Amazon AWS Certified Solutions Architect – Professional SAP-C02 Exam Dumps and Practice Test Questions Set 5 Q81-100

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Question 81:

A company wants to deploy a highly available web application in multiple AWS regions to improve global performance and ensure fault tolerance. Which solution should a solutions architect implement?

Answer:

A) Multi-region deployment with Route 53 latency-based routing and CloudFront
B) Single-region ALB with EC2 Auto Scaling
C) S3 static website hosting with Transfer Acceleration
D) EC2 instances in a single region behind a Global Accelerator

Explanation:

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

In multi-region deployments, applications are hosted in two or more AWS regions to ensure high availability and fault tolerance. Route 53 latency-based routing directs users to the region that provides the lowest network latency. It continuously monitors health and automatically reroutes traffic to healthy regions in case of failures, ensuring uninterrupted service.

CloudFront, a global content delivery network (CDN), caches static and dynamic content at edge locations closer to users. This reduces latency, decreases origin load, and improves the performance of web applications across the globe. CloudFront can also integrate with Lambda@Edge for dynamic content processing, further enhancing responsiveness and user experience.

Option B, a single-region ALB with EC2 Auto Scaling, can provide availability within a region but cannot protect against full regional outages. It may also result in higher latency for users located far from the deployed region. Option C, S3 static website hosting with Transfer Acceleration, is only suitable for static content and cannot handle dynamic web application workloads or global failover. Option D, EC2 instances in a single region behind Global Accelerator, improves network performance but does not provide high availability across multiple regions.

This architecture also integrates well with security services. AWS WAF and AWS Shield protect the application from web exploits and DDoS attacks. IAM policies and KMS ensure secure access control and data encryption. CloudWatch metrics monitor ALB performance, EC2 health, CloudFront cache hit ratios, and Route 53 health checks. Operational best practices are supported by automated scaling, global failover, and serverless caching at the edge.

By combining multi-region deployment, Route 53 latency-based routing, and CloudFront, the architecture achieves high availability, low latency, disaster recovery, and operational simplicity. This aligns with the AWS Well-Architected Framework pillars, including reliability, performance efficiency, operational excellence, security, and cost optimization. For SAP-C02 scenarios, it demonstrates best practices for globally distributed web applications designed for high traffic, low latency, and resilience.

Question 82:

A company needs to implement a scalable, serverless architecture to process millions of IoT events per day, apply transformations, and store results for analytics. Which AWS services combination is most suitable?

Answer:

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

Explanation:

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

AWS IoT Core provides a fully managed, scalable platform to ingest IoT device data. It supports millions of devices concurrently, ensuring reliable message delivery and secure authentication using X.509 certificates or IAM roles. IoT Core routes messages to Lambda functions or other AWS services for real-time processing.

Lambda functions execute business logic in response to events, scaling automatically to handle spikes in message volume. This serverless approach eliminates the need to manage EC2 instances, reducing operational complexity and cost. Transformations, filtering, or enrichment can be applied in near real-time before storing results in DynamoDB.

DynamoDB provides a highly available, fully managed NoSQL database with millisecond latency and automatic scaling to accommodate high-throughput workloads. Its flexible data model is well-suited for IoT telemetry, and DynamoDB Streams enable event-driven workflows for downstream processing or analytics pipelines.

Option B, SQS with EC2 consumers, introduces operational overhead. EC2 instances must be provisioned, monitored, and scaled to handle high-throughput workloads. Option C, SNS with S3 triggers, is unsuitable for high-volume IoT event processing due to limited ordering guarantees and lack of durable ingestion. Option D, RDS with batch ingestion, introduces latency and cannot scale efficiently for millions of messages per day.

Security is enforced through IAM roles, TLS for data in transit, and KMS encryption for data at rest. CloudWatch metrics monitor Lambda execution, IoT message delivery, and DynamoDB throughput. CloudTrail provides auditing of all management actions, enabling compliance with regulatory requirements.

This architecture enables scalable, resilient, and cost-efficient processing of IoT events. It reduces operational overhead, ensures durability, and supports serverless real-time data processing for analytics and monitoring purposes. For SAP-C02 exam scenarios, it demonstrates best practices for serverless, event-driven IoT architectures with low latency, operational simplicity, and high availability.

When a company needs to process millions of IoT events per day, apply transformations, and store the results for analytics, it is critical to design a scalable, low-latency, and fully managed architecture. IoT workloads are often unpredictable in volume, requiring services that can automatically scale to handle spikes in message traffic while minimizing operational overhead. AWS provides a variety of services for ingesting, processing, and storing IoT data, and a combination of AWS IoT Core, AWS Lambda, and DynamoDB is the most suitable solution.

AWS IoT Core acts as a fully managed, highly scalable platform for ingesting IoT device data. It supports millions of devices concurrently, ensuring reliable message delivery with low latency. IoT Core also provides secure authentication and authorization through X.509 certificates, IAM roles, and policies. Messages from devices can be routed directly to AWS Lambda, Amazon Kinesis, or other services, allowing real-time event processing without the need for intermediate storage or infrastructure.

AWS Lambda functions process incoming IoT events in a serverless manner. Lambda automatically scales in response to incoming event volume, allowing applications to handle bursts of IoT messages without manual provisioning. Lambda functions can perform transformations, filtering, enrichment, or anomaly detection before storing the processed data. By using Lambda, companies eliminate the need to manage EC2 instances or other compute infrastructure, reducing both operational complexity and costs.

DynamoDB serves as a durable, highly available, and fully managed NoSQL database for storing the processed IoT data. It provides millisecond latency, automatic scaling, and a flexible schema suitable for high-throughput IoT telemetry. DynamoDB Streams can trigger downstream workflows, enabling real-time analytics or additional processing pipelines. This ensures that data is immediately available for dashboards, machine learning applications, or alerting systems.

Other options are less suitable. Using SQS with EC2 consumers introduces operational overhead and latency since EC2 instances must be provisioned, monitored, and scaled manually. SNS with S3 triggers cannot guarantee message ordering and is not optimized for high-throughput IoT streams. RDS with batch ingestion introduces delays and does not scale efficiently to handle millions of messages per day.

Security and monitoring are integral to this architecture. IAM roles control access to IoT Core, Lambda, and DynamoDB. TLS ensures data is encrypted in transit, and KMS encryption protects data at rest. CloudWatch provides metrics for Lambda execution, IoT message delivery, and DynamoDB throughput, while CloudTrail audits all management actions for compliance.

This architecture delivers a serverless, scalable, durable, and cost-efficient solution for processing IoT events in real time. It minimizes operational overhead, supports high availability, and ensures near real-time analytics and monitoring, making it an ideal design for SAP-C02 exam scenarios.

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

Question 83:

A company needs to reduce read latency for a high-traffic DynamoDB table with millions of requests per second. Which solution is most appropriate?

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 that reduces read latency from milliseconds to microseconds. It is designed to support high-throughput applications with low-latency requirements. DAX operates as a write-through cache, ensuring that updates to DynamoDB are automatically reflected in the cache, maintaining consistency.

Option B, ElastiCache Redis, requires application-level integration with DynamoDB, adding operational complexity. Option C, S3 Transfer Acceleration, only improves object transfer speeds for S3, not database queries. Option D, RDS Read Replicas, are for relational databases and cannot accelerate DynamoDB queries.

DAX clusters provide automatic scaling, high availability, and failover capabilities. CloudWatch monitors cache hit ratios, latency, and node health. IAM roles and KMS encryption ensure secure access, while TLS encrypts data in transit. By offloading read operations from DynamoDB, DAX improves performance, reduces throttling, and supports read-heavy workloads at scale.

This architecture follows AWS best practices for performance efficiency, reliability, and operational simplicity. It ensures that high-traffic applications maintain predictable low-latency performance without overloading the database. For SAP-C02 scenarios, it demonstrates how to optimize DynamoDB for globally distributed, high-throughput applications using caching.

Question 84:

A company wants to implement exactly-once processing semantics for high-volume financial transactions. 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 Data Streams provides durable, ordered delivery at the shard level, ensuring that events are processed sequentially. Lambda consumers can checkpoint records, ensuring exactly-once processing semantics, which is critical for financial transactions and other sensitive workloads. Data is replicated across multiple Availability Zones to ensure durability.

Option B, SQS standard queues, provides at-least-once delivery and can result in duplicate processing. FIFO queues enforce ordering but have limited throughput. Option C, SNS, does not guarantee ordering or exactly-once processing. Option D, DynamoDB Streams, only captures table changes and is limited to DynamoDB workloads.

CloudWatch metrics provide monitoring of processing lag, throughput, and Lambda performance. KMS encrypts data at rest, TLS encrypts data in transit, and IAM roles control access to streams. Extended data retention allows replay of events if downstream systems fail.

This architecture is highly available, scalable, and durable, supporting real-time, exactly-once processing. For SAP-C02 scenarios, it demonstrates best practices for serverless, event-driven applications that require transactional integrity and reliability.

Question 85:

A company wants to orchestrate a serverless workflow with multiple Lambda functions, conditional branching, and retries. 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 provides a fully managed serverless orchestration solution using state machines. It supports sequential, parallel, and conditional execution, built-in error handling, retries, and timeouts. Step Functions integrates with Lambda, ECS, SNS, and other AWS services, enabling complex serverless workflows without manual orchestration.

Option B, SWF, is a legacy workflow service requiring worker management. Option C, Batch, is for batch workloads, not orchestration. Option D, SQS, is a messaging service, not a workflow engine.

Step Functions offers operational visibility through execution history, CloudWatch metrics, and X-Ray tracing. Standard workflows are durable and suitable for long-running tasks, while express workflows support high-throughput, short-duration tasks. IAM and KMS enforce security and compliance.

This architecture reduces operational overhead, improves reliability, and ensures observability, making it a best-practice approach for complex serverless workflows in SAP-C02 scenarios.

When a company needs to orchestrate a serverless workflow that involves multiple AWS Lambda functions, conditional branching, and retries, selecting the right orchestration service is critical to ensure reliability, scalability, and operational simplicity. Serverless workflows often involve complex business logic, dependencies, and error handling, which can be challenging to implement manually without a managed orchestration service.

AWS Step Functions is the most suitable service for orchestrating such serverless workflows. Step Functions allows developers to define workflows as state machines, where each step represents a task, such as invoking a Lambda function, running an ECS task, or publishing a message to SNS. Workflows can include sequential, parallel, and conditional execution paths, making it possible to model complex logic with minimal code. Built-in error handling, retries, and timeouts ensure that tasks can recover gracefully from failures without requiring additional operational intervention. Step Functions also provides integration with a wide range of AWS services, enabling seamless orchestration across the AWS ecosystem.

Alternative solutions are less appropriate for serverless workflow orchestration. Amazon Simple Workflow Service (SWF) is a legacy orchestration service that requires managing worker nodes, making it operationally intensive and less suitable for modern serverless applications. AWS Batch is designed for large-scale batch processing workloads rather than real-time orchestration of tasks, so it does not support the fine-grained conditional logic or event-driven execution required in serverless workflows. Amazon Simple Queue Service (SQS) is a fully managed messaging service that enables decoupled communication between components, but it does not provide orchestration, retries, or workflow logic.

Step Functions offers additional operational benefits. Standard workflows are durable and maintain a complete execution history, making them suitable for long-running tasks or audit requirements. Express workflows are optimized for high-throughput, short-duration workflows, supporting scenarios that require low-latency execution. Security and compliance are managed through IAM roles for access control and KMS for encryption of sensitive workflow data. CloudWatch metrics, execution history, and AWS X-Ray tracing provide observability, allowing developers to monitor performance, debug issues, and optimize workflows efficiently.

This architecture reduces operational overhead, improves reliability, and simplifies the implementation of complex serverless workflows, making it the best-practice solution for orchestration in SAP-C02 scenarios.

The correct answer is A) AWS Step Functions.

Question 86:

A company wants to implement a global caching solution for a multi-region web application to improve performance for static and dynamic content. Which solution is best?

Answer:

A) CloudFront with regional edge caches
B) ElastiCache Redis
C) S3 Transfer Acceleration
D) RDS Read Replicas

Explanation:

The correct answer is A) CloudFront with regional edge caches.

CloudFront caches static and dynamic content at edge locations close to users, reducing latency globally. Regional edge caches store content near major AWS regions, improving performance for dynamic content that changes frequently.

Option B, ElastiCache Redis, is a database cache and cannot serve global web traffic efficiently. Option C, S3 Transfer Acceleration, only improves object upload/download speeds, not dynamic content delivery. Option D, RDS Read Replicas, are database-only solutions and do not accelerate web content.

CloudFront integrates with Lambda@Edge for dynamic content processing. It supports HTTPS, IAM policies, KMS encryption, CloudWatch monitoring, and WAF/Shield integration for security. This architecture improves global performance, reduces origin load, and provides high availability, aligning with SAP-C02 best practices.

Question 87:

A company wants to process a large volume of streaming financial transactions in real-time, detect anomalies, and trigger alerts. Which AWS services combination is most appropriate?

Answer:

A) Amazon Kinesis Data Streams with Lambda and CloudWatch
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 and CloudWatch.

Kinesis Data Streams provides a high-throughput, durable, and ordered data ingestion service, making it suitable for real-time financial transaction processing. Shards in Kinesis allow parallel processing, ensuring scalability. Lambda consumers can process each transaction in real-time, applying business logic to detect anomalies, such as fraudulent transactions, sudden spikes, or unusual patterns.

CloudWatch provides monitoring, metrics, and alerting for both Kinesis stream health and Lambda execution performance. CloudTrail logs administrative and data access actions for auditing, ensuring compliance with financial regulations. KMS encryption secures sensitive financial data at rest, while TLS ensures encryption in transit.

Option B, SQS with EC2 consumers, introduces latency and operational overhead due to manual scaling and instance management. Option C, SNS with S3 triggers, is unsuitable for real-time, ordered, and high-throughput transaction processing. Option D, RDS with batch processing, cannot provide near-real-time anomaly detection due to processing delays.

This architecture provides scalability, fault tolerance, and low latency while maintaining operational simplicity. By leveraging managed services like Kinesis and Lambda, the organization can focus on business logic rather than real infrastructure management. In SAP-C02 scenarios, this illustrates event-driven, serverless -time processing, fault tolerance, durability, and monitoring best practices.

Question 88:

A company wants to implement a secure, highly available data lake for structured and unstructured data with minimal operational overhead. Which solution is best?

Answer:

A) S3 with Lake Formation, Glue, and Athena
B) EC2 with RDS
C) S3 with manual ETL using Lambda
D) RDS with S3 backup

Explanation:

The correct answer is A) S3 with Lake Formation, Glue, and Athena.

Amazon S3 provides virtually unlimited, durable, and scalable storage for all types of data. Lake Formation simplifies the creation and management of a secure data lake by centralizing access control, data cataloging, and governance policies. AWS Glue automates ETL operations, allowing efficient ingestion, transformation, and preparation of data for analytics. Athena enables serverless querying of S3 data without requiring provisioning of infrastructure.

Option B, EC2 with RDS, cannot scale efficiently for unstructured data and increases operational overhead. Option C, S3 with manual Lambda ETL, requires extensive management of ETL workflows and is prone to operational errors. Option D, RDS with S3 backup, is suitable for structured data only and does not provide serverless analytics capabilities.

This architecture ensures high availability, scalability, and operational simplicity. Security is enforced via IAM policies, Lake Formation fine-grained access control, KMS encryption, and HTTPS/TLS in transit. CloudWatch monitors Glue jobs, Athena query performance, and S3 storage metrics. CloudTrail provides auditing for compliance.

For SAP-C02, this demonstrates best practices for building a secure, scalable, serverless data lake that supports analytics on structured and unstructured data with minimal operational overhead, integrating storage, ETL, cataloging, and querying services.

Question 89:

A company wants to deploy a web application with unpredictable traffic patterns while minimizing costs. Which architecture is most suitable?

Answer:

A) AWS Lambda with API Gateway and DynamoDB
B) EC2 Auto Scaling with ALB
C) ECS on EC2
D) S3 static hosting with CloudFront

Explanation:

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

Serverless architecture automatically scales with incoming requests, reducing costs by only charging for actual usage. API Gateway handles request routing, authentication, throttling, and caching. Lambda executes application logic, scaling horizontally with incoming requests, without manual infrastructure management. DynamoDB provides highly available, low-latency storage for dynamic content.

Option B, EC2 Auto Scaling with ALB, can scale but may require overprovisioning to handle sudden spikes, increasing cost and operational complexity. Option C, ECS on EC2, requires managing clusters and capacity. Option D, S3 static hosting, only supports static content and cannot handle dynamic web applications.

Security is maintained using IAM roles, KMS encryption, and HTTPS. CloudWatch monitors Lambda metrics and API Gateway requests. This serverless approach supports high availability, fault tolerance, and predictable performance during unpredictable traffic spikes.

In SAP-C02 scenarios, this demonstrates cost-efficient, scalable, and fault-tolerant serverless architectures suitable for web applications with variable workloads.

When deploying a web application that experiences unpredictable traffic patterns, it is important to design an architecture that can automatically scale with demand while minimizing costs. Traditional infrastructure approaches require provisioning resources in advance, which can lead to overprovisioning or underperformance during traffic spikes. AWS provides several solutions for building scalable web applications, but serverless architectures offer the most cost-effective and operationally simple approach for workloads with variable traffic.

The most suitable architecture is a combination of AWS Lambda, API Gateway, and DynamoDB. In this serverless model, API Gateway serves as the entry point for client requests, handling routing, authentication, throttling, and caching. It scales automatically to accommodate spikes in traffic without requiring manual intervention. AWS Lambda executes the application logic, and because it is serverless, it automatically scales horizontally to process incoming requests. Billing is based on actual usage, rather than pre-provisioned capacity, which significantly reduces cost for applications with unpredictable or spiky traffic. DynamoDB provides low-latency, highly available storage for dynamic content, with the ability to scale throughput automatically according to demand. This combination ensures that the entire application stack can handle sudden surges in traffic without compromising performance or requiring infrastructure management.

Alternative options are less optimal for unpredictable workloads. EC2 Auto Scaling with an Application Load Balancer can scale to meet demand, but it requires planning and provisioning instances ahead of time. Sudden traffic spikes may require temporary overprovisioning to avoid latency or throttling, which increases costs. ECS on EC2 introduces additional operational complexity because clusters and capacity must be managed manually, including patching, scaling, and monitoring. S3 static hosting with CloudFront is ideal for static websites, but it cannot support dynamic web applications that require backend processing and database access.

This serverless architecture also ensures security and observability. IAM roles control access to Lambda functions and DynamoDB tables. KMS provides encryption for sensitive data, and HTTPS encrypts data in transit. CloudWatch collects metrics from Lambda and API Gateway, enabling monitoring of request volume, latency, and error rates. The architecture provides high availability, fault tolerance, and automatic scaling, ensuring predictable performance even under sudden and unpredictable traffic spikes.

In SAP-C02 scenarios, using AWS Lambda with API Gateway and DynamoDB demonstrates a cost-efficient, scalable, and fully managed serverless solution for modern web applications.

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

Question 90:

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

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 replicates data asynchronously across multiple regions with replication lag typically under one second, meeting stringent RPO requirements. Automatic failover ensures applications can resume operations quickly, meeting RTO objectives. Secondary regions can also serve read requests, reducing latency.

Option B, cross-region snapshots, introduce delays for restoration. Option C requires manual replication and failover, increasing the risk of errors. Option D is operationally complex and unreliable for strict RPO/RTO.

Aurora automatically maintains six copies of data across three Availability Zones in each region. Security is enforced using IAM roles, KMS encryption, and TLS. CloudWatch monitors replication lag and instance health, while CloudTrail logs actions for auditing.

This solution ensures high availability, operational simplicity, disaster recovery readiness, and fault tolerance, following SAP-C02 best practices for multi-region relational database architectures.

Question 91:

A company needs to implement a real-time analytics solution for IoT data that is durable, scalable, and low-latency. Which solution is best?

Answer:

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

Explanation:

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

Kinesis Data Streams enables high-throughput, low-latency ingestion of IoT streaming data. Shards allow parallel processing for scalability. Lambda functions process data in real-time, triggering analytics or alerts. Data is replicated across multiple Availability Zones for durability.

Option B, SQS with EC2, introduces latency and operational complexity. Option C, SNS with S3, cannot handle ordered, high-throughput streams. Option D, RDS batch processing, introduces delays unsuitable for real-time analytics.

CloudWatch monitors shard health, processing lag, and Lambda performance. IAM, KMS, and TLS provide security and encryption. This architecture ensures scalability, durability, low latency, and operational simplicity, following SAP-C02 best practices for IoT analytics pipelines.

When a company needs to implement a real-time analytics solution for IoT data, the architecture must provide durability, low latency, scalability, and operational simplicity. IoT devices generate continuous streams of data, often at high volumes, requiring a system that can ingest, process, and analyze this data in near real time. AWS offers multiple services for building such architectures, but the combination of Amazon Kinesis Data Streams with AWS Lambda is the most suitable for these requirements.

Amazon Kinesis Data Streams is a fully managed streaming service designed to handle high-throughput, low-latency ingestion of data. It divides incoming data into shards, which allows parallel processing and horizontal scalability. This design ensures that large volumes of IoT data can be ingested without bottlenecks. Kinesis also replicates data across multiple Availability Zones within a region, providing high durability and fault tolerance. In the event of hardware or AZ failures, the data remains safe and accessible for processing.

AWS Lambda acts as a real-time consumer of the Kinesis stream. Lambda functions automatically scale with the volume of incoming data, allowing immediate processing of IoT events. This enables analytics, transformations, anomaly detection, or triggering alerts in real time. Lambda eliminates the need to manage servers, reducing operational complexity and enabling a truly serverless architecture. The combination of Kinesis and Lambda supports extended data retention, which allows replaying streams if downstream processes fail or if analytics need to be rerun.

Alternative options are less appropriate for real-time IoT analytics. Using Amazon SQS with EC2 instances introduces latency because EC2 instances must poll the queue, and scaling requires manual provisioning and management. SNS with S3 cannot handle ordered, high-throughput streams, making it unsuitable for real-time analytics pipelines. RDS with batch processing introduces significant delays because it is designed for batch-oriented workloads rather than continuous, low-latency streaming data.

Operational monitoring and security are integrated into this architecture. Amazon CloudWatch provides metrics on shard health, Lambda execution, and processing lag. Security is ensured through IAM roles and policies, TLS for data in transit, and KMS for encryption at rest.

Overall, this architecture provides a durable, scalable, low-latency, and serverless solution for real-time IoT analytics, following best practices for efficient and reliable data processing.

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

Question 92:

A company wants to reduce latency for global users accessing dynamic content in a web application. Which AWS architecture 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 routes traffic based on latency, directing users to the closest healthy region. Multi-region ALBs distribute traffic within each region. CloudFront caches static and dynamic content at edge locations, reducing latency and load on origin servers.

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

CloudFront integrates with Lambda@Edge for dynamic content processing. Security is enforced using IAM, KMS, TLS, WAF, and Shield. CloudWatch provides monitoring, metrics, and alarms. This architecture ensures low-latency global access, high availability, and fault tolerance.

Question 93:

A company wants to implement a cost-efficient serverless data processing pipeline for batch log files uploaded to S3. Which solution is most suitable?

Answer:

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

Explanation:

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

S3 event notifications trigger Lambda functions whenever a new log file is uploaded, providing a fully serverless, event-driven architecture that eliminates the need for manual polling or scheduling. Lambda functions can transform, clean, or enrich data, and store it back in S3 in a query-optimized format, such as Parquet or ORC. Athena enables serverless querying of this data directly from S3, eliminating the need for provisioning databases or compute resources.

This approach is highly cost-efficient, as Lambda is billed per execution and Athena is billed per query. It scales automatically to handle bursts of log uploads, ensuring no files are missed or delayed. Durability is inherent due to S3’s 11 9’s of durability and cross-AZ replication.

Option B, EC2 Auto Scaling with custom scripts, requires operational overhead to manage instances, updates, scaling policies, and monitoring. Option C, RDS batch ingestion, introduces delays and additional cost due to provisioned database capacity. Option D, SNS notifications to EC2, still requires server management and scaling logic, increasing operational complexity.

Security is enforced using IAM roles for least-privilege access, KMS encryption for data at rest, and HTTPS/TLS for data in transit. CloudWatch monitors Lambda execution, S3 storage metrics, and Athena query performance. CloudTrail ensures auditing of access and data operations.

This architecture aligns with the AWS Well-Architected Framework by achieving operational excellence, cost optimization, reliability, and performance efficiency. For SAP-C02 scenarios, it demonstrates how to build scalable, serverless, event-driven data processing pipelines that minimize operational overhead while providing fast analytics capabilities.

Question 94:

A company wants to implement global low-latency access to an API for users worldwide while ensuring 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 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, handling request routing, throttling, authentication, and caching. Lambda functions in multiple regions process requests, scaling automatically to meet global demand. Route 53 latency-based routing ensures requests are directed to the nearest healthy region, providing low-latency access for end-users.

Option B, a single-region EC2 API, cannot provide low-latency access for global users and is susceptible to regional failures. Option C, SNS with Lambda in one region, is suitable only for asynchronous event-driven workloads and cannot serve synchronous API requests efficiently. Option D, RDS with read replicas, addresses data replication but does not solve API low-latency global access.

Security is maintained using IAM roles, TLS for encryption in transit, and KMS for sensitive data. CloudWatch provides metrics for API usage, Lambda execution, and error rates. CloudTrail logs all management actions for auditing purposes.

This architecture is fully serverless, ensuring operational simplicity, elasticity, and fault tolerance. It allows for predictable performance, high availability, and seamless global failover. For SAP-C02 scenarios, it demonstrates best practices for serverless global APIs with multi-region fault tolerance and low-latency access, aligning with pillars of reliability, performance efficiency, and cost optimization.

Question 95:

A company wants to reduce operational overhead and improve scalability for a large-scale machine learning inference pipeline. Which solution is most appropriate?

Answer:

A) Amazon SageMaker endpoint with auto-scaling
B) EC2 instances with manually deployed models
C) Lambda functions for heavy ML inference
D) S3 with batch scripts

Explanation:

The correct answer is A) Amazon SageMaker endpoint with auto-scaling.

SageMaker endpoints provide fully managed, scalable, and highly available model hosting. Auto-scaling ensures that endpoint instances scale according to request volume, maintaining low latency for inference while minimizing cost. SageMaker also integrates with monitoring tools, logging, and IAM for secure access.

Option B, EC2 instances with manual model deployment, introduces operational complexity, requires scaling management, and is less fault-tolerant. Option C, Lambda functions, are suitable only for lightweight inference workloads due to memory and execution time limits. Option D, S3 with batch scripts, is inefficient for real-time inference.

CloudWatch monitors endpoint performance, request latency, and instance health. KMS encryption secures model artifacts, and TLS ensures secure data in transit. CloudTrail logs administrative actions for compliance.

This architecture improves operational efficiency, scalability, and reliability. For SAP-C02 exam scenarios, it demonstrates serverless-ready, managed ML inference solutions that align with best practices for cost optimization, operational excellence, and performance efficiency.

Question 96:

A company needs to implement a global, low-latency cache for a multi-region DynamoDB application. Which solution is best?

Answer:

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

Explanation:

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

DAX is a fully managed, in-memory cache for DynamoDB that provides microsecond read latency while maintaining write-through consistency. It supports horizontally scalable clusters and automatically handles failover and replication within regions.

Option B, ElastiCache Redis, requires additional application-level integration and does not provide native DynamoDB integration. Option C, RDS Read Replicas, is relational-only and cannot accelerate NoSQL workloads. Option D, S3 Transfer Acceleration, is irrelevant for database queries.

IAM, KMS, and TLS secure DAX access and communication. CloudWatch metrics monitor cache hit ratio, node health, and latency. This architecture reduces DynamoDB throttling, improves application performance, and enables high-throughput, globally distributed access.

For SAP-C02 scenarios, this demonstrates best practices for globally distributed, high-performance NoSQL workloads, emphasizing caching, operational simplicity, and reliability.

Question 97:

A company wants to build a real-time event-driven pipeline for e-commerce transactions, ensuring durability, scalability, and exactly-once processing. Which AWS architecture is most appropriate?

Answer:

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

Explanation:

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

Kinesis provides durable, ordered data ingestion with replication across multiple Availability Zones. Lambda consumers can checkpoint processing to achieve exactly-once semantics. This is critical for financial transactions and e-commerce event pipelines.

Option B, SQS standard queues, may result in duplicate processing and only guarantees at-least-once delivery. Option C, SNS with S3, is suitable for asynchronous event notifications, not high-throughput, ordered transactional workloads. Option D, DynamoDB Streams, is limited to DynamoDB events and cannot serve diverse transactional data.

CloudWatch metrics monitor shard health, processing lag, and Lambda performance. KMS encrypts data at rest, TLS encrypts data in transit, and CloudTrail logs all actions for auditing. This architecture ensures scalability, low-latency processing, durability, and operational simplicity.

For SAP-C02 scenarios, it illustrates real-time, event-driven architectures with fault tolerance, exact processing semantics, and scalability for mission-critical e-commerce pipelines.

Question 98:

A company wants to deploy a multi-region web application with automatic failover and low-latency access. Which AWS services combination is best?

Answer:

A) Multi-region ALBs, CloudFront, and Route 53 latency-based routing
B) Single-region ALB with CloudFront
C) EC2 in one region with Global Accelerator
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. Route 53 latency-based routing directs users to the nearest healthy region. CloudFront caches content at edge locations, reducing latency globally.

Single-region ALB cannot handle regional outages. Global Accelerator with a single region cannot provide high availability. S3 Transfer Acceleration is only suitable for static content.

Security is enforced with IAM, KMS, TLS, WAF, and Shield. CloudWatch monitors ALB health, CloudFront cache metrics, and Route 53 routing. This architecture provides high availability, fault tolerance, low latency, and operational simplicity, following SAP-C02 best practices for globally distributed web applications.

Question 99:

A company wants to implement a highly available relational database across multiple regions with minimal replication lag. Which solution is best?

Answer:

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

Explanation:

The correct answer is A) Amazon Aurora Global Database.

Aurora Global Database replicates asynchronously across regions with replication lag under one second. Secondary regions can serve read requests while providing disaster recovery and failover capabilities.

Option B, cross-region snapshots, introduce significant delays. Option C requires manual effort and is error-prone. Option D, standby RDS in a single region, cannot provide multi-region fault tolerance.

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

This architecture ensures high availability, low-latency replication, disaster recovery readiness, and operational simplicity, aligning with SAP-C02 best practices for multi-region relational database deployment.

Question 100:

A company wants to build a serverless workflow with multiple Lambda functions, branching, error handling, and retries. 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 orchestrates serverless workflows using state machines. It supports sequential, parallel, and conditional execution, retries, error handling, and timeouts. Integration with Lambda, ECS, and SNS enables fully managed workflows.

Option B, SWF, is legacy and requires worker management. Option C, Batch, is for batch processing only. Option D, SQS, is a messaging service without orchestration capabilities.

CloudWatch monitors execution metrics, errors, and performance. X-Ray provides traceability for debugging. IAM and KMS ensure security and compliance.

This architecture reduces operational overhead, improves reliability, and ensures observability. For SAP-C02 exam scenarios, it demonstrates best practices for serverless workflow orchestration with fault tolerance, conditional execution, and retries.