Amazon AWS Certified Solutions Architect – Professional SAP-C02 Exam Dumps and Practice Test Questions Set 4 Q61-80

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Question 61:

A company wants to deploy a high-traffic web application that must scale automatically to handle sudden traffic spikes while minimizing cost. Which solution is the most appropriate?

Answer:

A) AWS Lambda with API Gateway and DynamoDB
B) EC2 Auto Scaling with Application Load Balancer
C) ECS with EC2 launch type
D) S3 static hosting with Transfer Acceleration

Explanation:

The correct answer is A) AWS Lambda with API Gateway and DynamoDB.

Serverless architectures using Lambda provide automatic scaling in response to incoming traffic. There is no need to provision servers, which reduces operational overhead and optimizes costs because you only pay for actual usage. API Gateway handles request routing, throttling, caching, and authorization, while DynamoDB provides a highly scalable, low-latency database backend.

Option B, EC2 Auto Scaling with ALB, does scale automatically but may require overprovisioning during peak traffic, leading to higher costs and potential latency during scaling events. Option C, ECS with EC2 launch type, also requires provisioning and management of EC2 instances, adding operational complexity. Option D, S3 static hosting, is suitable for static content only and cannot support dynamic processing.

This architecture supports high availability across multiple Availability Zones. Lambda automatically scales horizontally, API Gateway handles request routing, and DynamoDB can manage large read/write workloads with on-demand capacity mode. Security is enforced through IAM roles, KMS encryption for sensitive data, and HTTPS connections. Logging and monitoring are integrated via CloudWatch and X-Ray.

The serverless approach ensures predictable performance during sudden spikes, reduces operational management, and supports best practices for reliability, cost optimization, and operational excellence. For SAP-C02 exam scenarios, this demonstrates the proper design for highly scalable, cost-efficient, and resilient web applications.

Question 62:

A company needs to process a large stream of sensor data in real-time, perform analytics, and trigger alerts for anomalies. Which AWS service combination is best?

Answer:

A) Amazon Kinesis Data Streams with Lambda
B) Amazon SQS with EC2
C) Amazon SNS with S3 triggers
D) RDS with batch processing

Explanation:

The correct answer is A) Amazon Kinesis Data Streams with Lambda.

Kinesis Data Streams supports high-throughput, low-latency ingestion of streaming data. Each shard allows parallel processing, ensuring scalability. Lambda consumers process the data in real time, allowing detection of anomalies and triggering alerts.

Option B, SQS with EC2, introduces additional latency and operational complexity because EC2 instances must be provisioned and managed. Option C, SNS with S3 triggers, cannot handle high-throughput real-time streams with ordering guarantees. Option D, RDS with batch processing, introduces delays and is unsuitable for real-time analytics.

Kinesis ensures durability by replicating data across multiple Availability Zones. CloudWatch monitoring tracks stream health, latency, and Lambda processing performance. Data retention can be extended to allow replay in case of downstream failures. Security is enforced with IAM roles, TLS encryption for data in transit, and KMS for data at rest.

This architecture ensures low-latency, high-throughput processing of streaming data. It provides operational simplicity, durability, and scalability. Kinesis Data Streams with Lambda is ideal for IoT, clickstream analytics, and monitoring systems, making it a best practice for serverless event-driven architectures in SAP-C02 scenarios.

When a company needs to process a large stream of sensor data in real time, perform analytics, and trigger alerts for anomalies, the architecture must support high-throughput ingestion, low-latency processing, and scalability while ensuring durability and operational simplicity. AWS offers multiple services for event-driven and streaming data processing, but selecting the right combination is essential for real-time analytics and alerting.

Amazon Kinesis Data Streams is a fully managed streaming service designed to handle high-volume, low-latency data ingestion. It divides the incoming stream into shards, each of which can be processed in parallel, allowing applications to scale automatically to accommodate increases in data throughput. Kinesis replicates data across multiple Availability Zones, providing durability and fault tolerance, ensuring that no data is lost even if an Availability Zone experiences a failure.

AWS Lambda integrates seamlessly with Kinesis as a consumer for real-time processing. Lambda functions automatically scale in response to the stream’s load, eliminating the need to provision and manage servers. This enables the immediate analysis of sensor data, detection of anomalies, and triggering of alerts or downstream workflows. For example, Lambda can evaluate sensor readings against thresholds and invoke SNS or other notification services to alert operations teams. Data retention in Kinesis can be configured to allow replaying records in case of processing failures, providing flexibility and resilience in the analytics pipeline.

Alternative solutions are less suitable for this use case. Using Amazon SQS with EC2 introduces additional latency, because EC2 instances must poll the queue, and scaling requires manual intervention. Amazon SNS with S3 triggers is not designed for high-throughput, ordered, real-time streams, and therefore cannot reliably support low-latency anomaly detection. RDS with batch processing introduces significant delays due to its batch-oriented nature, making it impractical for real-time analytics and alerting.

Operational monitoring and security are built into the Kinesis and Lambda architecture. CloudWatch provides metrics for stream health, shard processing, and Lambda execution performance. Security is enforced through IAM roles and policies, TLS encryption for data in transit, and KMS encryption for data at rest.

This combination ensures low-latency, high-throughput processing, durability, and operational simplicity. It is ideal for IoT applications, real-time monitoring, and clickstream analytics, making Kinesis Data Streams with Lambda the recommended architecture for real-time analytics and anomaly detection.

The correct answer is A) Amazon Kinesis Data Streams with Lambda.

Question 63:

A company wants to implement a disaster recovery strategy for an RDS database with an RPO of 5 minutes and RTO under 20 minutes. Which solution is appropriate?

Answer:

A) Amazon Aurora Global Database
B) Cross-region RDS snapshots
C) Standby EC2 servers with manual replication
D) Manual database replication scripts

Explanation:

The correct answer is A) Amazon Aurora Global Database.

Aurora Global Database supports multi-region, low-latency replication. Replication lag is typically under one second, allowing minimal data loss. Automatic failover ensures that applications can resume operations quickly, meeting the RTO of 20 minutes.

Option B, cross-region snapshots, introduces significant recovery time. Option C requires manual intervention and is error-prone. Option D is operationally complex and not reliable for strict RPO/RTO requirements.

Aurora automatically replicates six copies of data across three Availability Zones in each region. Monitoring and operational insights are provided by CloudWatch, while security and compliance are enforced through IAM, KMS encryption, and CloudTrail logging. Secondary regions can also be used for read scaling, reducing latency and improving availability.

This architecture ensures disaster recovery, high availability, and resilience while minimizing operational overhead. It demonstrates the correct design for enterprise-level RDS workloads in SAP-C02 scenarios.

Question 64:

A company needs to reduce read latency for a DynamoDB table that receives millions of requests per second. Which solution is best?

Answer:

A) DynamoDB Accelerator (DAX)
B) ElastiCache Redis
C) S3 Transfer Acceleration
D) RDS Read Replicas

Explanation:

The correct answer is A) DynamoDB Accelerator (DAX).

DAX provides a fully managed, in-memory cache for DynamoDB. It reduces read latency from milliseconds to microseconds while maintaining consistency via write-through caching. DAX clusters scale horizontally and offer high availability with failover across nodes.

Option B, ElastiCache, requires additional application logic for caching. Option C, S3 Transfer Acceleration, is not suitable for database query caching. Option D, RDS Read Replicas, cannot cache DynamoDB queries.

DAX integrates with IAM, KMS encryption, and TLS. CloudWatch monitors cache performance and health. It reduces database load, improves performance, and ensures low-latency access for high-traffic applications. For SAP-C02, DAX demonstrates best practices for globally scalable, high-performance DynamoDB applications.

Question 65:

A company wants to orchestrate a serverless workflow with multiple Lambda functions, conditional branching, and retries. Which service is most suitable?

Answer:

A) AWS Step Functions
B) Amazon SWF
C) AWS Batch
D) Amazon SQS

Explanation:

The correct answer is A) AWS Step Functions.

Step Functions allows defining state machines for serverless workflows. It supports sequential, parallel, and conditional execution, built-in error handling, and retries. It integrates with Lambda, ECS, SNS, and other services.

Option B, SWF, is legacy and requires managing worker nodes. Option C, Batch, is for batch workloads, not orchestration. Option D, SQS, is a messaging service without orchestration capabilities.

Step Functions provides operational visibility via execution history, CloudWatch metrics, and X-Ray tracing. Security and compliance are enforced via IAM roles and KMS encryption. Standard workflows support long-running durable processes, and express workflows allow high-throughput short-duration tasks. This architecture simplifies complex workflows, reduces operational overhead, and ensures reliability for serverless applications.

Question 66:

A company wants a highly available messaging system with exactly-once processing for financial transactions. Which AWS service is appropriate?

Answer:

A) Amazon Kinesis Data Streams
B) Amazon SQS standard queues
C) Amazon SNS
D) DynamoDB Streams

Explanation:

The correct answer is A) Amazon Kinesis Data Streams.

Kinesis ensures durability and ordering per shard. Lambda or EC2 consumers can checkpoint processed records for exactly-once processing. Data is replicated across multiple Availability Zones, providing resilience.

Option B, SQS standard queues, provide at-least-once delivery and may result in duplicates. FIFO queues enforce order but have limited throughput. Option C, SNS, does not guarantee order or exactly-once semantics. Option D, DynamoDB Streams, only captures table changes and is unsuitable for general messaging.

Kinesis integrates with CloudWatch for metrics, KMS for encryption, and supports extended retention for reprocessing. This architecture supports high-throughput, low-latency, durable, and reliable messaging for financial workloads.

When designing a highly available messaging system for financial transactions, it is critical to ensure durability, ordering, and exactly-once processing to maintain data integrity and support compliance requirements. Financial workloads often demand strict guarantees on message delivery and processing, and the architecture must handle high-throughput streams while providing fault tolerance.

Amazon Kinesis Data Streams is the most appropriate AWS service for this scenario. Kinesis enables real-time streaming of data and organizes messages into shards, preserving the order of records within each shard. Records are replicated across multiple Availability Zones, ensuring durability and resilience in case of failures. Consumers, such as AWS Lambda functions or EC2-based applications, can process records and maintain checkpoints, which allows exactly-once processing semantics. Checkpointing ensures that each record is processed precisely once, even if retries are required due to transient failures. Kinesis also supports extended data retention, allowing records to be reprocessed if necessary for auditing or error recovery, which is especially important for financial systems where traceability is essential.

Other AWS messaging services have limitations in this context. Amazon SQS standard queues provide at-least-once delivery, which can result in duplicate messages and complicate exactly-once processing. SQS FIFO queues address ordering and deduplication but have lower throughput limits, which can restrict scalability for high-volume financial transactions. Amazon SNS is a pub/sub messaging service that does not guarantee message order or exactly-once delivery, making it unsuitable for applications where processing consistency is critical. DynamoDB Streams capture changes in a DynamoDB table but are intended for event-driven data replication and analytics, not general-purpose messaging or high-throughput transaction processing.

Kinesis integrates seamlessly with AWS CloudWatch for monitoring shard health, consumer lag, and throughput, while security is managed through IAM roles and policies, TLS encryption in transit, and KMS encryption at rest. This combination provides a highly available, durable, and secure messaging architecture capable of supporting low-latency, exactly-once processing for financial workloads.

The correct answer is A) Amazon Kinesis Data Streams.

Question 67:

A company needs a scalable, globally distributed database with low-latency reads for users worldwide. Which solution is most appropriate?

Answer:

A) Amazon Aurora Global Database
B) RDS Multi-AZ
C) DynamoDB global tables
D) Amazon Redshift

Explanation:

The correct answer is A) Amazon Aurora Global Database.

Aurora Global Database replicates data asynchronously across regions with sub-second replication lag. Secondary regions can serve read queries locally, reducing latency for global users. Failover provides high availability.

Option B, Multi-AZ RDS, is limited to a single region. Option C, DynamoDB global tables, is NoSQL and may not meet relational database requirements. Option D, Redshift, is designed for analytics, not transactional workloads.

Aurora integrates with CloudWatch, IAM, KMS, and CloudTrail. Automatic storage scaling, read scaling, and multi-region availability ensure low-latency access, high performance, and operational simplicity for globally distributed applications.

Question 68:

A company wants to implement real-time analytics on streaming IoT data with durability and low-latency processing. Which architecture is best?

Answer:

A) Amazon Kinesis Data Streams with Lambda
B) SQS with EC2 consumers
C) SNS with S3 triggers
D) RDS with batch processing

Explanation:

The correct answer is A) Amazon Kinesis Data Streams with Lambda.

Kinesis provides real-time ingestion and processing of high-throughput streams. Lambda consumers allow near real-time analytics and alerting. Data is replicated across multiple AZs for durability.

Option B, SQS with EC2, introduces latency and operational overhead. Option C, SNS with S3, cannot handle ordered, high-throughput streams. Option D, RDS batch processing, is not real-time.

CloudWatch monitors shard health, processing lag, and Lambda performance. Security is managed via IAM, TLS, and KMS. This architecture provides durability, scalability, and low-latency processing for IoT analytics.

When a company needs to implement real-time analytics on streaming IoT data, the architecture must support high-throughput ingestion, low-latency processing, and durability to ensure reliable handling of incoming data. AWS provides several services for streaming and event-driven processing, but the choice depends on the need for real-time responsiveness, scalability, and operational simplicity.

Amazon Kinesis Data Streams is a fully managed service designed for real-time streaming of large amounts of data. It allows IoT devices to continuously send data to Kinesis, which stores the data across multiple availability zones, ensuring durability and fault tolerance. Kinesis organizes the data into shards, which can be scaled horizontally to handle increases in throughput. Using AWS Lambda as a consumer enables near real-time processing of data as it arrives. Lambda functions can transform, analyze, or route the data to other services such as Amazon S3, Amazon Redshift, or Amazon Elasticsearch Service for further analysis. This combination allows the architecture to provide both low-latency processing and scalability while minimizing operational management, as Lambda automatically scales to match incoming data volume.

Alternative solutions have limitations for real-time IoT analytics. Using Amazon SQS with EC2 consumers introduces additional latency because EC2 instances must poll the queue for messages. This approach also increases operational overhead, as the infrastructure must be maintained and scaled manually. SNS with S3 triggers cannot reliably handle ordered, high-throughput streams, which is often required for IoT telemetry. Amazon RDS with batch processing is unsuitable because batch-oriented architectures inherently introduce delays, making real-time analysis impossible.

The Kinesis Data Streams and Lambda architecture also provides robust observability and security. CloudWatch monitors shard health, processing lag, and Lambda performance, allowing operators to detect bottlenecks or failures quickly. IAM roles and policies control access to streams and Lambda functions, while TLS and KMS encryption protect data in transit and at rest.

This solution ensures durable, scalable, and low-latency processing for IoT analytics, enabling real-time insights and alerting without the operational complexity of managing infrastructure manually.

The correct answer is A) Amazon Kinesis Data Streams with Lambda.

Question 69:

A company wants to reduce latency for global users accessing dynamic content in a web application. Which AWS services combination is best?

Answer:

A) CloudFront with multi-region ALBs and Route 53 latency-based routing
B) Single-region ALB
C) S3 with Transfer Acceleration
D) Global Accelerator with single-region EC2

Explanation:

The correct answer is A) CloudFront with multi-region ALBs and Route 53 latency-based routing.

Route 53 directs users to the closest region. CloudFront caches dynamic content at edge locations. Multi-region ALBs distribute traffic across regions for high availability.

Single-region ALB cannot handle regional failover. S3 Transfer Acceleration only improves static content transfer. Global Accelerator improves network performance but does not provide application failover.

CloudFront reduces latency globally while multi-region ALBs ensure fault tolerance. CloudWatch, CloudTrail, and WAF integrate to provide observability, security, and compliance. This architecture ensures low-latency access and high availability.

Question 70:

A company wants to implement fine-grained access control for S3 objects with encryption at rest and in transit. Which architecture is most appropriate?

Answer:

A) S3 with IAM roles, bucket policies, and KMS-managed keys
B) Public S3 bucket
C) Client-side encryption with manual key management
D) Server-side encryption with AES-256 only

Explanation:

The correct answer is A) S3 with IAM roles, bucket policies, and KMS-managed keys.

IAM roles provide temporary credentials and access restrictions. Bucket policies enforce object-level permissions. KMS ensures encryption at rest, and HTTPS encrypts data in transit. CloudTrail logs all activity for auditing.

Option B exposes data publicly. Option C requires manual key management, increasing risk. Option D only encrypts at rest without centralized key management or fine-grained access.

This solution ensures secure, auditable, and compliant access, following AWS best practices. It reduces operational overhead, supports lifecycle management, versioning, and logging, and meets enterprise security and compliance standards.

When designing a secure architecture for Amazon S3 that requires fine-grained access control along with encryption at rest and in transit, it is important to combine identity management, access policies, and encryption mechanisms. AWS provides several features that, when used together, ensure data is protected, access is restricted appropriately, and compliance requirements are met.

The most appropriate solution is to use Amazon S3 with IAM roles, bucket policies, and KMS-managed keys. IAM roles allow applications and users to obtain temporary credentials with scoped permissions, ensuring that only authorized entities can access specific S3 objects. By using roles instead of long-term credentials, the risk of credential compromise is reduced, and access can be managed dynamically. Bucket policies and object-level ACLs enforce fine-grained permissions, controlling who can read, write, or delete specific objects. These policies can include conditions based on factors such as IP address, VPC endpoint, or request encryption, providing granular control over S3 access.

Encryption is critical for protecting sensitive data both at rest and in transit. AWS Key Management Service (KMS) managed keys allow centralized, auditable encryption of S3 objects. KMS provides the ability to rotate keys automatically, define key policies, and log all cryptographic operations through AWS CloudTrail for audit purposes. HTTPS ensures that all data transmitted to and from S3 is encrypted in transit, preventing interception or eavesdropping. Combined, these features provide end-to-end security for S3 objects while simplifying key management and maintaining compliance.

Other options are less suitable for enterprise-grade security. A public S3 bucket exposes data to anyone with an internet connection, violating confidentiality requirements. Client-side encryption with manual key management places the burden of secure key storage, rotation, and access control on the application, increasing the risk of operational errors and data breaches. Server-side encryption with AES-256 alone encrypts data at rest but lacks centralized key management, fine-grained access control, and auditability provided by KMS.

Using IAM roles, bucket policies, and KMS-managed keys ensures that S3 storage is secure, auditable, and compliant. It reduces operational overhead, supports features like lifecycle management and versioning, and provides enterprise-grade protection while maintaining flexibility and scalability for modern applications.

The correct answer is A) S3 with IAM roles, bucket policies, and KMS-managed keys.

Question 71:

A company wants to build a serverless data pipeline that ingests, processes, and stores high-volume log data in near real-time. Which AWS services combination should be used?

Answer:

A) Amazon Kinesis Data Firehose, Lambda, S3
B) Amazon SQS with EC2 consumers
C) Amazon SNS with S3 triggers
D) RDS with batch ingestion

Explanation:

The correct answer is A) Amazon Kinesis Data Firehose, Lambda, S3.

Amazon Kinesis Data Firehose is designed for streaming data ingestion and delivery. It can scale automatically to handle high-volume log data from multiple sources, making it ideal for serverless architectures. Firehose provides durability by replicating data across multiple Availability Zones and ensures reliable delivery to S3, Redshift, Elasticsearch Service, or third-party destinations.

Lambda can process incoming data in real time, performing transformations, filtering, or enrichment before data is persisted. Using serverless compute eliminates the need to manage EC2 instances, allowing the architecture to scale automatically in response to fluctuating workloads. Lambda integrates seamlessly with Firehose, enabling complex processing logic without operational overhead.

Option B, SQS with EC2 consumers, introduces operational complexity since EC2 instances must be provisioned, monitored, and scaled manually. Handling dynamic workloads efficiently requires careful capacity planning, which increases the risk of overprovisioning or underprovisioning.

Option C, SNS with S3 triggers, is suitable for notifications or event-driven processing but lacks the high-throughput, ordered, and durable ingestion capabilities needed for streaming log data at scale. SNS cannot provide sequential processing guarantees or handle millions of events per second efficiently.

Option D, RDS with batch ingestion, is not suitable for real-time streaming workloads. Batch processing introduces latency, lacks scalability for unpredictable spikes, and does not provide native durability or ordering guarantees.

This architecture ensures that logs are ingested in near real-time, transformed, and stored reliably. S3 provides virtually unlimited storage and lifecycle management, enabling cost-efficient retention policies, versioning, and lifecycle transitions to Glacier for long-term storage. Data stored in S3 can be further analyzed using Athena, Redshift Spectrum, or EMR, providing actionable insights without moving data.

Security is enforced at multiple layers. IAM roles grant least-privilege access to Firehose and Lambda, while KMS encryption ensures that data at rest is secure. HTTPS/TLS encrypts data in transit, protecting sensitive information. CloudTrail logs all administrative and data access actions for auditing and compliance, while CloudWatch metrics and alarms allow monitoring of throughput, latency, and error rates.

This approach aligns with AWS Well-Architected Framework pillars, including operational excellence, reliability, performance efficiency, security, and cost optimization. By leveraging managed, serverless services, organizations can minimize operational overhead, reduce latency, and ensure high availability while scaling to meet unpredictable workloads.

In SAP-C02 scenarios, this solution demonstrates best practices for designing event-driven, serverless, and highly available data pipelines. It highlights the integration of ingestion, transformation, and storage services while ensuring reliability, scalability, and cost efficiency for real-time log processing and analytics.

Question 72:

A company is building a globally distributed web application and needs to ensure low latency for read-heavy workloads. Which AWS architecture is most appropriate?

Answer:

A) DynamoDB global tables with DAX caching
B) Single-region RDS with Read Replicas
C) Multi-AZ RDS without caching
D) S3 static website hosting

Explanation:

The correct answer is A) DynamoDB global tables with DAX caching.

DynamoDB global tables provide a fully managed, multi-region, multi-master NoSQL database. Each region contains a full replica of the table, allowing applications to read and write locally while automatically replicating changes to other regions. This ensures low-latency reads for users worldwide and provides resilience against regional failures.

DAX adds an in-memory caching layer for DynamoDB, reducing read latency from milliseconds to microseconds. It supports write-through caching, ensuring that changes are propagated correctly and consistently to the database. This combination allows global read-heavy workloads to scale efficiently while maintaining low latency, high availability, and fault tolerance.

Option B, single-region RDS with Read Replicas, cannot provide low latency for global users outside the primary region. Replication lag and network distance can impact responsiveness. Option C, Multi-AZ RDS without caching, ensures high availability but does not address latency for globally distributed read workloads. Option D, S3 static website hosting, is suitable for static content but cannot store and query dynamic application data.

DynamoDB global tables integrate with IAM for secure access and KMS for encryption at rest. Data in transit is protected by TLS. CloudWatch provides detailed monitoring, including read/write throughput, replication lag, and throttled requests. With global tables, applications can automatically handle failover in the event of regional outages, ensuring business continuity.

Caching with DAX reduces database load, improves application responsiveness, and enables predictable performance for read-heavy workloads. This architecture follows AWS best practices for global performance, operational simplicity, cost optimization, and resilience.

For SAP-C02 scenarios, this demonstrates the correct approach for designing globally distributed applications with low-latency access, high availability, and scalability, incorporating caching and multi-region replication to ensure consistent user experiences worldwide.

Question 73:

A company wants to implement a multi-region disaster recovery solution for a mission-critical transactional database with an RPO of under 1 minute. Which solution is most suitable?

Answer:

A) Amazon Aurora Global Database
B) Cross-region RDS snapshots
C) Manual database replication
D) Standby EC2 database servers

Explanation:

The correct answer is A) Amazon Aurora Global Database.

Aurora Global Database provides asynchronous replication across multiple AWS regions with replication lag typically under one second, which meets the sub-1-minute RPO requirement. Failover is automated, ensuring rapid recovery (RTO) in the event of a regional outage. Secondary regions can also serve read queries, reducing latency for global users and providing additional operational capacity.

Option B, cross-region RDS snapshots, introduces delays because restoring from snapshots can take several minutes to hours, which violates strict RPO and RTO requirements. Option C, manual replication, is operationally complex, error-prone, and cannot guarantee transactional consistency during failover. Option D, standby EC2 database servers, require manual synchronization and failover processes, making them unsuitable for stringent recovery objectives.

Aurora maintains six copies of data across three Availability Zones per region, providing fault tolerance at the storage layer. Security is ensured with IAM access control, KMS encryption at rest, and TLS encryption in transit. CloudWatch monitors replication lag, query performance, and instance health, while CloudTrail logs administrative actions for auditing.

Secondary regions can be leveraged for read scaling, improving global performance and reducing latency. Aurora automatically handles failover with minimal human intervention, simplifying operations and ensuring business continuity. This architecture also supports automated storage scaling and continuous backups, further reducing operational complexity.

For SAP-C02 exam scenarios, this architecture demonstrates best practices for multi-region disaster recovery, low-latency global access, high availability, and operational simplicity for mission-critical relational databases. It highlights the advantages of managed services like Aurora in achieving stringent RPO and RTO objectives.

Question 74:

A company wants to implement a real-time analytics pipeline for clickstream data that is durable, scalable, and low-latency. Which AWS solution is most appropriate?

Answer:

A) Amazon Kinesis Data Streams with Lambda
B) Amazon SQS with EC2 consumers
C) Amazon SNS with S3 triggers
D) RDS with batch ingestion

Explanation:

The correct answer is A) Amazon Kinesis Data Streams with Lambda.

Kinesis Data Streams is designed for high-throughput, real-time ingestion of streaming data. Each stream is divided into shards, allowing parallel processing. Lambda consumers can process data as it arrives, performing analytics, transformations, or aggregations. Kinesis replicates data across multiple Availability Zones to ensure durability and fault tolerance.

Option B, SQS with EC2 consumers, requires manual management of EC2 instances and scaling logic, making it less suitable for real-time, high-throughput processing. Option C, SNS with S3 triggers, cannot guarantee message ordering, durability, or processing reliability at scale. Option D, RDS batch ingestion, introduces significant latency and is not suitable for near real-time analytics.

Kinesis supports data replay, allowing downstream systems to reprocess data if needed. CloudWatch metrics monitor shard health, processing lag, and Lambda function performance. IAM policies control access to streams, KMS encryption secures data at rest, and TLS ensures encryption in transit.

This architecture enables scalable, low-latency processing for high-volume clickstream data. It supports operational simplicity, durability, and integration with downstream storage and analytics tools like S3, Redshift, and EMR. For SAP-C02 scenarios, this demonstrates best practices for building serverless, real-time data pipelines with high availability and reliability.

Question 75:

A company is designing a multi-region web application that must remain available even if an entire AWS region fails. Users are globally distributed, and low latency is required. Which solution should a solutions architect implement?

Answer:

A) Multi-region deployment with Route 53 latency-based routing, CloudFront, and multi-region ALBs
B) Single-region ALB with CloudFront
C) S3 static website hosting with Transfer Acceleration
D) Global Accelerator with single-region EC2

Explanation:

The correct answer is A) Multi-region deployment with Route 53 latency-based routing, CloudFront, and multi-region ALBs.

This architecture ensures both high availability and low latency for global users. Route 53 latency-based routing directs users to the AWS region that provides the lowest network latency, improving response times significantly. Health checks are continuously performed to detect regional failures. In the event of a failure, Route 53 automatically reroutes traffic to healthy regions, providing resilience and minimal disruption.

CloudFront acts as a global content delivery network (CDN). It caches both static and dynamic content closer to end users. Dynamic content can also leverage regional edge caches, reducing latency for frequently requested content. CloudFront’s caching mechanism reduces load on the origin servers, improving application scalability and performance while lowering costs.

Multi-region ALBs provide high availability across multiple Availability Zones in each region. Within a region, the ALB distributes incoming traffic to multiple backend instances, ensuring fault tolerance. By deploying ALBs in multiple regions, the architecture can survive a complete regional outage. This deployment ensures continuous availability of dynamic application components while users are directed to the nearest active region.

Option B, a single-region ALB with CloudFront, only provides high availability within that region and cannot withstand a full regional outage. While CloudFront improves static content delivery, dynamic requests are still routed to a single region, creating a single point of failure.

Option C, S3 static website hosting with Transfer Acceleration, is suitable only for static websites. It cannot host dynamic application workloads or provide real-time failover for backend services. Transfer Acceleration optimizes content delivery, but it does not address dynamic application availability.

Option D, Global Accelerator with single-region EC2, improves network performance by routing users over the AWS global network. However, it cannot protect against regional failures because all application workloads reside in one region. It also cannot scale dynamically for multi-region workloads.

From a security perspective, this architecture can integrate AWS WAF and AWS Shield to protect against web attacks and DDoS threats. IAM policies and KMS encryption provide secure access to backend resources, while CloudTrail auditing ensures operational accountability. CloudWatch monitors ALB health, EC2 instances, CloudFront cache performance, and Route 53 routing, enabling proactive management of global traffic.

Operational efficiency is improved by leveraging managed services that automatically scale with traffic. This approach reduces the need for manual intervention, prevents downtime, and allows teams to focus on business logic rather than infrastructure management. By deploying applications across multiple regions, organizations also improve disaster recovery capabilities and comply with regulatory requirements for redundancy and high availability.

In the context of SAP-C02 exam scenarios, this solution demonstrates a multi-region, fault-tolerant architecture with low-latency access for globally distributed users. It emphasizes high availability, automated failover, caching strategies, security best practices, and operational monitoring, aligning with the AWS Well-Architected Framework pillars of reliability, performance efficiency, security, operational excellence, and cost optimization.

Question 76:

A company needs to implement a disaster recovery solution for a relational database with strict RPO (<1 min) and RTO (<15 min) requirements. Which solution is best?

Answer:

A) Amazon Aurora Global Database
B) Cross-region RDS snapshots
C) Manual database replication using EC2
D) Standby EC2 servers in another region

Explanation:

The correct answer is A) Amazon Aurora Global Database.

Aurora Global Database is specifically designed for multi-region disaster recovery with near-zero replication lag (typically under one second). This architecture satisfies stringent RPO requirements, ensuring minimal data loss during a regional outage. Failover to the secondary region can occur automatically, allowing applications to resume operations quickly, meeting the RTO requirements.

Option B, cross-region RDS snapshots, is unsuitable for strict RPO/RTO scenarios because snapshot-based recovery introduces significant delays. Snapshot restores take time, which can exceed critical recovery time objectives. Option C, manual replication using EC2 instances, adds operational complexity and is prone to errors. Maintaining consistency and synchronization manually is challenging and can result in data loss. Option D, standby EC2 servers, require manual failover, which does not meet strict recovery objectives.

Aurora replicates six copies of data across three Availability Zones in each region. Data is encrypted at rest using KMS and in transit using TLS, ensuring security and compliance. CloudWatch monitors replication lag, instance health, and query performance. CloudTrail provides auditing of administrative actions.

Secondary regions are also used to scale read operations, improving global performance and reducing latency. Aurora automatically handles failover, reducing operational overhead and human error. The architecture also supports automatic storage scaling and continuous backups, enhancing reliability.

For SAP-C02 scenarios, this solution illustrates a best-practice multi-region relational database architecture that provides high availability, low-latency global access, and disaster recovery with minimal operational effort. It demonstrates knowledge of managed multi-region replication, fault-tolerant database design, and operational excellence.

Question 77:

A company wants to implement a serverless architecture to process IoT telemetry data, apply business logic, and store results. Which combination of AWS services is most appropriate?

Answer:

A) AWS IoT Core, Lambda, DynamoDB
B) SQS with EC2 consumers
C) SNS with S3 triggers
D) RDS with manual ingestion

Explanation:

The correct answer is A) AWS IoT Core, Lambda, DynamoDB.

AWS IoT Core provides a highly scalable, secure, and fully managed ingestion layer for IoT device data. It supports millions of devices simultaneously and ensures secure device authentication and message delivery. Data is delivered to AWS Lambda functions for serverless processing. Lambda enables real-time execution of business logic without provisioning or managing servers, scaling automatically with traffic volume.

DynamoDB serves as the backend database. It provides a fully managed, low-latency NoSQL solution that can scale horizontally to handle millions of requests per second. Its flexible data model accommodates IoT telemetry, and integration with DynamoDB Streams allows event-driven workflows for further processing.

Option B, SQS with EC2 consumers, adds operational complexity. EC2 instances must be provisioned, monitored, and scaled manually, which increases cost and overhead. Option C, SNS with S3 triggers, is not optimized for high-throughput real-time IoT data, and cannot guarantee durability and ordering. Option D, RDS with manual ingestion, introduces latency and does not scale efficiently for high-velocity IoT workloads.

This architecture also incorporates best practices for security, durability, and monitoring. IAM roles enforce least-privilege access, TLS encrypts data in transit, and KMS ensures encryption at rest. CloudWatch tracks Lambda execution metrics, DynamoDB performance, and IoT Core delivery success. CloudTrail provides detailed logs for auditing.

Operational simplicity is a key advantage. No servers need to be managed, and the architecture automatically scales in response to device traffic. IoT Core ensures reliable ingestion, Lambda handles processing with automatic retries, and DynamoDB provides fast, durable storage.

For SAP-C02 scenarios, this demonstrates the correct approach to serverless, event-driven IoT architectures. It emphasizes managed service integration, scalability, low-latency processing, fault tolerance, security, and observability.

Question 78:

A company wants to reduce read latency for a globally distributed DynamoDB table. Which solution is best?

Answer:

A) DynamoDB Accelerator (DAX)
B) ElastiCache Redis
C) S3 Transfer Acceleration
D) RDS Read Replicas

Explanation:

The correct answer is A) DynamoDB Accelerator (DAX).

DAX is a fully managed, in-memory caching solution for DynamoDB. It reduces read latency from milliseconds to microseconds while providing write-through consistency. DAX clusters scale horizontally and provide high availability with automatic failover.

Option B, ElastiCache Redis, requires manual integration with the application and does not provide native DynamoDB integration, increasing operational overhead. Option C, S3 Transfer Acceleration, only improves object transfer speed, not database query performance. Option D, RDS Read Replicas, are for relational databases and cannot accelerate DynamoDB queries.

DAX integrates with IAM for secure access, TLS for in-transit encryption, and KMS for data at rest. CloudWatch monitors cache hit rates, latency, and node health. By offloading read requests from DynamoDB, DAX improves performance, reduces throttling, and supports read-heavy workloads at global scale.

For SAP-C02 exam scenarios, this demonstrates best practices for improving read performance in global, high-throughput NoSQL applications, emphasizing operational simplicity, low latency, and fault tolerance.

Question 79:

A company wants exactly-once processing for high-throughput event-driven workloads. Which AWS service is most appropriate?

Answer:

A) Amazon Kinesis Data Streams with Lambda
B) SQS standard queues with Lambda
C) SNS with S3 triggers
D) DynamoDB Streams

Explanation:

The correct answer is A) Amazon Kinesis Data Streams with Lambda.

Kinesis ensures durable, ordered delivery at the shard level. Lambda consumers can checkpoint records to ensure exactly-once processing semantics. Data is replicated across multiple Availability Zones for durability.

Option B, SQS standard queues, provide at-least-once delivery and may result in duplicate processing. FIFO queues enforce order but are limited in throughput. Option C, SNS, does not guarantee ordering or exactly-once semantics. Option D, DynamoDB Streams, only captures table changes and is limited to DynamoDB events.

Kinesis integrates with CloudWatch for monitoring, KMS for encryption, and supports extended retention for replayability. This architecture ensures high-throughput, low-latency, reliable processing suitable for critical workloads such as financial transactions or IoT telemetry.

For SAP-C02 scenarios, it demonstrates best practices for event-driven, serverless applications with exactly-once guarantees, durability, scalability, and observability.

Question 80:

A company needs to orchestrate a serverless workflow with multiple Lambda functions, error handling, retries, and conditional branching. Which service should be used?

Answer:

A) AWS Step Functions
B) Amazon SWF
C) AWS Batch
D) Amazon SQS

Explanation:

The correct answer is A) AWS Step Functions.

AWS Step Functions provides serverless workflow orchestration using state machines. It supports sequential, parallel, and conditional execution, built-in error handling, retries, and timeouts. It integrates with Lambda, ECS, SNS, and other AWS services, providing seamless coordination of complex workflows.

Option B, SWF, is legacy and requires managing worker nodes. Option C, Batch, is designed for batch jobs, not real-time orchestration. Option D, SQS, is a messaging service without workflow orchestration.

Step Functions also provides operational visibility through execution history, CloudWatch metrics, and X-Ray tracing. Standard workflows are durable and track execution state for long-running processes, while express workflows support high-throughput short-duration tasks. IAM policies and KMS encryption secure workflow execution.

This architecture simplifies complex serverless orchestration, reduces operational overhead, improves reliability, and ensures observability. It aligns with SAP-C02 exam best practices for serverless, scalable, and resilient workflow design.