Amazon AWS Certified Solutions Architect – Professional SAP-C02 Exam Dumps and Practice Test Questions Set 7 Q121-140

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Question 121:

A company wants to implement a cost-effective, serverless data processing pipeline for logs stored in S3 that allows analytics without provisioning servers. Which solution is most appropriate?

Answer:

A) S3 event triggers with Lambda and Athena
B) EC2 Auto Scaling with custom scripts
C) RDS batch ingestion
D) SNS notifications to EC2

Explanation:

The correct answer is A) S3 event triggers with Lambda and Athena.

S3 provides durable storage for logs with replication across multiple Availability Zones. Using S3 event notifications, Lambda functions can automatically trigger when new log files are uploaded. This creates a fully serverless, event-driven architecture that eliminates the need to manually poll for new files or schedule batch jobs.

Lambda functions can transform, enrich, or aggregate the data and store it in query-optimized formats like Parquet or ORC for analytics. Athena enables serverless, SQL-based querying directly on S3 objects without provisioning any infrastructure. Users only pay for the data scanned during queries, ensuring cost efficiency.

Option B, EC2 Auto Scaling with scripts, introduces operational complexity for instance management, patching, and scaling. Option C, RDS batch ingestion, requires managing database capacity and can incur delays in analytics processing. Option D, SNS to EC2, requires server management and does not scale automatically for processing high-volume log data.

Security is maintained using IAM roles with least privilege, KMS encryption for data at rest, and TLS for encryption in transit. CloudWatch monitors Lambda execution, S3 storage metrics, and Athena query performance. CloudTrail provides auditing for compliance.

Operational efficiency is achieved through automation; the serverless design ensures the architecture scales dynamically with incoming logs, while cost optimization arises from pay-per-use billing.

For SAP-C02 scenarios, this demonstrates best practices for serverless, event-driven analytics pipelines, emphasizing scalability, reliability, cost optimization, and operational simplicity. By using S3, Lambda, and Athena together, businesses can efficiently ingest, transform, and analyze massive datasets without maintaining any servers.

Question 122:

A company needs a globally distributed API for low-latency user access with automatic failover. Which architecture is most suitable?

Answer:

A) API Gateway with multi-region Lambda backends and Route 53 latency-based routing
B) Single-region EC2 API behind ALB
C) SNS with Lambda in a single region
D) RDS with read replicas

Explanation:

The correct answer is A) API Gateway with multi-region Lambda backends and Route 53 latency-based routing.

API Gateway acts as a fully managed front door, providing request routing, throttling, authentication, and caching. Lambda functions in multiple regions handle the API requests and scale automatically, eliminating the need to manage servers.

Route 53 latency-based routing directs users to the closest healthy region, ensuring low-latency access globally. Health checks enable automatic failover, maintaining availability even if one region fails.

Option B, a single-region EC2 API, introduces a single point of failure and cannot provide global low-latency performance. Option C, SNS with Lambda in one region, is asynchronous and unsuitable for synchronous API requests. Option D, RDS read replicas, addresses database scaling but does not solve API low-latency or failover needs.

CloudWatch monitors API Gateway and Lambda metrics, while IAM, KMS, and TLS provide secure access and data protection. CloudTrail enables auditing.

Operational simplicity is achieved as Lambda scales automatically with user demand. Cost optimization arises from the serverless model where you pay only for execution and routing, rather than idle servers.

For SAP-C02 exam purposes, this architecture demonstrates best practices for globally distributed, serverless APIs with fault tolerance, low-latency performance, and minimal operational overhead. It aligns with pillars of reliability, performance efficiency, operational excellence, cost optimization, and security.

Question 123:

A company wants to implement a high-performance caching layer for DynamoDB to reduce read latency and prevent throttling. Which solution is best?

Answer:

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

Explanation:

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

DAX is a fully managed, in-memory caching solution designed specifically for DynamoDB. It reduces read latency from milliseconds to microseconds and operates as a write-through cache, maintaining consistency with the underlying table. This ensures that applications with high read demands, such as e-commerce or gaming, maintain predictable low-latency performance.

ElastiCache Redis (option B) requires application-level integration, increasing complexity and operational overhead. RDS Read Replicas (option C) are relational and irrelevant for DynamoDB. S3 Transfer Acceleration (option D) optimizes S3 object transfers but does not accelerate database queries.

DAX supports multi-AZ deployments for high availability and failover. CloudWatch monitors cache hit ratios, latency, and node health. IAM, KMS, and TLS secure the cluster. Operationally, DAX offloads reads from DynamoDB, reducing throttling, enhancing performance, and ensuring predictable global application performance.

SAP-C02 exam scenarios emphasize performance efficiency, operational simplicity, and reliability, which are all demonstrated by implementing DAX. This architecture illustrates best practices for high-throughput, low-latency NoSQL applications while maintaining fault tolerance and operational simplicity.

Question 124:

A company wants to orchestrate a serverless workflow with conditional logic, retries, and error handling. Which AWS 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.

Step Functions is a fully managed orchestration service that supports sequential, parallel, and conditional execution of serverless workflows. Lambda functions, ECS tasks, and other AWS services can be integrated to build complex pipelines with retries, timeouts, and error handling.

SWF (option B) is legacy and requires manual worker management. AWS Batch (option C) is for batch workloads and cannot orchestrate serverless workflows. Amazon SQS (option D) is a messaging service, not an orchestration service.

CloudWatch monitors workflow execution, errors, and duration. X-Ray provides tracing for debugging. IAM roles and KMS provide secure access. CloudTrail logs actions for auditing.

Operational simplicity is achieved by managing workflows declaratively rather than manually coding orchestration logic. SAP-C02 exam scenarios highlight serverless orchestration best practices, focusing on reliability, scalability, and operational excellence.

Question 125:

A company wants a highly available relational database across multiple regions with minimal replication lag. Which AWS solution is most appropriate?

Answer:

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

Explanation:

The correct answer is A) Amazon Aurora Global Database.

Aurora Global Database provides low-latency, cross-region replication with typical replication lag under one second. Secondary regions can serve read requests, reducing latency for globally distributed users. In the event of a regional failure, a secondary region can be promoted to primary, ensuring high availability and disaster recovery.

Cross-region RDS snapshots (option B) are slow and do not meet stringent RPO/RTO requirements. Manual replication (option C) increases operational complexity and risk. Standby RDS in a single region (option D) does not provide multi-region fault tolerance.

CloudWatch monitors replication lag, instance health, and query performance. KMS encrypts data at rest, TLS secures data in transit, and CloudTrail logs administrative actions.

Operationally, Aurora Global Database reduces management overhead, scales automatically, and provides globally distributed read access. SAP-C02 exam scenarios use this architecture as a reference for multi-region relational database deployment best practices, emphasizing reliability, performance efficiency, operational excellence, and security.

Question 126:

A company needs to process millions of IoT messages per day in real time with minimal operational overhead. Which AWS solution is most suitable?

Answer:

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

Explanation:

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

AWS IoT Core is a fully managed, highly scalable service that ingests millions of device messages per day. It supports MQTT, HTTPS, and WebSocket protocols for device connectivity. IoT Core ensures secure device authentication using X.509 certificates, IAM policies, or custom authorizers, making sure only authorized devices can publish or subscribe to topics. This provides a highly secure and reliable ingestion layer.

Once messages are received, AWS Lambda functions are triggered for real-time processing. Lambda provides a serverless compute layer that scales automatically according to message volume. There is no need for server provisioning, patching, or scaling management, reducing operational overhead dramatically. Lambda can perform transformations, enrichments, validations, or filtering on the incoming IoT messages before storing results.

Processed messages are stored in Amazon DynamoDB, a fully managed, high-performance NoSQL database. DynamoDB is designed for millisecond-scale read and write operations and scales seamlessly to support millions of concurrent requests. Its integration with DynamoDB Streams allows downstream event-driven processing, analytics, or real-time monitoring.

Option B, SQS with EC2 consumers, introduces operational complexity due to server management, instance scaling, and fault tolerance considerations. Option C, SNS with S3 triggers, is asynchronous and unsuitable for high-throughput real-time processing. Option D, RDS batch ingestion, cannot handle real-time processing efficiently due to batch latency and limited scaling for high-throughput scenarios.

Security is a critical aspect of this architecture. IAM roles enforce least-privilege access for Lambda functions and IoT Core rules. DynamoDB tables can be encrypted at rest using AWS KMS, while TLS ensures encryption in transit between devices, IoT Core, Lambda, and DynamoDB. CloudTrail logs all administrative actions for auditing, supporting compliance requirements for security and regulatory standards.

Operational monitoring is provided by Amazon CloudWatch, which tracks IoT message ingestion rates, Lambda execution metrics, error rates, and DynamoDB throughput. Alarms can be set up to detect abnormal traffic patterns, message processing failures, or latency spikes. This proactive monitoring ensures the system can react to operational issues quickly and maintain reliability.

From a cost optimization perspective, this architecture is highly efficient. Lambda scales automatically with traffic, so you only pay for the compute resources you use. DynamoDB on-demand mode eliminates the need to provision capacity upfront, allowing the architecture to adapt dynamically to the variable message volume typical of IoT workloads. S3 can also be integrated for long-term message archival, further reducing costs for historical data storage.

This architecture aligns with AWS Well-Architected Framework pillars, including reliability, performance efficiency, operational excellence, security, and cost optimization. For SAP-C02 exam scenarios, it demonstrates best practices for real-time, serverless IoT processing, including scaling for high throughput, maintaining low operational overhead, ensuring durability and fault tolerance, and applying robust security controls.

Moreover, this architecture supports future-proofing. Additional AWS services like Amazon Kinesis Data Firehose or Amazon Timestream can be integrated to handle more complex analytics, long-term time-series storage, or machine learning predictions. By building on serverless principles, the company avoids infrastructure lock-in and operational overhead while gaining the flexibility to evolve the pipeline as business requirements change.

In conclusion, AWS IoT Core, Lambda, and DynamoDB together provide a fully managed, highly scalable, secure, and cost-efficient solution for real-time IoT message ingestion and processing, making it the ideal architecture for high-volume, mission-critical IoT applications, perfectly aligned with SAP-C02 exam objectives.

Question 127:

A company wants a highly available, globally distributed web application with minimal latency and automatic failover. Which AWS architecture is most suitable?

Answer:

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

Explanation:

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

This architecture ensures global availability, low latency, and fault tolerance. Multi-region Application Load Balancers (ALBs) distribute traffic across multiple Availability Zones within each region, maintaining high availability in the event of individual zone failures.

Amazon CloudFront acts as a global content delivery network (CDN), caching static and dynamic content at edge locations close to users. This reduces latency and offloads traffic from origin servers. CloudFront also integrates with Lambda@Edge for dynamic content personalization, which further improves response times for global users.

Route 53 latency-based routing directs users to the closest healthy region, reducing latency for international users. Health checks enable automatic failover, ensuring the application remains available even if an entire region fails.

Option B, a single-region ALB with EC2 Auto Scaling, cannot provide global failover and creates a single point of failure. Option C, Global Accelerator with a single-region EC2 deployment, optimizes network performance but does not protect against regional outages. Option D, S3 static website with Transfer Acceleration, is suitable only for static content and cannot serve dynamic workloads.

Security is enforced through IAM roles, KMS encryption, TLS, AWS WAF for web application security, and AWS Shield for DDoS mitigation. CloudTrail provides auditing for compliance purposes.

Operational monitoring is achieved via CloudWatch, which tracks ALB request metrics, CloudFront cache hit ratios, Route 53 health check statuses, and Lambda@Edge executions. Alarms can be configured to detect latency spikes or region-level outages.

This architecture is also cost-efficient, leveraging CloudFront caching to reduce origin load and ALB utilization. Multi-region deployment costs are balanced by the improved performance and high availability benefits.

For SAP-C02 exam purposes, this demonstrates best practices for building globally distributed, high-performance web applications that are reliable, scalable, operationally efficient, secure, and cost-effective.

Question 128:

A company needs a real-time analytics pipeline for processing streaming e-commerce transactions with durability, scalability, and exactly-once processing. Which solution is best?

Answer:

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

Explanation:

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

Amazon Kinesis Data Streams is a fully managed, real-time streaming solution capable of ingesting millions of events per second from multiple sources. It ensures durability by replicating data across multiple Availability Zones, making it highly fault-tolerant and resilient to failures. Kinesis maintains ordered delivery at the shard level, which is essential for e-commerce transactions where event order impacts inventory management, financial reconciliation, and downstream analytics.

Lambda functions serve as the compute layer, processing streams in real-time. Lambda automatically scales to match the volume of incoming data without the need for server management. It also provides checkpointing functionality that ensures exactly-once processing semantics, which is critical for accurate financial transactions, inventory updates, and analytics reporting.

Option B, SQS standard queue with Lambda, provides at-least-once delivery, which may result in duplicate processing. Although SQS FIFO queues maintain order, their throughput is limited relative to Kinesis shards, making them less ideal for high-volume e-commerce streams. Option C, SNS with S3 triggers, is more suitable for asynchronous notifications and batch processing rather than real-time ordered transaction streams. Option D, DynamoDB Streams, is restricted to capturing changes within DynamoDB tables and is not a general-purpose streaming solution for diverse e-commerce workloads.

Security is a key consideration. IAM roles ensure least-privilege access to Kinesis streams and Lambda functions. KMS handles encryption at rest for both the stream and any downstream storage, while TLS encrypts data in transit to prevent interception. CloudTrail logs all administrative actions for auditing and compliance, ensuring traceability for regulatory requirements.

Operational considerations include scaling and failure handling. Kinesis automatically replicates data across multiple AZs, ensuring durability. Lambda retries failed events and provides detailed logs for troubleshooting. Shard splitting and merging allow horizontal scaling to accommodate spikes in order volume, ensuring consistent performance even during peak traffic periods such as Black Friday sales or holiday campaigns.

Cost optimization is inherent in the serverless approach. Billing is based on the volume of data ingested and Lambda execution time, eliminating idle server costs. The architecture adapts dynamically to changing workloads, reducing waste and allowing efficient use of resources.

This architecture aligns with the AWS Well-Architected Framework pillars, including reliability, operational excellence, performance efficiency, security, and cost optimization. For SAP-C02 exam scenarios, it demonstrates best practices for building real-time, event-driven, serverless architectures capable of handling large-scale transactional workloads with exactly-once processing guarantees.

Integration with additional AWS services can enhance functionality. For instance, processed data can be stored in Amazon S3 for archival and long-term analytics or loaded into Redshift for complex reporting. Amazon CloudWatch provides monitoring of stream lag, Lambda performance, and error rates, while AWS X-Ray can trace the event flow for debugging and optimization.

Ultimately, this solution is scalable, durable, secure, cost-effective, and operationally simple, making it the ideal architecture for real-time e-commerce transaction processing. It ensures reliability, integrity, and operational efficiency, which are critical factors assessed in the SAP-C02 exam.

Question 129:

A company wants a globally distributed, low-latency cache for a high-traffic DynamoDB application to reduce read latency. Which solution is most appropriate?

Answer:

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

Explanation:

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

DAX is a fully managed, in-memory caching service designed specifically for DynamoDB. It reduces read latency from milliseconds to microseconds while providing a write-through caching mechanism, ensuring consistency with the underlying table. This capability is essential for high-traffic applications such as e-commerce, gaming leaderboards, or IoT dashboards, where low-latency access is crucial for user experience and operational efficiency.

ElastiCache Redis (option B) is a general-purpose caching solution but requires additional integration logic to maintain consistency with DynamoDB, increasing operational complexity. RDS Read Replicas (option C) are specific to relational databases and cannot accelerate NoSQL workloads. S3 Transfer Acceleration (option D) is irrelevant for database queries, as it only optimizes object transfers.

DAX supports multi-AZ deployments, providing automatic failover and high availability. CloudWatch metrics enable monitoring of cache hit ratios, latency, throughput, and node health. Security is enforced through IAM roles for least-privilege access, KMS encryption for data at rest, and TLS for in-transit encryption.

Operationally, DAX offloads a significant portion of read traffic from DynamoDB, preventing throttling and improving overall system responsiveness. Applications can maintain predictable performance even under sudden spikes in traffic, reducing the risk of service degradation during peak usage periods.

Cost optimization is achieved because DAX reduces read requests to DynamoDB, which lowers overall operational costs. Its serverless scaling capability ensures that resources are allocated efficiently, matching demand without over-provisioning.

For SAP-C02 exam scenarios, this architecture illustrates best practices for globally distributed, high-performance NoSQL applications, emphasizing performance efficiency, reliability, operational simplicity, and cost optimization. It also demonstrates the ability to design architectures that can scale dynamically and provide low-latency access across multiple regions.

Additionally, DAX integrates seamlessly with monitoring and operational tooling. Developers can use CloudWatch alarms to detect anomalies such as high cache miss rates, monitor latency, and take preemptive actions. AWS X-Ray can trace requests through the caching layer, providing end-to-end visibility for troubleshooting performance bottlenecks.

Ultimately, DAX provides a scalable, low-latency caching layer that significantly improves the performance of DynamoDB-backed applications, while maintaining durability, consistency, and operational simplicity, which is aligned with SAP-C02 exam principles for high-performance application design.

Question 130:

A company wants a serverless orchestration for multiple Lambda functions with retries, conditional branching, and error handling. Which service is best?

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 is a fully managed, serverless orchestration service designed for building complex workflows with multiple steps, branching logic, retries, error handling, and timeouts. It integrates seamlessly with Lambda, ECS tasks, SNS, SQS, and other AWS services, allowing developers to construct complex serverless applications without managing infrastructure.

Amazon SWF (option B) is a legacy service requiring manual worker node management, increasing operational overhead. AWS Batch (option C) is designed for batch jobs and cannot orchestrate event-driven, serverless workflows effectively. Amazon SQS (option D) is a messaging service and does not provide workflow orchestration or error handling capabilities.

Step Functions provides visual workflow design, which allows easy understanding of state transitions, error handling paths, retries, and timeouts. CloudWatch monitors workflow execution metrics, error counts, and duration, while AWS X-Ray allows tracing of each step for performance and debugging. IAM policies enforce least-privilege access, and KMS ensures encryption for sensitive data within workflow parameters.

Operationally, Step Functions reduces human intervention and increases workflow reliability by automatically retrying failed steps, handling exceptions, and ensuring correct sequencing. This allows developers to focus on business logic rather than managing orchestration complexity. The service can scale automatically to support high-throughput scenarios, enabling large-scale serverless applications to function seamlessly.

Cost optimization is achieved because Step Functions is serverless, and you pay only for the transitions executed in the workflow. There is no need to provision EC2 instances, ECS clusters, or other compute resources.

For SAP-C02 exam scenarios, this demonstrates best practices for serverless orchestration, including reliability, operational efficiency, scalability, and security. Step Functions workflows can also integrate with monitoring and alerting mechanisms, ensuring the architecture is robust, fault-tolerant, and compliant.

Question 131:

A company wants to implement a multi-region, low-latency relational database for global users with disaster recovery capabilities. Which solution is most appropriate?

Answer:

A) Amazon Aurora Global Database
B) RDS cross-region snapshots
C) EC2-hosted MySQL with manual replication
D) Standby RDS in a single region

Explanation:

The correct answer is A) Amazon Aurora Global Database.

Amazon Aurora Global Database is designed to provide low-latency, multi-region replication for relational databases. It enables a primary cluster in one region and read-only secondary clusters in other regions. Typical replication lag is less than one second, which ensures near real-time replication for global applications. Secondary regions can serve read queries locally, reducing latency for users far from the primary region.

In case of a regional outage, a secondary region can be promoted to primary to maintain availability, significantly reducing downtime and achieving low Recovery Time Objective (RTO) and Recovery Point Objective (RPO) targets. Aurora replicates data across multiple Availability Zones within each region, ensuring high durability and resilience against infrastructure failures.

Option B, RDS cross-region snapshots, is suitable for backups but does not provide continuous replication. Restoring a snapshot takes time and increases RTO, making it unsuitable for mission-critical applications. Option C, EC2-hosted MySQL with manual replication, introduces operational complexity, requires ongoing monitoring, and is prone to human error. Option D, standby RDS in a single region, only provides intra-region failover and does not protect against full regional outages.

Security is enforced using IAM roles for access control, KMS encryption for data at rest, and TLS for in-transit encryption. CloudTrail logs all administrative actions, providing auditing and compliance support. CloudWatch monitors replication lag, instance health, CPU utilization, and query performance. Alarms can alert administrators to anomalies, ensuring operational reliability.

Operational benefits include simplified management, automatic failover within regions, and high availability across regions. Aurora Global Database automatically scales storage as data grows, reducing administrative overhead. The serverless Aurora variant can further optimize costs by adjusting capacity based on workload demand.

From a SAP-C02 exam perspective, this scenario demonstrates best practices for multi-region relational database deployment, emphasizing reliability, performance efficiency, operational excellence, security, and cost optimization. It showcases how AWS solutions enable high availability, disaster recovery, and low-latency access for global applications.

Aurora Global Database also supports read-intensive workloads, offloading read traffic from the primary region and providing a scalable solution for distributed applications such as SaaS platforms, e-commerce, financial systems, or IoT analytics dashboards. Using read-only replicas in multiple regions ensures consistent performance even during traffic spikes.

Integration with other AWS services like CloudFront, Route 53, and Lambda can further optimize application performance. For example, a global application can use Route 53 latency-based routing to direct traffic to the nearest region and leverage Aurora Global Database for local read queries while maintaining synchronous writes in the primary region.

Cost optimization is another advantage. Aurora reduces operational costs by eliminating manual replication management and scaling storage automatically. The pay-as-you-go pricing model ensures organizations pay only for the storage and compute resources they use, making it cost-efficient compared to maintaining self-managed database clusters in multiple regions.

In summary, Amazon Aurora Global Database provides a robust, scalable, secure, and low-latency solution for multi-region relational database deployment. It reduces operational complexity, ensures high availability, supports global users, and aligns with SAP-C02 exam best practices for reliability, performance efficiency, and disaster recovery.

Question 132:

A company wants a serverless real-time analytics pipeline for IoT data with minimal operational overhead and high scalability. Which architecture is most appropriate?

Answer:

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

Explanation:

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

AWS IoT Core provides a fully managed, scalable ingestion layer for IoT devices, supporting millions of simultaneous connections. Devices can securely publish data using MQTT, HTTPS, or WebSocket protocols. Authentication is enforced via X.509 certificates, IAM roles, or custom authorizers, ensuring secure access.

Lambda functions process messages in real-time, automatically scaling with workload demand. This serverless model eliminates operational overhead related to server provisioning, patching, or scaling. Lambda can filter, enrich, aggregate, or transform data before storing it in a highly durable database.

DynamoDB serves as a highly available NoSQL database that can handle millions of reads/writes per second with millisecond latency. DynamoDB Streams enables downstream processing, triggering Lambda functions for analytics, alerts, or further transformations.

Option B, SQS with EC2 consumers, introduces operational overhead and manual scaling. Option C, SNS with S3 triggers, is asynchronous and not optimized for high-throughput real-time analytics. Option D, RDS batch processing, introduces latency and requires database capacity management.

Security is enforced via IAM roles, KMS encryption for DynamoDB, and TLS for all in-transit data. CloudTrail ensures auditing, and CloudWatch monitors ingestion rates, Lambda execution metrics, and database throughput. Alerts can be set up for anomalies or failures.

Operationally, this architecture scales seamlessly with IoT traffic, maintains low latency, ensures durability, and minimizes operational effort. Cost optimization arises from pay-per-use billing for Lambda and DynamoDB, avoiding idle infrastructure costs.

For SAP-C02 scenarios, this demonstrates best practices for serverless, real-time IoT analytics, emphasizing reliability, operational efficiency, performance efficiency, cost optimization, and security.

The architecture can be extended to include Kinesis Data Firehose or Timestream for advanced analytics, allowing businesses to implement predictive maintenance, anomaly detection, and real-time dashboards. This approach ensures the company can ingest, process, and analyze massive IoT datasets without managing any servers, aligning with modern cloud-native design principles.

Question 133:

A company wants to reduce read latency for DynamoDB with a high-volume, globally distributed application. Which solution is most suitable?

Answer:

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

Explanation:

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

DAX provides a fully managed, in-memory caching layer specifically for DynamoDB, reducing read latency from milliseconds to microseconds. It operates as a write-through cache, ensuring consistency with the underlying table, which is critical for applications with high read demand like e-commerce or gaming leaderboards.

ElastiCache Redis (option B) is not integrated with DynamoDB by default, requiring application-level caching logic and additional operational management. RDS Read Replicas (option C) apply only to relational databases and cannot reduce latency for NoSQL queries. S3 Transfer Acceleration (option D) is designed for object transfers, not database query performance.

DAX supports multi-AZ deployments for high availability, with automatic failover if a node fails. CloudWatch provides metrics on cache hit ratio, node health, and latency. IAM, KMS, and TLS ensure secure access and encrypted communication.

Operationally, DAX offloads reads from DynamoDB, reducing throttling, improving performance, and ensuring predictable latency even during spikes. This enables applications to scale efficiently without manual intervention. Cost optimization arises from reduced read capacity consumption in DynamoDB and pay-as-you-go pricing for DAX nodes.

SAP-C02 exam principles emphasize performance efficiency, operational simplicity, reliability, and scalability. Using DAX aligns with these best practices while supporting globally distributed, high-performance workloads.

Question 134:

A company wants a globally distributed API with low-latency access for users and automatic failover. Which architecture is most appropriate?

Answer:

A) API Gateway with multi-region Lambda backends and Route 53 latency-based routing
B) Single-region EC2 API behind ALB
C) SNS with Lambda in one region
D) RDS with read replicas

Explanation:

The correct answer is A) API Gateway with multi-region Lambda backends and Route 53 latency-based routing.

API Gateway acts as a fully managed API front door, providing request routing, throttling, caching, authentication, and integration with backend services. Deploying Lambda functions in multiple AWS regions allows the system to scale automatically based on user demand while providing low-latency responses globally.

Route 53 latency-based routing ensures that users are directed to the nearest healthy region, minimizing response times and ensuring high availability even if one region experiences an outage. Health checks allow automatic failover to the next best-performing region.

Option B, a single-region EC2 API, creates a single point of failure and cannot guarantee global low-latency access. Option C, SNS with Lambda in a single region, is asynchronous and unsuitable for synchronous API calls. Option D, RDS with read replicas, addresses database scaling but does not solve the global API or failover requirements.

Security is enforced via IAM roles, KMS for encryption, and TLS for data in transit. AWS WAF protects against common web exploits, and AWS Shield mitigates DDoS attacks. CloudTrail logs all administrative actions for auditing and compliance purposes.

Operational monitoring is achieved with CloudWatch, which tracks API Gateway request metrics, Lambda function performance, and Route 53 health check status. Alerts can be set for abnormal latency, error rates, or regional outages, ensuring proactive operational management.

This architecture is highly scalable, as Lambda automatically adjusts to the number of requests, and operational overhead is minimal because infrastructure is fully managed. Costs are optimized through pay-per-use billing for Lambda and API Gateway requests, rather than provisioning and maintaining servers.

From a SAP-C02 exam perspective, this demonstrates best practices for designing globally distributed, highly available, low-latency, serverless architectures, emphasizing operational excellence, performance efficiency, reliability, security, and cost optimization.

This architecture can also be integrated with CloudFront and Lambda@Edge to further improve latency and caching for dynamic content, while supporting future scalability for additional regions or endpoints without requiring significant infrastructure changes.

Question 135:

A company wants to deploy a real-time IoT analytics solution with durability, scalability, and low operational overhead. Which architecture is best?

Answer:

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

Explanation:

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

AWS IoT Core provides a fully managed, secure, and scalable ingestion service for millions of IoT devices. Devices can connect using MQTT, HTTPS, or WebSockets, and authentication is enforced with X.509 certificates, IAM policies, or custom authorizers. This ensures that only authorized devices send messages, enhancing security.

Lambda functions process IoT messages in real time, providing a serverless compute layer that scales automatically with the message volume. Functions can transform, filter, aggregate, or enrich the data before storing it. Operational overhead is minimal since no servers need to be provisioned or managed.

DynamoDB serves as a highly available, low-latency NoSQL database for storing processed IoT data. DynamoDB Streams can trigger additional Lambda functions for downstream analytics, real-time dashboards, or alerting mechanisms. Multi-AZ replication ensures durability, and on-demand capacity allows automatic scaling for unpredictable workloads.

Option B, SQS with EC2 consumers, increases operational complexity due to instance management and manual scaling. Option C, SNS with S3 triggers, is asynchronous and not optimized for high-volume real-time IoT analytics. Option D, RDS batch processing, introduces latency and requires database capacity planning.

Security measures include IAM roles for least-privilege access, KMS encryption at rest, and TLS encryption in transit. CloudTrail enables auditing, and CloudWatch monitors ingestion rates, Lambda execution metrics, and DynamoDB throughput. Alerts can be configured for anomalies or failures.

Operationally, this architecture is highly scalable, fully managed, and cost-effective, leveraging serverless principles. It aligns with the AWS Well-Architected Framework pillars including operational excellence, reliability, performance efficiency, security, and cost optimization.

For SAP-C02 exam scenarios, this solution demonstrates real-time, serverless IoT analytics best practices, ensuring durability, scalability, low latency, and minimal operational effort. Future enhancements could include Kinesis Data Firehose for streaming analytics, Amazon Timestream for time-series data storage, and integration with SageMaker for predictive analytics.

Question 136:

A company wants to accelerate read performance for a high-traffic DynamoDB table while maintaining consistency. Which solution is most suitable?

Answer:

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

Explanation:

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

DAX provides a fully managed, in-memory caching layer designed specifically for DynamoDB. It reduces read latency from milliseconds to microseconds and maintains write-through consistency with the underlying DynamoDB table, ensuring applications always access the most current data.

ElastiCache Redis (option B) can provide caching but requires application-level integration and management to maintain consistency with DynamoDB, increasing operational complexity. RDS Read Replicas (option C) apply only to relational databases. S3 Transfer Acceleration (option D) is irrelevant for database queries.

DAX supports multi-AZ deployments for high availability and automatic failover. CloudWatch monitors cache hit ratios, node health, and latency, while IAM, KMS, and TLS ensure secure access and encrypted communication.

Operational benefits include reducing read load on DynamoDB, preventing throttling, and ensuring predictable latency under high traffic. Cost optimization occurs by lowering read capacity unit consumption and leveraging pay-as-you-go pricing for DAX nodes.

From a SAP-C02 exam perspective, DAX demonstrates performance efficiency, operational simplicity, reliability, and scalability for high-traffic NoSQL workloads. It supports globally distributed, low-latency applications and aligns with AWS best practices for caching strategies.

Question 137:

A company wants to orchestrate multiple serverless Lambda functions with conditional logic, retries, and error handling. Which service is best?

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 is a serverless orchestration service that enables the building of complex workflows with sequential or parallel tasks, branching logic, retries, error handling, and timeouts. It integrates seamlessly with Lambda, ECS, SNS, and other AWS services.

Amazon SWF (option B) is a legacy solution that requires manual worker management. AWS Batch (option C) is designed for batch jobs rather than real-time workflows. Amazon SQS (option D) is a messaging service and cannot orchestrate workflows.

Step Functions provides a visual workflow editor, making it easy to design and understand state transitions. CloudWatch monitors execution metrics, while X-Ray enables end-to-end tracing. IAM roles and KMS encryption provide security for workflow execution data.

Operationally, Step Functions reduces human intervention, ensures reliability, and provides automatic retries and error handling. Costs are minimized as the service is serverless and billed per state transition.

SAP-C02 exam scenarios highlight Step Functions as a best practice for serverless orchestration, focusing on operational excellence, reliability, security, and scalability. It simplifies complex workflows while minimizing infrastructure management.

Question 138:

A company wants a serverless, cost-effective pipeline for processing S3 log files in real time for analytics. Which solution is best?

Answer:

A) S3 event triggers with Lambda and Athena
B) EC2 Auto Scaling with custom scripts
C) RDS batch ingestion
D) SNS to EC2

Explanation:

The correct answer is A) S3 event triggers with Lambda and Athena.

S3 triggers invoke Lambda functions when new log files arrive. Lambda can transform, filter, or enrich logs, and Athena allows serverless SQL queries directly on S3, providing cost-efficient analytics without provisioning infrastructure.

EC2 Auto Scaling (option B) increases operational complexity. RDS batch ingestion (option C) introduces latency and requires capacity management. SNS to EC2 (option D) does not scale efficiently for real-time analytics.

Security, monitoring, cost optimization, and operational simplicity are achieved using IAM roles, KMS, TLS, CloudWatch, and CloudTrail.

SAP-C02 best practices include serverless, event-driven analytics pipelines with high scalability, durability, and minimal operational overhead.

Question 139:

A company wants to build a multi-region, high-availability web application with low latency. Which architecture is best?

Answer:

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

Explanation:

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

Multi-region ALBs distribute traffic within each region. CloudFront caches content globally to reduce latency. Route 53 latency-based routing directs users to the nearest healthy region for low latency and automatic failover.

Single-region ALB (option B) and Global Accelerator (option C) do not provide full regional failover. S3 Transfer Acceleration (option D) is suitable only for static content.

Security, monitoring, cost optimization, and operational efficiency are addressed through IAM, KMS, TLS, WAF, Shield, CloudWatch, and CloudTrail.

SAP-C02 exam scenarios emphasize global, highly available, low-latency application design aligning with performance, reliability, and operational excellence best practices.

Question 140:

A company wants a cost-efficient, serverless, real-time analytics pipeline for IoT sensor data. Which solution is ideal?

Answer:

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

Explanation:

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

IoT Core ingests sensor data securely and reliably. Lambda processes data in real-time, scaling automatically. DynamoDB stores processed results with low latency and durability.

SQS with EC2 (option B) increases operational overhead. SNS with S3 (option C) is asynchronous. RDS batch (option D) introduces latency and scaling challenges.

Security is enforced via IAM, KMS, TLS. CloudWatch provides metrics and alerts. CloudTrail ensures auditing. Cost optimization arises from serverless pay-per-use billing.

This architecture demonstrates best practices for serverless IoT analytics, emphasizing scalability, low latency, operational simplicity, reliability, and cost efficiency, aligning perfectly with SAP-C02 exam requirements.