Amazon AWS Certified Solutions Architect – Associate SAA-C03 Exam Dumps and Practice Test Questions Set 9 Q161-180

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Question 161:

A company wants to deploy a web application that must be highly available, fault-tolerant, and capable of handling sudden traffic spikes globally. Which architecture is most appropriate?

Answer:

A) Multi-region deployment with Route 53 latency-based routing, EC2 Auto Scaling, and RDS Global Databases
B) Single EC2 instance in one region
C) S3 static website
D) Lambda only

Explanation:

Option A is correct. Multi-region deployment provides high availability by hosting application components in two or more AWS regions. Route 53 latency-based routing directs users to the nearest healthy region, improving performance. EC2 Auto Scaling groups adjust instance counts automatically based on traffic demand, ensuring the application can handle sudden spikes efficiently. RDS Global Databases replicate data asynchronously across regions, providing low-latency reads for global users and disaster recovery capabilities.

Single EC2 instances are prone to failure and cannot scale globally, causing poor performance during traffic spikes. S3 static websites are limited to static content and cannot host dynamic applications. Lambda-only solutions are fully serverless but may require significant architectural refactoring for relational database integration.

This multi-region design improves fault tolerance, reliability, and performance. EC2 Auto Scaling integrates with CloudWatch to monitor CPU, memory, and request metrics, scaling resources automatically. Application Load Balancers distribute traffic across healthy instances and perform health checks to ensure service availability. RDS Global Databases support automated backups, cross-region replication, and failover mechanisms to minimize downtime.

Security measures include VPC isolation, least-privilege IAM roles, TLS/HTTPS encryption, and KMS encryption for data at rest. CloudTrail provides detailed logging of API calls for auditing purposes. CloudWatch aggregates monitoring data across regions, allowing operations teams to detect and respond to anomalies promptly.

Organizations benefit from operational simplicity, improved user experience, high availability, disaster recovery readiness, and compliance adherence. This architecture follows AWS Well-Architected Framework principles for operational excellence, security, reliability, performance efficiency, and cost optimization, making it ideal for global-scale web applications that require robust, fault-tolerant infrastructure.

Question 162:

A company wants to ingest and process high-volume streaming data from IoT devices in near real-time and store aggregated results for analytics. Which AWS architecture is recommended?

Answer:

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

Explanation:

Option A is correct. Kinesis Data Streams is designed to handle high-throughput, real-time streaming data by dividing streams into shards that allow parallel processing. Lambda functions are triggered automatically upon new records arriving in the stream. These functions can perform data transformations, filtering, and aggregations, then store processed results in DynamoDB, which offers millisecond read/write latency and automatic scaling.

S3 is suited for batch analytics but cannot handle real-time ingestion. EC2 batch processing introduces latency and requires manual scaling. RDS Multi-AZ offers high availability for relational workloads but is not optimized for continuous real-time streaming.

Kinesis ensures durability, fault tolerance, and replayability, enabling reprocessing in case of errors. Lambda scales automatically, handles failures with dead-letter queues, and integrates with multiple AWS services. DynamoDB supports global tables for multi-region access, encryption at rest with KMS, and IAM-based access controls.

CloudWatch monitors throughput, Lambda execution time, and DynamoDB performance metrics. CloudTrail tracks all API calls for auditing purposes. Step Functions can orchestrate complex workflows with conditional logic, retries, and notifications.

This architecture enables operational monitoring, real-time analytics, anomaly detection, and reporting without manual infrastructure management. It aligns with AWS Well-Architected Framework principles for operational excellence, reliability, performance efficiency, security, and cost optimization. The solution is cost-effective, scalable, fault-tolerant, and suitable for IoT environments where rapid ingestion and processing of massive streaming datasets are required.

Option A is the most suitable architecture for ingesting and processing high-volume streaming data from IoT devices in near real-time while storing aggregated results for analytics. Amazon Kinesis Data Streams provides a scalable platform for real-time data ingestion. Streams are divided into shards, which allow parallel processing of large datasets, ensuring that high-throughput data from numerous IoT devices can be processed efficiently without bottlenecks. Kinesis also ensures durability and fault tolerance by replicating data across multiple Availability Zones, and it supports replaying records, enabling recovery or reprocessing in case of failures or downstream errors.

AWS Lambda integrates seamlessly with Kinesis, automatically triggering functions when new records arrive in the stream. Lambda functions can perform data transformations, filtering, aggregations, or enrichment before storing the results. Being serverless, Lambda automatically scales in response to incoming traffic and charges only for actual execution time, which makes it cost-effective for variable workloads. Dead-letter queues capture failed events for later analysis, ensuring reliable processing without data loss. Lambda also integrates with other AWS services, enabling more complex workflows without managing infrastructure.

Amazon DynamoDB serves as the storage layer for processed and aggregated data, providing low-latency access for applications, dashboards, and analytics tools. DynamoDB automatically scales to accommodate variable workloads and supports features like global tables for multi-region access, encryption at rest with KMS, and fine-grained IAM-based access control. This ensures that processed data is both highly available and secure.

Other options are less suitable for real-time IoT data processing. S3, option B, is ideal for batch analytics but cannot handle continuous streams. EC2 batch processing, option C, requires manual scaling and scheduling and introduces latency. RDS Multi-AZ, option D, ensures high availability for relational workloads but is not designed for high-throughput streaming ingestion and processing.

Monitoring and management are handled using CloudWatch, which tracks stream throughput, Lambda execution metrics, and DynamoDB performance, while CloudTrail logs all API activity for auditing and compliance purposes. AWS Step Functions can orchestrate complex workflows, implement retries, and trigger notifications when certain conditions are met. This architecture provides a cost-effective, highly scalable, fault-tolerant, and secure solution for IoT environments, enabling real-time analytics, anomaly detection, and operational monitoring while reducing operational overhead. It aligns with AWS Well-Architected Framework principles for operational excellence, reliability, performance efficiency, security, and cost optimization.

Question 163:

A company wants to provide temporary access to specific S3 objects for external partners without creating IAM users. Which method is most secure and auditable?

Answer:

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

Explanation:

Option A is correct. Pre-signed URLs allow temporary access to specific S3 objects without creating permanent IAM users. The URL contains a cryptographic signature and an expiration time, restricting access to the defined object and action (GET, PUT, etc.). CloudTrail logs all access events for auditing and compliance purposes.

Public S3 buckets expose data to the internet, creating security and compliance risks. Shared IAM credentials violate the principle of least privilege and complicate auditing. S3 Standard is a storage class and does not provide access control mechanisms.

Pre-signed URLs can be generated dynamically using SDKs, Lambda, or API Gateway. They ensure that permissions are scoped to specific objects and are time-limited. Encryption at rest with KMS and secure HTTPS transmission protect sensitive data. Access logs and monitoring provide insight into usage and potential anomalies.

This approach reduces operational overhead by eliminating the need to manage temporary IAM users while providing secure, auditable, and time-limited access. It ensures compliance with internal governance and regulatory requirements. It aligns with AWS Well-Architected Framework principles for operational excellence, security, reliability, and cost optimization. Organizations gain a secure mechanism for collaboration with external partners while minimizing administrative effort and risk exposure.

Option A is the most secure and auditable method for providing temporary access to specific S3 objects for external partners without creating IAM users. Pre-signed URLs allow organizations to grant time-limited permissions to particular objects in S3, ensuring that access is strictly controlled. Each URL contains a cryptographic signature and an expiration timestamp, restricting the allowed operations, such as GET or PUT, to a defined duration. This approach provides a secure alternative to creating permanent IAM credentials for external collaborators, reducing administrative overhead and minimizing security risks.

CloudTrail logging complements pre-signed URLs by capturing detailed records of all access events. These logs include information about which objects were accessed, when the access occurred, and which principal or service used the pre-signed URL. This enables organizations to maintain an audit trail, support compliance reporting, and monitor for unauthorized or unusual access patterns. By combining pre-signed URLs with CloudTrail, companies can ensure accountability and governance while allowing external partners to work with sensitive data safely.

Other options are less suitable for secure and auditable temporary access. Public S3 buckets, option B, expose objects to the entire internet and are vulnerable to unauthorized access, making them unsuitable for sensitive data. Shared IAM credentials, option C, violate the principle of least privilege and complicate auditing because multiple users share the same credentials, making it difficult to track individual actions. S3 Standard storage, option D, refers only to a storage class and does not provide any mechanisms for access control, temporary permissions, or auditing.

Pre-signed URLs can be generated dynamically using AWS SDKs, Lambda functions, or API Gateway endpoints, allowing for automated workflows that create URLs only when needed. Security is enhanced with encryption at rest using KMS and secure transmission via HTTPS. Access logs and monitoring provide insights into usage patterns and can detect anomalies or potential misuse. This architecture reduces operational complexity, eliminates the need to manage temporary IAM users, and ensures secure, auditable, and time-limited access to S3 objects. By leveraging pre-signed URLs with CloudTrail logging, organizations can enable secure collaboration with external partners while aligning with AWS Well-Architected Framework principles for security, operational excellence, reliability, and cost optimization. This approach provides a robust, scalable, and compliant solution for controlled data sharing.

Question 164:

A company wants to analyze large volumes of historical S3 data without moving it to a data warehouse. Which AWS service is most appropriate?

Answer:

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

Explanation:

Option A is correct. Amazon Athena allows serverless, SQL-based querying of data directly stored in S3, without requiring a separate data warehouse. Athena is cost-effective because users are billed based on the amount of data scanned.

RDS requires migrating data and managing infrastructure, which introduces operational overhead. EC2-based solutions require provisioning servers, installing database engines, and managing scaling. DynamoDB is a NoSQL service, unsuitable for ad-hoc SQL queries on large S3 datasets.

Athena integrates with AWS Glue for automatic schema discovery and metadata cataloging. Partitioning and data compression can optimize queries and reduce scanned data for cost savings. CloudWatch monitors query execution metrics, while CloudTrail logs API calls for auditing. IAM and KMS provide secure access and encryption.

This architecture enables ad-hoc querying, complex transformations, and analytics without infrastructure management. It supports rapid insights into historical datasets and aligns with AWS Well-Architected Framework principles for operational excellence, security, performance efficiency, and cost optimization. Organizations can gain insights, maintain compliance, and operate efficiently while leveraging serverless and scalable analytics on S3 data.

Question 165:

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

Answer:

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

Explanation:

Option A is correct. AWS Database Migration Service (DMS) allows continuous replication from an on-premises Oracle database to Amazon RDS, enabling near-zero downtime migration. RDS Multi-AZ ensures high availability through synchronous replication to a standby instance in a separate Availability Zone, allowing automatic failover in case of failure.

EC2 with self-managed Oracle requires manual replication, failover, and patch management. S3 cannot host relational databases. DynamoDB is NoSQL and incompatible with Oracle workloads.

DMS supports homogeneous migration and validates data to ensure consistency. RDS Multi-AZ automates backups, patching, failover, and replication. CloudWatch monitors instance health, CPU, memory, and replication lag. CloudTrail provides auditing of migration actions. IAM roles, VPC isolation, and KMS encryption secure the migration and data at rest.

This architecture reduces operational complexity, maintains business continuity, ensures high availability, and aligns with AWS Well-Architected principles for reliability, operational excellence, performance efficiency, security, and cost optimization. Organizations can migrate critical workloads safely, maintain uptime, and achieve disaster recovery readiness without manual intervention.

Question 166:

A company wants to implement automated backups and retention policies for EBS volumes while replicating them across regions for disaster recovery. Which solution is most suitable?

Answer:

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

Explanation:

Option A is correct. Amazon Data Lifecycle Manager (DLM) enables automated creation, retention, and deletion of EBS snapshots based on defined policies. Cross-region snapshot copy ensures that backups are available in multiple AWS regions, supporting disaster recovery, compliance, and business continuity.

Manual snapshots are error-prone and require operational effort. EC2 scripts require ongoing maintenance and monitoring. S3 Standard storage provides object storage but does not manage EBS snapshots or automate backup processes.

DLM supports incremental snapshots, which reduce storage costs and optimize performance. Policies can define schedules, retention periods, and cross-region replication, providing robust and reliable disaster recovery planning. CloudWatch monitors snapshot creation, status, and replication success/failure. CloudTrail logs snapshot actions for auditing and compliance purposes. KMS encryption ensures that snapshots are secure at rest, and IAM enforces access control policies.

This architecture reduces operational overhead, ensures automated disaster recovery, and provides high availability. Organizations can quickly restore EBS volumes in another region in case of failure, minimizing downtime. It aligns with AWS Well-Architected Framework principles for operational excellence, reliability, security, and cost optimization. Automated backup management improves governance, reduces manual intervention, and ensures business continuity across regions, supporting both operational and regulatory requirements for enterprise workloads.

Question 167:

A company wants to implement a serverless web application that automatically scales and charges only for actual compute usage. Which AWS services should be used?

Answer:

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

Explanation:

Option A is correct. AWS Lambda provides serverless compute that automatically scales with demand and charges only for execution time. API Gateway exposes RESTful endpoints and triggers Lambda functions to handle requests. DynamoDB provides a fully managed, low-latency NoSQL database that automatically scales to accommodate variable traffic.

EC2 instances require manual scaling, patching, and management, which increases operational complexity. Elastic Beanstalk simplifies deployment but is not fully serverless and still relies on underlying EC2 instances. S3 alone cannot run dynamic application logic; it is limited to static content delivery.

The serverless architecture allows the application to scale automatically with traffic, ensuring high availability and fault tolerance. Lambda integrates seamlessly with other AWS services such as S3, Kinesis, and SNS to create event-driven workflows. CloudWatch monitors Lambda executions, API Gateway request metrics, and DynamoDB performance. IAM roles enforce least-privilege access, and KMS provides encryption for sensitive data.

Organizations benefit from reduced operational overhead, cost efficiency, and rapid scaling without manual intervention. The architecture supports rapid development and deployment, allowing focus on business logic rather than infrastructure management. It aligns with AWS Well-Architected Framework principles for operational excellence, security, reliability, performance efficiency, and cost optimization. This solution is ideal for variable workloads, event-driven architectures, and global applications requiring automatic scaling and fault tolerance.

Question 168:

A company wants to implement a real-time analytics pipeline for IoT data that is highly scalable, fault-tolerant, and low-latency. Which architecture is recommended?

Answer:

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

Explanation:

Option A is correct. Amazon Kinesis Data Streams handles high-throughput streaming data by dividing streams into shards for parallel processing. Lambda functions are triggered for each incoming event, enabling real-time transformation, aggregation, and filtering. DynamoDB stores processed results, providing millisecond read/write latency and global scalability.

S3 is ideal for batch analytics but cannot handle continuous streaming data with low latency. EC2 batch processing introduces operational overhead and delays due to manual scaling and resource management. RDS Multi-AZ provides high availability for relational workloads but is not optimized for real-time ingestion of streaming data.

Kinesis ensures durability, fault tolerance, and replayability, allowing reprocessing in case of errors. Lambda provides automatic scaling, integrates with dead-letter queues for failed events, and supports multiple triggers. DynamoDB supports encryption at rest, IAM-based access control, and global tables for multi-region replication.

CloudWatch monitors throughput, Lambda execution, and DynamoDB performance metrics. CloudTrail logs API calls for auditing. Step Functions orchestrate complex workflows, conditional processing, error handling, and notifications.

This architecture minimizes operational overhead, supports fault tolerance, and enables rapid insights from IoT data streams. It aligns with AWS Well-Architected Framework principles for operational excellence, security, reliability, performance efficiency, and cost optimization. Organizations gain a highly scalable, cost-effective, and serverless solution for analyzing real-time streaming data.

Question 169:

A company wants to provide temporary, secure access to S3 objects for external partners without creating IAM users. Which method is recommended?

Answer:

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

Explanation:

Option A is correct. Pre-signed URLs allow temporary access to specific S3 objects without creating IAM users. Each URL has an expiration time and can restrict access to specific operations such as GET or PUT. CloudTrail logging ensures that all access actions are auditable, supporting compliance and security requirements.

Public S3 buckets expose data publicly, creating security risks. Shared IAM credentials violate the principle of least privilege and complicate auditing. S3 Standard is a storage class and does not provide access control or time-limited permissions.

Pre-signed URLs can be generated dynamically through SDKs, Lambda, or API Gateway. They enforce secure, temporary, and object-specific access. Data at rest is encrypted using KMS, and HTTPS ensures encryption in transit. Access logs and monitoring enable auditing and anomaly detection.

This approach reduces operational complexity while ensuring secure collaboration with external partners. Organizations maintain governance and compliance without managing temporary IAM users. It aligns with AWS Well-Architected Framework principles for operational excellence, security, reliability, and cost optimization. Temporary access can be generated programmatically, providing flexibility and security for external stakeholders.

Option A is the most suitable method for providing temporary, secure access to S3 objects for external partners without creating IAM users. Pre-signed URLs allow organizations to grant time-limited access to specific S3 objects, restricting permissions to particular actions such as GET for downloads or PUT for uploads. Each URL contains a cryptographic signature and an expiration timestamp, ensuring that access automatically expires after a defined period. This approach provides a secure alternative to creating permanent IAM users or sharing credentials with external partners, reducing security risks and operational overhead.

CloudTrail logging complements pre-signed URLs by recording all access events, including which objects were accessed, the timestamp of access, and the entity that used the URL. These audit logs enable organizations to maintain a detailed trail of activity for compliance reporting, security monitoring, and incident investigation. By combining pre-signed URLs with CloudTrail, companies can ensure accountability, transparency, and governance while allowing external users to access sensitive data safely.

Other options are less suitable for secure, temporary access. Public S3 buckets, option B, expose data to anyone with a URL, creating significant security and compliance risks. Shared IAM credentials, option C, violate the principle of least privilege and make auditing difficult because multiple users share the same credentials, making it impossible to track individual actions reliably. S3 Standard, option D, is a storage class and does not provide access control mechanisms, time-limited permissions, or auditing capabilities, making it unsuitable for controlled external access.

Pre-signed URLs can be generated dynamically using AWS SDKs, Lambda functions, or API Gateway endpoints, enabling automated workflows that generate URLs on demand. Data at rest can be encrypted using AWS KMS, while HTTPS ensures encryption in transit, protecting sensitive information from unauthorized access. Access logs and monitoring can detect anomalies or potential misuse, further enhancing security. This solution reduces operational complexity by eliminating the need to manage temporary IAM users, while still maintaining secure, auditable, and flexible access for external collaborators. By leveraging pre-signed URLs with CloudTrail logging, organizations align with AWS Well-Architected Framework principles, including operational excellence, security, reliability, and cost optimization, ensuring a scalable and compliant method for sharing S3 objects.

Question 170:

A company wants to reduce latency for a global web application and improve performance for users worldwide. Which architecture is most suitable?

Answer:

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

Explanation:

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

EC2 in a single region increases latency for distant users. S3 alone does not provide global caching or low-latency content delivery. Direct Connect provides private network connectivity but does not improve global performance or reduce latency for end-users.

CloudFront supports caching strategies, TTL configuration, origin failover, and integration with Lambda@Edge for dynamic content manipulation. AWS WAF protects against common web exploits, and AWS Shield provides DDoS mitigation. CloudWatch monitors performance, cache hit ratios, and latency, while CloudTrail logs all API and configuration actions for auditing.

This architecture improves global user experience, reduces latency, and ensures security. Organizations benefit from a scalable, high-performance, and fault-tolerant solution. It aligns with AWS Well-Architected Framework principles for operational excellence, performance efficiency, security, reliability, and cost optimization, providing globally optimized content delivery with minimal operational effort.

Question 171:

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

Answer:

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

Explanation:

Option A is correct. AWS Database Migration Service (DMS) enables continuous replication from an on-premises SQL Server to Amazon RDS, supporting near-zero downtime migration. RDS Multi-AZ provides automatic failover to a standby instance in another Availability Zone, ensuring high availability.

EC2 with self-managed SQL Server requires manual replication, patching, monitoring, and failover management, increasing operational complexity and risk. S3 is object storage, unsuitable for relational databases. DynamoDB is NoSQL and incompatible with SQL Server workloads.

DMS ensures data consistency and allows ongoing replication, minimizing downtime during the migration. RDS Multi-AZ automates backups, patching, replication, and failover to provide seamless availability. CloudWatch monitors replication lag, CPU, memory, and storage metrics. CloudTrail logs API calls for auditing, while IAM roles and KMS encryption provide security for both in-transit and at-rest data.

Organizations benefit from operational simplicity, high availability, and disaster recovery readiness. This approach aligns with AWS Well-Architected Framework principles for operational excellence, reliability, performance efficiency, security, and cost optimization. The solution ensures business continuity during migration, provides high availability, and reduces administrative overhead while maintaining compliance and security best practices for critical workloads.

Option A is the most appropriate architecture for migrating an on-premises SQL Server database to AWS with minimal downtime while ensuring high availability. AWS Database Migration Service (DMS) enables continuous replication from the source SQL Server to Amazon RDS, allowing organizations to maintain near-zero downtime during the migration process. DMS supports homogeneous migrations, ensuring that the source and target database types are compatible, and it ensures data consistency and integrity throughout the migration. This continuous replication allows applications to remain operational during migration, avoiding significant service interruptions.

Amazon RDS Multi-AZ SQL Server complements DMS by providing a highly available and fault-tolerant managed relational database environment. Multi-AZ deployments synchronously replicate data to a standby instance in another Availability Zone, providing automatic failover in the event of hardware or network failures. This ensures minimal downtime and improves resilience for mission-critical applications. RDS handles routine tasks such as automated backups, patching, and maintenance, reducing administrative overhead and allowing organizations to focus on application development rather than infrastructure management.

Other options are less suitable for this use case. EC2 with self-managed SQL Server, option B, requires manual setup of replication, monitoring, patching, and failover, increasing complexity and operational risk. S3, option C, is object storage and cannot host relational databases. DynamoDB, option D, is a NoSQL database and is incompatible with SQL Server workloads, making it unsuitable for migration scenarios that require relational features and ACID compliance.

Monitoring and operational visibility are enhanced using CloudWatch, which tracks replication lag, CPU utilization, memory, and storage metrics. CloudTrail logs all API calls and changes, supporting auditing and compliance requirements. Security is enforced through IAM roles and policies, and KMS encryption protects data both at rest and in transit. By leveraging AWS DMS with RDS Multi-AZ SQL Server, organizations achieve a migration strategy that ensures high availability, fault tolerance, and operational efficiency while reducing administrative overhead. This architecture aligns with AWS Well-Architected Framework principles for operational excellence, reliability, performance efficiency, security, and cost optimization, ensuring business continuity and compliance for critical SQL Server workloads during migration and beyond.

Question 172:

A company wants to implement automated compliance monitoring and remediation for IAM policies across multiple AWS accounts. Which solution is recommended?

Answer:

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

Explanation:

Option A is correct. AWS Config evaluates AWS resources against compliance rules, such as verifying IAM policies for excessive permissions. When integrated with AWS Organizations, these rules can be enforced across multiple accounts centrally. Config rules can trigger automated remediation to fix non-compliant resources, reducing human error and operational overhead.

IAM policies alone cannot monitor compliance or automate remediation. EC2 scripts require manual maintenance, monitoring, and triggering. S3 does not provide IAM compliance capabilities.

Using AWS Config with Organizations provides a centralized view of compliance status, enabling auditing and enforcement at scale. CloudWatch monitors Config rule compliance, and CloudTrail logs all changes and API calls for auditing purposes. Automated remediation ensures least-privilege policies are enforced continuously.

This solution enhances security governance, reduces risk, and ensures continuous compliance across multiple accounts. It aligns with AWS Well-Architected Framework principles for operational excellence, security, reliability, performance efficiency, and cost optimization. Organizations can maintain a consistent security posture, automate governance, and reduce administrative effort across their AWS environment.

Question 173:

A company wants to implement a serverless architecture for a web application that scales automatically and charges only for usage. Which AWS services are best suited?

Answer:

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

Explanation:

Option A is correct. AWS Lambda executes application logic without managing servers, scaling automatically based on traffic. API Gateway exposes RESTful endpoints, triggering Lambda functions to handle requests. DynamoDB provides a fully managed NoSQL database that scales automatically with demand and offers low-latency access to application data.

EC2 instances require manual scaling, patching, and management, increasing operational complexity. Elastic Beanstalk simplifies deployment but still relies on EC2 and is not fully serverless. S3 alone cannot host dynamic application logic.

This serverless architecture supports event-driven workflows and integrates with other services such as S3, SNS, and Kinesis for complex processing. CloudWatch monitors Lambda execution metrics, API Gateway performance, and DynamoDB throughput. IAM roles enforce least-privilege access, and KMS ensures encryption for sensitive data at rest.

Organizations benefit from operational simplicity, cost efficiency, and automatic scaling without manual intervention. This architecture supports rapid application development, aligns with AWS Well-Architected principles for operational excellence, security, reliability, performance efficiency, and cost optimization, and provides a highly scalable, fault-tolerant solution for dynamic workloads.

Question 174:

A company wants to implement a real-time analytics pipeline for IoT data that is highly scalable and fault-tolerant. Which architecture is recommended?

Answer:

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

Explanation:

Option A is correct. Kinesis Data Streams ingests high-volume streaming data in real-time, with shards enabling parallel processing. Lambda functions process events automatically, performing data transformation, aggregation, and filtering. DynamoDB stores results for low-latency access and automatic scaling.

S3 is optimized for batch analytics and cannot provide real-time processing. EC2 batch processing introduces latency and operational overhead. RDS Multi-AZ is high availability for relational workloads but is not optimized for real-time streaming.

Kinesis provides durability and replayability, allowing event reprocessing. Lambda automatically scales to match incoming traffic and integrates with dead-letter queues for error handling. DynamoDB supports encryption, IAM-based access control, and global tables for multi-region replication.

CloudWatch monitors throughput, execution metrics, and latency. CloudTrail provides auditing and compliance logs. Step Functions can orchestrate complex workflows, including retries, error handling, and notifications.

This architecture is highly scalable, cost-effective, fault-tolerant, and fully serverless. It enables organizations to process IoT streaming data efficiently while minimizing operational overhead. It aligns with AWS Well-Architected principles for operational excellence, security, reliability, performance efficiency, and cost optimization, making it ideal for real-time IoT analytics pipelines.

Option A is the recommended architecture for implementing a real-time analytics pipeline for IoT data that is both highly scalable and fault-tolerant. Amazon Kinesis Data Streams provides a robust platform for ingesting high-volume streaming data from IoT devices in real time. Streams are divided into shards, which allow parallel processing and ensure that large amounts of data can be ingested and processed efficiently without bottlenecks. Kinesis also guarantees data durability and supports replayability, which enables reprocessing of events in case of errors or downstream failures, ensuring fault tolerance and reliability.

AWS Lambda integrates seamlessly with Kinesis Data Streams, automatically triggering functions whenever new records arrive in the stream. Lambda functions can perform real-time data transformations, aggregations, filtering, or enrichment before storing the processed results. Being serverless, Lambda automatically scales to handle varying levels of incoming data, and charges are based solely on execution time, which makes this architecture cost-efficient. Lambda functions can also be configured with dead-letter queues to capture failed events for later investigation or reprocessing, enhancing reliability and reducing the risk of data loss.

Amazon DynamoDB serves as the storage layer for processed data, providing low-latency read and write access to support dashboards, analytics applications, or downstream processing. DynamoDB automatically scales to accommodate variable workloads, supports encryption at rest with KMS, and allows fine-grained access control through IAM. Global tables can replicate data across multiple regions, enabling low-latency access for global applications and enhancing disaster recovery capabilities.

Other options are less suitable for real-time IoT analytics. S3, option B, is designed for batch analytics rather than continuous streaming ingestion. EC2 batch processing, option C, introduces latency and requires manual scaling and operational management. RDS Multi-AZ, option D, provides high availability for relational workloads but is not optimized for the high-throughput, low-latency requirements of real-time streaming.

Operational visibility is provided through CloudWatch, which monitors throughput, Lambda execution times, latency, and error metrics, while CloudTrail records all API activity for auditing and compliance purposes. AWS Step Functions can orchestrate more complex workflows, handling retries, error conditions, and notifications. This architecture delivers a highly scalable, fault-tolerant, and cost-effective solution for real-time IoT analytics, minimizing operational overhead while aligning with AWS Well-Architected Framework principles for operational excellence, security, reliability, performance efficiency, and cost optimization. By leveraging Kinesis, Lambda, and DynamoDB, organizations can build an end-to-end, serverless analytics pipeline capable of processing massive streaming datasets in real time.

Question 175:

A company wants to provide secure, temporary access to S3 objects for external partners without creating IAM users. Which approach is most appropriate?

Answer:

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

Explanation:

Option A is correct. Pre-signed URLs grant temporary access to specific S3 objects without requiring IAM users. Each URL has an expiration time and can restrict access to specific operations. CloudTrail logs all access events, ensuring auditing and compliance.

Public S3 buckets expose sensitive data, creating security risks. Shared IAM credentials violate least-privilege principles and complicate auditing. S3 Standard is a storage class and does not control access.

Pre-signed URLs can be generated dynamically via SDKs, Lambda, or API Gateway. They enforce secure, object-specific, and time-limited access. Data at rest is encrypted using KMS, and HTTPS encrypts data in transit. Access logs and CloudWatch monitoring detect anomalies and support compliance requirements.

This solution reduces operational overhead while providing secure, auditable access to external partners. Organizations maintain governance, ensure compliance, and minimize risk. It aligns with AWS Well-Architected Framework principles for operational excellence, security, reliability, performance efficiency, and cost optimization, offering a scalable, secure mechanism for external collaboration.

Question 176:

A company wants to reduce latency for a global web application while securing it against web attacks. Which architecture is most suitable?

Answer:

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

Explanation:

Option A is correct. CloudFront caches content at edge locations worldwide, ensuring that users access content from the nearest location, reducing latency and improving user experience. The origin can be S3 for static content or EC2 for dynamic applications. AWS WAF provides protection against common web exploits such as SQL injection and cross-site scripting (XSS), while AWS Shield defends against DDoS attacks.

A single EC2 instance is a single point of failure, lacks global performance optimization, and cannot handle high traffic efficiently. S3 static websites are limited to static content and do not support dynamic processing. Direct Connect improves private connectivity but does not optimize content delivery or security globally.

CloudFront supports caching strategies, TTL configuration, origin failover, and Lambda@Edge for dynamic content modifications. CloudWatch monitors performance metrics, latency, and cache hit ratios, while CloudTrail logs configuration and access events for auditing. IAM roles enforce least-privilege access, and KMS provides encryption for sensitive data.

This architecture improves performance, security, fault tolerance, and operational efficiency. It aligns with AWS Well-Architected principles for operational excellence, performance efficiency, security, reliability, and cost optimization. Organizations benefit from scalable, secure, and globally optimized content delivery, reducing latency for users and maintaining protection against attacks without complex infrastructure management.

Question 177:

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

Answer:

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

Explanation:

Option A is correct. AWS Database Migration Service (DMS) allows continuous replication from an on-premises Oracle database to Amazon RDS, enabling near-zero downtime migration. Amazon RDS Multi-AZ deployment ensures high availability with synchronous replication to a standby instance in another Availability Zone, providing automatic failover in case of failure.

EC2 with self-managed Oracle increases operational complexity and requires manual replication, patching, and failover. S3 cannot host relational databases. DynamoDB is NoSQL and incompatible with Oracle workloads.

DMS validates data integrity during migration and supports homogeneous migration scenarios. RDS Multi-AZ automates backups, patching, replication, and failover processes. CloudWatch monitors replication lag, CPU, memory, and storage usage. CloudTrail tracks API calls for auditing. IAM roles and VPC isolation secure the database environment, while KMS encrypts data at rest.

This architecture ensures high availability, fault tolerance, and minimal downtime during migration. It reduces operational overhead, maintains business continuity, and aligns with AWS Well-Architected Framework principles for reliability, operational excellence, performance efficiency, security, and cost optimization. Organizations can migrate mission-critical workloads safely while minimizing disruption to users.

Question 178:

A company wants to implement automated backups and retention policies for EBS volumes while replicating them across regions for disaster recovery. Which solution is most suitable?

Answer:

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

Explanation:

Option A is correct. Amazon Data Lifecycle Manager (DLM) automates the creation, retention, and deletion of EBS snapshots based on defined policies. Cross-region replication ensures backups are available in another AWS region, supporting disaster recovery, compliance, and business continuity.

Manual snapshots require human intervention, increasing operational overhead and the risk of missed backups. EC2 scripts must be manually maintained and scheduled, increasing complexity. S3 is object storage and does not manage EBS snapshots.

DLM supports incremental snapshots, reducing storage costs and improving efficiency. Policies can define schedules, retention periods, and cross-region replication rules. CloudWatch monitors snapshot creation and status, while CloudTrail logs actions for auditing and compliance. KMS encryption secures snapshots at rest, and IAM policies enforce proper access control.

This architecture ensures automated, reliable, and cost-effective backup management while reducing operational complexity. Organizations can recover EBS volumes quickly in another region during failures, maintaining business continuity. It aligns with AWS Well-Architected Framework principles for operational excellence, reliability, security, and cost optimization, offering a scalable and fault-tolerant solution for EBS backup management.

Question 179:

A company wants to implement a serverless web application that automatically scales and charges only for actual usage. Which AWS services are most suitable?

Answer:

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

Explanation:

Option A is correct. AWS Lambda provides serverless compute that executes application logic without provisioning or managing servers, scaling automatically with demand, and charging only for actual execution time. API Gateway exposes RESTful endpoints to clients and triggers Lambda functions to handle incoming requests. DynamoDB offers a fully managed, scalable NoSQL database with low-latency access, automatically adjusting capacity to handle variable workloads.

EC2 instances require manual management, patching, and scaling, increasing operational overhead. Elastic Beanstalk simplifies deployment but relies on underlying EC2 instances, making it partially managed rather than fully serverless. S3 cannot host dynamic content and is limited to static object storage.

This architecture supports event-driven workflows and integrates seamlessly with other AWS services such as S3, SNS, and Kinesis. CloudWatch monitors Lambda executions, API Gateway requests, and DynamoDB throughput, while IAM enforces least-privilege access. KMS ensures encryption for sensitive data.

Organizations benefit from reduced operational complexity, cost efficiency, and automatic scaling without manual intervention. This solution supports rapid development and deployment, aligns with AWS Well-Architected Framework principles for operational excellence, reliability, security, performance efficiency, and cost optimization, and provides a highly scalable, fault-tolerant serverless solution suitable for variable workloads.

Question 180:

A company wants to implement a real-time analytics pipeline for IoT data that is highly scalable, fault-tolerant, and low-latency. Which architecture is recommended?

Answer:

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

Explanation:

Option A is correct. Amazon Kinesis Data Streams ingests high-volume IoT data in real-time, partitioned into shards for parallel processing. Lambda functions automatically process each incoming event, performing transformations, aggregations, and filtering before storing results in DynamoDB for fast, low-latency retrieval.

S3 is optimized for batch processing and cannot provide near real-time processing. EC2 batch processing introduces operational overhead and latency. RDS Multi-AZ ensures relational database high availability but is not optimized for streaming IoT data.

Kinesis ensures durability, fault tolerance, and event replay capabilities, allowing reprocessing in case of failures. Lambda scales automatically to handle incoming traffic and integrates with dead-letter queues for error handling. DynamoDB supports global tables, encryption at rest with KMS, and fine-grained IAM access control.

CloudWatch monitors throughput, latency, and execution metrics. CloudTrail provides auditing of API calls. Step Functions can orchestrate complex workflows with conditional logic, retries, and notifications.

This architecture minimizes operational overhead, provides real-time insights, and ensures high availability and fault tolerance. It aligns with AWS Well-Architected Framework principles for operational excellence, reliability, performance efficiency, security, and cost optimization. Organizations can deploy a scalable, cost-effective, and serverless solution for IoT analytics, supporting rapid insights and operational intelligence without manual infrastructure management.