the need to load it into a data warehouse. Amazon Athena is a serverless interactive query service that allows users to run standard SQL queries directly against data stored in S3. Since Athena queries the data in place, organizations do not need to perform time-consuming and costly data migration into a separate analytics platform, significantly reducing operational overhead and accelerating the time to insight. Pricing is based on the amount of data scanned per query, making Athena cost-effective, especially when combined with techniques like partitioning and compression to minimize scanned data.

RDS, as in option B, requires moving the data into a relational database and managing instances, storage, and scaling, which adds complexity and operational overhead. Using EC2 with custom scripts, as in option C, requires provisioning and maintaining compute resources, handling scaling for large datasets, and building custom query mechanisms, which increases both cost and administrative burden. DynamoDB, option D, is a NoSQL database designed for low-latency transactional workloads and is not optimized for large-scale ad hoc analytics on unstructured or semi-structured historical data.

Athena integrates seamlessly with AWS Glue, which provides a central metadata catalog for schema management, making it easier to query structured and semi-structured datasets. Data can be partitioned in S3 to optimize query performance and reduce costs. Athena also integrates with CloudWatch for monitoring query execution metrics and performance, and IAM policies enforce secure access control to ensure that only authorized users can run queries. Data encryption using KMS ensures that data at rest is protected, while HTTPS ensures data in transit is secure, helping meet regulatory and compliance requirements.

This architecture allows organizations to gain rapid insights from historical datasets, perform ad hoc analysis, and generate reports without managing infrastructure. By using Athena, companies reduce operational complexity, improve cost efficiency, and achieve scalable performance for analytics workloads. It aligns with AWS Well-Architected Framework principles for operational excellence, cost optimization, performance efficiency, and security, providing a highly efficient solution for analyzing large datasets stored in S3.

Question 128:

A company wants to migrate an on-premises SQL Server database to AWS with minimal downtime, ensuring high availability and automated backups. Which architecture is recommended?

Answer:

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

Explanation:

Option A is correct. Amazon RDS Multi-AZ SQL Server provides high availability by replicating data synchronously to a standby instance in another Availability Zone. AWS Database Migration Service (DMS) allows near-zero downtime migration by continuously replicating data from the on-premises database to RDS.

EC2 with self-managed SQL Server increases operational complexity and requires manual configuration of replication, backups, and failover. S3 is object storage and unsuitable for relational databases. DynamoDB is NoSQL and cannot handle SQL Server workloads or queries.

RDS Multi-AZ supports automated backups, patching, and disaster recovery. DMS can perform homogeneous migrations with ongoing replication, ensuring data consistency during cutover. CloudWatch monitors metrics like CPU, memory, storage, and replica lag, while CloudTrail audits all actions.

Security is enforced through IAM roles, network isolation via VPCs, and encryption at rest using KMS. SSL/TLS ensures data in transit is secure. This architecture reduces downtime, operational overhead, and risks associated with database migration while providing fault tolerance and scalability.

By using RDS Multi-AZ with DMS, organizations achieve a highly available, secure, and efficient migration strategy aligned with AWS Well-Architected Framework principles for reliability, operational excellence, performance efficiency, and security.

Question 129:

A company wants to implement a highly available and fault-tolerant web application with automatic scaling based on traffic. Which architecture is most suitable?

Answer:

A) EC2 Auto Scaling group, Application Load Balancer (ALB), and RDS Multi-AZ
B) Single EC2 instance
C) S3 static website
D) Lambda only

Explanation:

Option A is correct. EC2 Auto Scaling ensures the number of instances adjusts based on demand, maintaining performance during peak traffic and minimizing costs during low usage. The ALB distributes requests across healthy instances, improving availability and fault tolerance. RDS Multi-AZ provides synchronous replication to a standby database in another Availability Zone, ensuring minimal downtime during database failures.

A single EC2 instance creates a single point of failure. S3 static hosting is limited to static content and cannot execute dynamic application logic. Lambda alone requires serverless redesign and does not natively support multi-tier relational workloads.

Auto Scaling integrates with CloudWatch for monitoring CPU, memory, request counts, and latency. ALB performs health checks to route traffic only to healthy instances. RDS Multi-AZ automates failover, backups, and patching. IAM roles enforce least-privilege access, KMS encrypts data at rest, and TLS ensures secure transit.

This architecture achieves operational excellence, scalability, high availability, fault tolerance, and security while reducing administrative overhead. It aligns with AWS Well-Architected Framework principles for reliability, security, operational excellence, and performance efficiency.

Option A is the most suitable architecture for implementing a highly available and fault-tolerant web application that can automatically scale based on traffic. By using an EC2 Auto Scaling group, the application can dynamically adjust the number of compute instances to match demand. During periods of high traffic, Auto Scaling launches additional instances to maintain performance, while during low traffic periods, it terminates unnecessary instances to reduce costs. This elasticity ensures that the application remains responsive and cost-efficient regardless of traffic fluctuations.

The Application Load Balancer distributes incoming requests evenly across all healthy instances, improving fault tolerance and ensuring that the failure of a single instance does not impact user experience. The ALB also performs health checks on each instance and automatically stops routing traffic to instances that are unhealthy, further enhancing reliability and uptime. RDS Multi-AZ provides high availability for the database layer by replicating data synchronously to a standby instance in a different Availability Zone. In case of primary database failure, RDS automatically fails over to the standby instance, ensuring minimal downtime and continuous operation.

Other options are less suitable for highly available, scalable web applications. A single EC2 instance, as in option B, creates a single point of failure and cannot handle traffic spikes effectively. S3 static website hosting, option C, is only suitable for serving static content and does not support dynamic application logic. Lambda, option D, is serverless and event-driven but does not natively support multi-tier relational applications without significant redesign.

This architecture also integrates with AWS security and monitoring services. CloudWatch monitors metrics such as CPU utilization, memory, request counts, and latency, allowing administrators to optimize scaling policies. IAM roles enforce least-privilege access to resources, KMS ensures data at rest is encrypted, and TLS provides secure data transmission. RDS Multi-AZ automates backups, patching, and failover, reducing operational complexity. By combining Auto Scaling, ALB, and RDS Multi-AZ, organizations achieve operational excellence, high availability, fault tolerance, scalability, and security while minimizing administrative overhead. This design aligns closely with AWS Well-Architected Framework principles for reliability, performance efficiency, operational excellence, and security.

Question 130:

A company wants to provide global users with low-latency access to a web application while protecting against DDoS attacks. Which architecture is recommended?

Answer:

A) Amazon CloudFront with S3/EC2 origin, AWS WAF, and HTTPS
B) Single EC2 instance
C) S3 static website
D) Direct Connect

Explanation:

Option A is correct. CloudFront caches content at edge locations worldwide, reducing latency for global users. AWS WAF protects against common web attacks, such as SQL injection and XSS, while HTTPS ensures secure communication. CloudFront also integrates with AWS Shield for DDoS protection.

A single EC2 instance cannot provide global low-latency access and is a single point of failure. S3 static websites are limited to static content. Direct Connect provides dedicated connectivity but does not optimize content delivery or offer DDoS protection.

CloudFront supports caching strategies, origin failover, and integration with Lambda@Edge for dynamic content manipulation. CloudWatch monitors latency, cache hits, error rates, and traffic patterns. CloudTrail logs API calls for auditing and security compliance.

This architecture ensures high availability, scalability, and security while reducing operational overhead. Organizations achieve fast global access, protection from attacks, and improved fault tolerance, aligning with AWS Well-Architected Framework principles for performance efficiency, security, reliability, and operational excellence.

Question 131:

A company wants to automate compliance checks and remediation for IAM policies across multiple AWS accounts. Which solution is most suitable?

Answer:

A) AWS Config with AWS Organizations
B) IAM policies alone
C) EC2 instances
D) S3 only

Explanation:

Option A is correct. AWS Config evaluates IAM policies against compliance rules, while AWS Organizations provides centralized enforcement across multiple accounts. Automated remediation can be configured to fix non-compliant policies, reducing manual effort and ensuring governance.

IAM policies alone cannot enforce compliance across accounts. EC2 instances do not provide policy monitoring or remediation capabilities. S3 is for object storage and does not manage IAM policy compliance.

Config rules can trigger automatic remediation, such as revoking excessive permissions or enforcing least-privilege policies. Aggregators consolidate compliance data across accounts for monitoring. CloudWatch alarms notify administrators of non-compliant resources, and CloudTrail provides a full audit trail.

This solution enhances security, reduces risk, ensures operational efficiency, and maintains compliance with industry standards. It aligns with AWS Well-Architected principles for security, operational excellence, and reliability, providing automated governance and auditing across a multi-account AWS environment.

Question 132:

A company wants to analyze high-volume streaming data in real-time and store processed results for low-latency access. Which architecture is recommended?

Answer:

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

Explanation:

Option A is correct. Kinesis Data Streams enables real-time ingestion of high-velocity data, partitioned across shards for parallel processing. Lambda triggers automatically on new events to process, transform, and validate data. DynamoDB provides fast, scalable storage for processed results, enabling low-latency access.

S3 supports batch processing but is unsuitable for real-time analytics. EC2 batch jobs require manual scaling and cannot provide near real-time performance. RDS Multi-AZ ensures high availability but is not optimized for high-throughput streaming workloads.

Kinesis provides durability, replayability, and automatic scaling. Lambda scales automatically, with dead-letter queues for error handling. DynamoDB’s low-latency access, encryption at rest via KMS, and IAM-based access controls ensure secure, reliable storage.

CloudWatch monitors throughput, errors, and processing performance, while CloudTrail tracks API activity for audit purposes. Step Functions can orchestrate multi-step workflows, such as alerts or further processing.

This architecture supports IoT analytics, operational monitoring, anomaly detection, and real-time dashboards. It reduces operational complexity, scales automatically, and ensures fault tolerance while aligning with AWS Well-Architected principles for operational excellence, reliability, security, and performance efficiency.

Option A is the most suitable architecture for analyzing high-volume streaming data in real-time while providing low-latency access to processed results. Amazon Kinesis Data Streams enables real-time ingestion of high-velocity data, partitioned across shards to allow parallel processing. This ensures that large volumes of data can be handled efficiently and without bottlenecks. Kinesis provides durability and fault tolerance, storing multiple copies of data across multiple Availability Zones. It also supports replaying data in the event of processing errors or downstream failures, which adds reliability to real-time analytics pipelines.

AWS Lambda integrates seamlessly with Kinesis by automatically triggering functions when new data records are available. Lambda functions can perform transformations, filtering, enrichment, or validation on incoming streaming data. Being serverless, Lambda scales automatically in response to incoming events and charges only for execution time, making it a cost-efficient solution for variable workloads. Dead-letter queues can capture failed events for later analysis, improving operational resilience.

Amazon DynamoDB serves as a fast, scalable storage layer for processed data, enabling low-latency retrieval for applications, dashboards, and analytics. DynamoDB automatically scales to accommodate increasing workloads and provides high availability. Security features such as encryption at rest using AWS KMS and IAM-based access control ensure that sensitive data is protected. This combination allows real-time data to be ingested, processed, and made immediately accessible without manual intervention.

Other options are less suitable for real-time streaming analytics. S3 alone, option B, is ideal for batch storage but cannot provide near real-time processing. EC2 batch processing, option C, requires manual provisioning, scaling, and scheduling, and does not meet low-latency requirements. RDS Multi-AZ, option D, ensures database availability but is not designed for high-throughput, streaming workloads and cannot handle parallel processing efficiently.

CloudWatch monitoring provides insights into stream throughput, Lambda execution metrics, and DynamoDB performance, while CloudTrail enables auditing of API activity. Step Functions can orchestrate multi-step workflows such as triggering alerts or additional processing. This architecture is suitable for use cases such as IoT analytics, operational monitoring, anomaly detection, and real-time dashboards. It reduces operational complexity, scales automatically, and ensures fault tolerance, aligning with AWS Well-Architected Framework principles for operational excellence, reliability, security, and performance efficiency.

Question 133:

A company wants to store frequently accessed content close to global users while reducing latency and cost. Which architecture is recommended?

Answer:

A) Amazon CloudFront with S3 origin
B) S3 only
C) EC2 web servers
D) RDS Multi-AZ

Explanation:

Option A is correct. CloudFront caches content at edge locations globally, reducing latency and improving performance. S3 serves as the origin for durable storage of the content. Cached content reduces the number of requests to the S3 origin, lowering data transfer costs.

S3 alone cannot provide low-latency access globally. EC2 requires manual scaling, load balancing, and regional deployments, increasing complexity. RDS is relational storage and unsuitable for content delivery.

CloudFront supports caching strategies with configurable TTLs, cache invalidation, and Lambda@Edge for request/response processing. Security includes HTTPS for secure transport, WAF for web attack protection, and Shield for DDoS mitigation. CloudWatch monitors traffic, cache hit ratios, latency, and errors, while CloudTrail logs all access.

This architecture ensures low-latency global delivery, high availability, cost efficiency, and security. Organizations benefit from operational simplicity, scalability, and reliability, fully aligned with AWS Well-Architected principles for performance efficiency, operational excellence, security, and cost optimization.

Question 134:

A company wants to implement automated snapshots and cross-region replication for EBS volumes to support disaster recovery. Which solution is most appropriate?

Answer:

A) Amazon Data Lifecycle Manager (DLM) with cross-region snapshot copy
B) Manual snapshots
C) EC2 instance scripts
D) S3 Standard

Explanation:

Option A is correct. DLM automates EBS snapshot creation, retention, and deletion based on defined policies. Cross-region snapshot copy ensures snapshots are available in another region for disaster recovery, compliance, and business continuity.

Manual snapshots are error-prone and labor-intensive. EC2 scripts require maintenance and monitoring. S3 alone cannot snapshot EBS volumes.

DLM supports incremental snapshots, reducing storage costs, and integrates with CloudWatch for monitoring snapshot success/failure. Snapshots are encrypted using KMS for security. CloudTrail provides auditing for snapshot creation and replication.

This architecture reduces operational overhead, ensures high availability and fault tolerance, and supports disaster recovery strategies. Organizations can quickly restore volumes in another region during failures. It aligns with AWS Well-Architected Framework principles for operational excellence, reliability, security, and cost optimization.

Question 135:

A company wants to implement near real-time analytics on streaming IoT data and store results in a database optimized for low-latency access. Which architecture is most suitable?

Answer:

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

Explanation:

Option A is correct. Kinesis Data Streams provides scalable ingestion of high-volume streaming data, partitioned into shards for parallel processing. Lambda processes events in near real-time, performing transformations, aggregations, and validations. DynamoDB stores results for low-latency retrieval, with automatic scaling and encryption.

S3 is suitable for batch processing, not real-time analytics. EC2 batch jobs require manual scaling and cannot provide immediate processing. RDS Multi-AZ is relational and does not support high-throughput, real-time streaming workloads efficiently.

CloudWatch monitors metrics such as throughput, processing latency, and Lambda performance. CloudTrail provides audit logs for all actions. IAM roles enforce secure access between services, and KMS encryption protects sensitive data. Step Functions can orchestrate workflows, handle retries, and trigger notifications.

This architecture reduces operational overhead, ensures fault tolerance, and enables real-time analytics and monitoring. It aligns with AWS Well-Architected principles for operational excellence, reliability, security, and performance efficiency, supporting scalable IoT applications globally.

Option A is the most suitable architecture for implementing near real-time analytics on streaming IoT data while storing results in a database optimized for low-latency access. Amazon Kinesis Data Streams provides a scalable platform for ingesting high-volume streaming data. Streams are partitioned into shards, allowing parallel processing and ensuring that large amounts of data can be ingested efficiently without creating bottlenecks. Kinesis also ensures durability and fault tolerance by replicating data across multiple Availability Zones, and it supports replaying data for additional processing or recovery from failures.

AWS Lambda integrates directly with Kinesis, automatically triggering functions when new records arrive. Lambda functions can process events in near real-time, performing operations such as filtering, transformations, aggregations, and validations. This serverless approach scales automatically with the volume of incoming data and only incurs cost based on execution time, providing a highly efficient and cost-effective solution for processing streaming data. Dead-letter queues can capture failed events for further analysis, helping to maintain reliability.

Amazon DynamoDB serves as the storage layer for processed results, providing low-latency access for applications, dashboards, and analytics. DynamoDB automatically scales to handle increased read and write workloads and supports features like encryption at rest via KMS and fine-grained IAM-based access control to secure sensitive data. This ensures that processed data is both accessible quickly and securely.

Other options are less suitable for this use case. S3, as in option B, is optimized for batch processing and cannot handle real-time analytics. EC2 batch processing, option C, requires manual scaling, provisioning, and scheduling, and does not meet low-latency requirements. RDS Multi-AZ, option D, ensures high availability for relational data but is not optimized for high-throughput, real-time streaming workloads.

CloudWatch monitoring provides insights into stream throughput, Lambda execution performance, and DynamoDB read/write activity. CloudTrail logs all API activity for auditing purposes. Step Functions can orchestrate multi-step workflows, implement retries, and trigger notifications for exceptions. This architecture reduces operational overhead, ensures fault tolerance, and supports near real-time insights for IoT applications. By combining Kinesis, Lambda, and DynamoDB, organizations achieve scalable, highly available, secure, and performant real-time analytics, aligning with AWS Well-Architected Framework principles for operational excellence, reliability, security, and performance efficiency.

Question 136:

A company wants to enforce encryption for all S3 buckets and prevent public access across multiple accounts automatically. Which solution is most appropriate?

Answer:

A) AWS Config with AWS Organizations
B) S3 bucket policies alone
C) IAM policies
D) EC2 scripts

Explanation:

Option A is correct. AWS Config evaluates the configuration of S3 buckets against compliance rules, such as mandatory encryption and denial of public access. AWS Organizations allows centralized enforcement across multiple accounts, ensuring governance at scale.

S3 bucket policies alone cannot enforce rules across multiple accounts automatically. IAM policies cannot monitor compliance or enforce remediation centrally. EC2 scripts would require maintenance, monitoring, and automation logic, increasing operational complexity.

AWS Config rules can automatically remediate non-compliant buckets by applying encryption settings or adjusting access policies. Aggregators consolidate compliance data from multiple accounts for centralized monitoring. CloudWatch can trigger alerts for non-compliant resources, while CloudTrail provides a detailed audit trail of actions and changes.

This solution improves security, operational efficiency, and ensures regulatory compliance (e.g., HIPAA, PCI DSS, GDPR). Automated enforcement reduces human error and ensures continuous compliance without manual intervention. Using AWS Config with Organizations aligns with AWS Well-Architected Framework principles for security, operational excellence, and reliability, providing a scalable, manageable, and secure approach to multi-account governance.

Question 137:

A company wants to reduce latency for a globally distributed web application and ensure content is served efficiently to end-users worldwide. Which AWS service combination should be used?

Answer:

A) Amazon CloudFront with S3 or EC2 origin
B) EC2 in a single region
C) S3 alone
D) Direct Connect

Explanation:

Option A is correct. Amazon CloudFront caches static and dynamic content at edge locations globally, reducing latency by serving content from locations close to users. The origin can be S3 for static content or EC2 for dynamic content.

EC2 in a single region results in high latency for users located far from the region. S3 alone cannot deliver dynamic content efficiently or provide caching. Direct Connect provides private network connectivity but does not improve content delivery for global users.

CloudFront allows caching strategies, TTL configuration, and invalidation to optimize content delivery. Lambda@Edge can manipulate requests and responses for personalization or security checks. Integration with AWS WAF protects against web attacks, and AWS Shield mitigates DDoS threats. CloudWatch monitors cache performance, request latency, and error rates. CloudTrail provides an audit trail for access and configuration changes.

This architecture provides high availability, scalability, and security. It improves end-user experience by minimizing latency and reduces operational overhead through managed caching and global distribution. Aligning with AWS Well-Architected principles, it ensures performance efficiency, reliability, operational excellence, and cost optimization.

Question 138:

A company wants to implement a real-time analytics pipeline for IoT devices that scales automatically and provides low-latency storage. Which AWS services should be used?

Answer:

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

Explanation:

Option A is correct. Kinesis Data Streams captures high-volume IoT data in real-time, partitioned for parallel processing. Lambda functions process each event automatically, performing filtering, transformation, and aggregation. DynamoDB stores processed results for low-latency retrieval with automatic scaling.

S3 is batch-oriented and unsuitable for real-time analytics. EC2 batch jobs require manual scaling and cannot deliver near-real-time processing. RDS Multi-AZ is relational, lacks the flexibility for high-throughput streaming data, and may introduce latency.

Kinesis provides durability, replayability, and scaling for massive streaming workloads. Lambda scales automatically, and dead-letter queues handle processing failures. DynamoDB supports global tables, encryption at rest (KMS), and low-latency queries. CloudWatch monitors throughput, processing latency, and errors, while CloudTrail tracks all API activity.

This architecture allows IoT analytics, operational monitoring, and anomaly detection in real-time. Operational complexity is minimized, fault tolerance is ensured, and cost efficiency is achieved by using serverless services. The solution aligns with AWS Well-Architected principles for operational excellence, reliability, security, and performance efficiency, supporting scalable IoT applications globally.

Question 139:

A company wants to migrate an on-premises Oracle database to AWS with minimal downtime and ensure high availability. Which architecture is most suitable?

Answer:

A) AWS Database Migration Service (DMS) with Amazon RDS Multi-AZ
B) EC2 with self-managed Oracle
C) S3 only
D) DynamoDB

Explanation:

Option A is correct. AWS DMS allows continuous replication from an on-premises Oracle database to Amazon RDS, enabling near-zero downtime during migration. RDS Multi-AZ provides high availability with synchronous replication to a standby instance in another Availability Zone, supporting automatic failover.

EC2 with self-managed Oracle increases operational complexity and requires manual replication and failover configuration. S3 is object storage and unsuitable for relational database workloads. DynamoDB is NoSQL and cannot handle Oracle-specific SQL queries or workloads.

DMS supports homogeneous migration, data validation, and ongoing replication. RDS Multi-AZ ensures automated backups, patching, and disaster recovery. Security is enforced using IAM roles, network isolation via VPC, and KMS encryption. CloudWatch monitors replication performance, instance health, and resource utilization. CloudTrail provides auditing of all migration activities.

This architecture minimizes downtime, ensures operational efficiency, high availability, and security. It aligns with AWS Well-Architected principles for reliability, operational excellence, performance efficiency, and security, allowing businesses to migrate critical workloads safely and efficiently.

Question 140:

A company wants to implement a serverless web application that automatically scales with demand and charges only for compute usage. Which architecture is recommended?

Answer:

A) AWS Lambda, API Gateway, and DynamoDB
B) EC2 instances
C) Elastic Beanstalk with RDS
D) S3 only

Explanation:

Option A is correct. AWS Lambda provides serverless compute that scales automatically based on incoming requests and charges only for execution time. API Gateway manages HTTP requests and triggers Lambda functions. DynamoDB stores application data with low-latency access and automatic scaling.

EC2 instances require manual scaling, management, and cost management. Elastic Beanstalk is partially managed but not fully serverless. S3 alone cannot run dynamic application logic and is limited to static content.

Serverless architecture reduces operational overhead, ensures fault tolerance, and enables high availability. CloudWatch monitors Lambda metrics, API Gateway performance, and DynamoDB throughput. IAM roles enforce secure access, while KMS encrypts sensitive data.

This design aligns with AWS Well-Architected Framework principles for operational excellence, cost optimization, security, and performance efficiency. Organizations benefit from automatic scaling, reduced operational complexity, cost-effectiveness, and the ability to focus on application logic rather than infrastructure management.