Amazon AWS Certified Solutions Architect – Associate SAA-C03 Exam Dumps and Practice Test Questions Set 3 Q41-60

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Question 41:

A company wants to build a highly available web application with minimal operational overhead. The application should automatically scale based on demand, and the database should provide high availability with automatic failover. Which architecture is most suitable?

Answer:

A) EC2 instances across multiple AZs with Auto Scaling, Application Load Balancer, and RDS Multi-AZ
B) Single EC2 instance with EBS volume
C) Lambda only
D) S3 hosting

Explanation:

Option A is correct. Building a highly available, scalable web application requires distributing compute resources across multiple Availability Zones (AZs). Deploying EC2 instances across multiple AZs ensures redundancy; if one AZ fails, the remaining AZs continue to serve traffic, maintaining application availability. Auto Scaling automatically adjusts the number of instances based on traffic patterns, ensuring cost efficiency and the ability to handle sudden spikes. An Application Load Balancer (ALB) distributes incoming traffic to healthy instances, monitors their status, and supports features like sticky sessions, path-based routing, and SSL termination, enhancing operational flexibility.

For the database layer, RDS Multi-AZ provides synchronous replication to a standby instance in another AZ. In case the primary database fails, automatic failover occurs without manual intervention, reducing downtime and maintaining service continuity. Multi-AZ deployments also support backup automation, patching, and enhanced monitoring through CloudWatch metrics, simplifying operational management. This design follows AWS Well-Architected Framework principles, including operational excellence, reliability, performance efficiency, and cost optimization.

Option B, using a single EC2 instance, introduces a single point of failure, making it unsuitable for high availability. Option C, Lambda-only architectures, work well for stateless or serverless applications but may not support multi-tier applications requiring persistent database connections or complex logic. Option D, S3 hosting, is ideal for static content but cannot host dynamic application components or provide database functionality.

Implementing this architecture also supports security best practices. Each EC2 instance can reside in a private subnet behind the ALB, with strict security group rules and Network ACLs. The RDS instance can also reside in private subnets with encrypted storage and IAM authentication. CloudTrail and Config can monitor API activity and compliance, while CloudWatch alarms can notify administrators of performance or operational issues.

This approach ensures minimal operational overhead while maximizing reliability, scalability, and performance. Auto Scaling combined with RDS Multi-AZ reduces manual intervention, operational complexity, and the risk of human error. Additionally, integrating this architecture with Amazon CloudFront, if global content distribution is needed, further improves latency and enhances the user experience worldwide. By leveraging managed services like ALB and RDS, the company can focus on application development rather than infrastructure management. Overall, this architecture exemplifies an enterprise-ready AWS deployment that is highly available, fault-tolerant, cost-efficient, and aligned with AWS best practices for cloud-native applications.

Question 42:

A company wants to process a high volume of streaming data from IoT devices in real-time and trigger notifications if certain thresholds are exceeded. Which AWS architecture should a Solutions Architect recommend?

Answer:

A) Amazon Kinesis Data Streams with Lambda and CloudWatch
B) Amazon S3 with Athena
C) Amazon RDS Multi-AZ
D) EC2 instances with cron jobs

Explanation:

Option A is correct. Kinesis Data Streams captures high-throughput streaming data from IoT devices, providing near real-time ingestion. Data can be partitioned across shards to ensure scalable processing. Lambda functions can process the incoming stream in real-time, executing custom logic, transformations, and triggering alerts when predefined thresholds are exceeded. CloudWatch monitors metrics and can trigger alarms based on predefined conditions, allowing automated notifications via SNS or other alerting mechanisms.

Option B, S3 with Athena, supports batch analysis but cannot process streaming data in real-time. Option C, RDS Multi-AZ, ensures database availability but does not natively support real-time stream processing. Option D, EC2 instances with cron jobs, is operationally heavy, introduces latency, and cannot scale automatically to handle variable streaming rates.

The Kinesis-Lambda-CloudWatch architecture provides several advantages. Lambda automatically scales with the volume of incoming data, eliminating the need to provision servers. Kinesis ensures data durability by storing records for a configurable retention period, allowing retries or reprocessing if necessary. Integration with CloudWatch provides observability, enabling operators to monitor throughput, processing lag, and function errors. Alerts can be routed through Amazon SNS to notify teams immediately if critical thresholds are breached.

For enterprise IoT applications, this architecture supports reliable, low-latency processing, operational simplicity, and high scalability. Security can be implemented using IAM roles for fine-grained access control, and encryption at rest and in transit ensures compliance with regulations. By combining these services, organizations can build an event-driven system capable of analyzing massive streams of telemetry data in near real-time, triggering automated responses, and enabling timely decision-making for operational or business processes. This solution is cost-efficient, operationally resilient, and aligns with the AWS Well-Architected Framework for reliability and performance efficiency.

The correct architecture for processing a high volume of streaming data from IoT devices in real-time and triggering notifications when thresholds are exceeded is Amazon Kinesis Data Streams with Lambda and CloudWatch. Kinesis Data Streams provides a fully managed service capable of ingesting large volumes of streaming data with low latency. It allows data to be partitioned across multiple shards, ensuring that scaling can be achieved according to the incoming data rate. Lambda functions can then consume the streaming data from Kinesis in near real-time. These functions can apply custom logic to analyze data, detect threshold breaches, and trigger alerts instantly. CloudWatch adds an observability layer, monitoring metrics such as data throughput, processing lag, and Lambda execution errors. Combined with Amazon Simple Notification Service, it enables automated notifications to teams when critical conditions are detected.

Option B, Amazon S3 with Athena, is suitable for batch analysis of stored data but cannot provide real-time processing capabilities. Athena allows querying of large datasets directly from S3, which is ideal for historical or ad hoc analytics, but it cannot react immediately to incoming data streams or trigger instant notifications based on dynamic thresholds. This makes it unsuitable for IoT scenarios that require immediate responses.

Option C, Amazon RDS Multi-AZ, is primarily designed for database availability and failover protection. While it ensures that relational data is replicated and highly available, RDS does not natively support real-time stream ingestion or automated processing of high-volume telemetry data. Implementing real-time analytics on RDS would require additional layers of architecture, increasing complexity and latency.

Option D, EC2 instances with cron jobs, introduces operational overhead because servers must be provisioned, managed, and scaled manually. Cron jobs operate on fixed schedules, which introduces latency and cannot respond dynamically to unpredictable incoming data streams. This approach is inefficient for high-throughput IoT data and can be costly when scaling to meet demand.

In conclusion, the Kinesis-Lambda-CloudWatch architecture offers a fully serverless, scalable, and reliable solution for real-time IoT data processing. It ensures low-latency analysis, automatic scaling, operational simplicity, and secure handling of streaming data. This approach aligns with the AWS Well-Architected Framework, providing a cost-efficient, resilient, and event-driven solution capable of delivering timely notifications and supporting operational decision-making.

Question 43:

A company wants to implement a serverless architecture to process files uploaded to S3, transform the content, and store results in DynamoDB. Which services should be used together?

Answer:

A) Amazon S3, Lambda, and DynamoDB
B) EC2 instances, RDS, and S3
C) Elastic Beanstalk and RDS
D) S3 only

Explanation:

Option A is correct. When a file is uploaded to S3, an event can trigger a Lambda function to process and transform the data. The processed data can then be written to DynamoDB for low-latency storage and retrieval. This architecture eliminates server management, automatically scales with incoming requests, and only incurs costs when Lambda executes.

EC2 instances with RDS introduce operational overhead, require provisioning, scaling, and patching, and are less cost-efficient for variable workloads. Elastic Beanstalk is suited for deploying applications on managed EC2 infrastructure but does not provide a fully serverless approach. S3 alone stores files but cannot process or transform them.

Using Lambda ensures event-driven scalability and reliability. Lambda integrates with CloudWatch for logging and monitoring, providing observability and error tracking. DynamoDB provides high availability, low-latency access, and seamless scaling without manual intervention. Security is enhanced by using IAM roles for Lambda to access only the required S3 buckets and DynamoDB tables.

This architecture is ideal for ETL pipelines, media processing, and data transformation tasks. The event-driven, serverless design reduces operational complexity, improves scalability, and ensures cost efficiency by paying only for actual usage. Integration with other AWS services such as Step Functions can orchestrate complex workflows, error handling, and retries. This design aligns with AWS best practices for serverless applications, emphasizing automation, scalability, operational simplicity, and cost optimization.

Question 44:

A company wants to distribute dynamic web content to a global audience with low latency, high availability, and secure HTTPS access. Which AWS service combination is recommended?

Answer:

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

Explanation:

Option A is correct. CloudFront is a content delivery network that caches content at edge locations globally, reducing latency. HTTPS ensures data security in transit. The origin can be S3 for static content or EC2 for dynamic content. S3 alone does not provide edge caching or global distribution. EC2 in a single region introduces latency for distant users. Direct Connect provides private connectivity but is not a CDN.

CloudFront supports cache invalidation, geolocation routing, and integration with AWS WAF for additional security. For dynamic content, CloudFront can forward requests to the origin, maintaining freshness while benefiting from low-latency edge caching. Integration with Lambda@Edge allows executing custom logic close to end users, further improving performance and enabling personalization.

This architecture enhances availability by leveraging multiple edge locations and failover mechanisms. Security is enforced via HTTPS, TLS, and integration with IAM or CloudFront signed URLs. CloudFront also reduces the load on origin servers, optimizing costs and improving response times. This design pattern is widely used for e-commerce, media streaming, and global applications requiring secure, high-performance content delivery.

The recommended AWS service combination for distributing dynamic web content to a global audience with low latency, high availability, and secure HTTPS access is Amazon CloudFront with an S3 or EC2 origin and HTTPS. CloudFront is a content delivery network that caches content at edge locations around the world, allowing users to access data from a location geographically close to them, which significantly reduces latency. By serving content through HTTPS, CloudFront ensures that data is encrypted in transit, providing secure access for end users. For dynamic content, EC2 instances can act as the origin, while S3 can be used for static content such as images, scripts, or style sheets, enabling a flexible and scalable architecture.

Option B, using Amazon S3 alone, is not sufficient for global distribution of dynamic content. While S3 provides durable storage and static website hosting capabilities, it does not include a content delivery network or edge caching, which results in higher latency for users located far from the region where the bucket resides. S3 also lacks the ability to execute custom logic at edge locations, which can be essential for personalization or real-time request processing.

Option C, running EC2 instances in a single region, introduces significant latency for global users because all requests must travel to the region hosting the instances. This approach also limits availability, as an outage in that single region would render the application inaccessible. Scaling globally would require deploying additional EC2 instances in multiple regions, adding operational complexity and cost.

Option D, AWS Direct Connect, provides dedicated private network connectivity between on-premises environments and AWS, improving bandwidth and reducing network variability. However, Direct Connect is not designed for content delivery to a global audience and does not offer caching or edge distribution.

CloudFront also supports advanced features such as cache invalidation, geolocation routing, and integration with AWS WAF to protect against web attacks. Lambda@Edge allows execution of code closer to end users, enabling dynamic personalization and improved performance. By leveraging multiple edge locations, CloudFront enhances availability and reliability, reduces the load on origin servers, and optimizes costs. This architecture is commonly used for e-commerce platforms, media streaming services, and global applications that require secure, fast, and highly available content delivery.

Question 45:

A company wants to implement a highly available, fault-tolerant relational database for its critical production application. The solution must automatically failover without manual intervention. Which architecture is most suitable?

Answer:
A) Amazon RDS Multi-AZ
B) Single RDS instance
C) Amazon DynamoDB
D) EC2 with MySQL

Explanation:

Option A is correct. Amazon RDS Multi-AZ provides synchronous replication of the primary database to a standby instance in a different Availability Zone (AZ). If the primary instance fails due to hardware, network, or AZ outage, RDS automatically fails over to the standby instance, minimizing downtime and ensuring continuous database availability. Multi-AZ deployments integrate with automated backups, patch management, and monitoring via CloudWatch, reducing operational burden.

A single RDS instance lacks automatic failover and creates a single point of failure. DynamoDB is NoSQL and cannot support relational queries and transactional workloads in the same manner. EC2 with MySQL requires manual replication, failover scripts, monitoring, and patching, increasing operational complexity and the risk of human error.

RDS Multi-AZ deployments also support encrypted storage with KMS, ensuring compliance with data security requirements. High availability is critical for production applications that need to maintain service continuity and meet strict Service Level Agreements (SLAs). In addition to automatic failover, Multi-AZ RDS provides enhanced durability by replicating logs synchronously, ensuring no data loss during failover. This architecture aligns with AWS Well-Architected Framework principles of reliability and operational excellence by automating recovery processes and reducing manual intervention.

Organizations can also combine Multi-AZ with read replicas to offload read-heavy workloads while maintaining high availability for writes. CloudFormation templates can automate deployment of Multi-AZ RDS instances, ensuring consistent, repeatable, and secure infrastructure provisioning. This approach minimizes downtime, supports disaster recovery, and provides a cost-effective managed solution, eliminating the need for self-managed clustering or manual replication strategies. By leveraging Multi-AZ RDS, organizations can focus on application development rather than database operations, improving agility, reliability, and performance efficiency for critical workloads.

Question 46:

A company wants to run a serverless web application that triggers notifications when specific events occur in an S3 bucket. Which AWS services should be used?

Answer:

A) Amazon S3, Lambda, and Amazon SNS
B) EC2, S3, and CloudWatch
C) Elastic Beanstalk and RDS
D) S3 only

Explanation:

Option A is correct. Amazon S3 can generate events when objects are created, deleted, or modified. These events can trigger AWS Lambda functions, which can process the events and send notifications using Amazon SNS. This architecture is fully serverless, scales automatically, and charges only for usage, providing cost efficiency.

EC2 requires managing instances, operating systems, scaling, and monitoring, making it less efficient for event-driven workloads. Elastic Beanstalk automates application deployment but still requires underlying EC2 instances. S3 alone stores objects but cannot generate notifications or perform processing.

Lambda enables developers to focus on business logic rather than infrastructure management. Combined with SNS, notifications can be sent to multiple subscribers via email, SMS, or HTTP endpoints. The architecture supports real-time event processing and automation, improving responsiveness to operational events. Security is enforced through IAM roles assigned to Lambda, ensuring least-privilege access to S3 and SNS resources. Logging and monitoring can be achieved using CloudWatch, capturing invocation metrics, failures, and latency, enabling operational visibility.

This event-driven architecture is ideal for scenarios like automated image processing, document validation, and alerting systems. By decoupling storage, compute, and messaging components, the system improves fault tolerance, scalability, and maintainability. Organizations benefit from reduced operational overhead, minimal costs for idle resources, and the ability to respond to events in near real-time. This solution demonstrates best practices for building serverless applications on AWS, leveraging managed services to maximize efficiency, reliability, and security.

Question 47:

A company wants to analyze log files stored in S3 using SQL queries without managing any servers. Which AWS service is appropriate?

Answer:

A) Amazon Athena
B) Amazon Redshift
C) Amazon RDS
D) Amazon DynamoDB

Explanation:

Option A is correct. Amazon Athena is a serverless interactive query service that allows running standard SQL queries on data stored in S3 without provisioning or managing servers. Athena supports multiple formats such as CSV, JSON, Parquet, and ORC, enabling flexible analysis of structured and semi-structured data.

Redshift is a managed data warehouse but requires cluster management. RDS provides relational storage but does not allow querying raw S3 files directly. DynamoDB is a NoSQL database and does not support SQL queries.

Athena integrates with AWS Glue Data Catalog to manage schema definitions and maintain a consistent view of data across multiple datasets. This allows analysts to perform ad-hoc queries, create dashboards, and generate insights without ETL or infrastructure management. Queries in Athena are charged per data scanned, which encourages efficient data storage and partitioning strategies.

Athena is ideal for log analysis, auditing, security compliance, and business intelligence. Logs can be partitioned by date or other attributes to minimize query costs and improve performance. CloudWatch can trigger Lambda functions to move or preprocess logs, enabling automated workflows. Athena supports integration with Amazon QuickSight for visual analytics, making it a powerful tool for real-time operational insights.

This architecture eliminates server management, scales automatically, and supports flexible querying and reporting. Security best practices include encrypting S3 data using SSE-KMS, controlling access through IAM policies, and monitoring queries via CloudTrail for compliance. By leveraging Athena, organizations gain a cost-effective, scalable, and highly available solution for analyzing large datasets in S3, improving operational efficiency and decision-making capabilities.

Question 48:

A company wants to store infrequently accessed data cost-effectively while retaining the ability to retrieve it within minutes. Which S3 storage class is most appropriate?

Answer:

A) S3 Standard-Infrequent Access (S3 Standard-IA)
B) S3 Standard
C) S3 Glacier Deep Archive
D) Amazon EBS

Explanation:

Option A is correct. S3 Standard-IA is optimized for infrequently accessed data but provides low-latency access when needed. It is cost-effective for long-term storage while still offering immediate retrieval within milliseconds to seconds.

S3 Standard is designed for frequently accessed data, making it more expensive for infrequent workloads. Glacier Deep Archive is extremely cost-efficient but retrieval can take up to 12 hours, which is unsuitable for applications needing quick access. EBS provides block storage for EC2 instances but is not ideal for infrequently accessed object storage.

S3 Standard-IA integrates seamlessly with lifecycle policies, allowing organizations to transition objects from Standard to Standard-IA automatically as access patterns change, optimizing costs without manual intervention. Versioning can be enabled to protect against accidental deletion, while encryption using SSE-KMS or SSE-S3 ensures compliance and security.

The architecture also supports analytics and monitoring through CloudWatch and S3 Storage Lens, enabling insights into storage usage and access patterns. By using Standard-IA, companies can achieve a balance between cost, performance, and availability, making it ideal for backups, disaster recovery archives, and infrequently accessed business data. This approach aligns with AWS Well-Architected Framework principles of cost optimization, operational efficiency, and performance efficiency.

Question 49:

A company wants to provide low-latency global access to static web content stored in S3 while enforcing HTTPS. Which solution is optimal?

Answer:

A) Amazon CloudFront with S3 origin and HTTPS
B) S3 Standard only
C) EC2 instances in a single region
D) AWS Direct Connect

Explanation:

Option A is correct. Amazon CloudFront is a content delivery network (CDN) that caches content at edge locations around the world, reducing latency for users. By setting the origin as S3, the static content is securely stored and served efficiently. HTTPS ensures encryption in transit, protecting sensitive data and maintaining compliance with security best practices such as PCI DSS or HIPAA.

S3 alone does not provide caching or global edge distribution. EC2 instances in a single region may suffer from high latency for geographically distributed users. AWS Direct Connect provides private connectivity but does not function as a CDN.

CloudFront integrates with additional services like AWS WAF for web application firewall capabilities, allowing protection against common web exploits and DDoS attacks. With CloudFront, caching policies can be configured to optimize performance, such as TTL (Time-to-Live) settings for cached objects. Cache invalidation ensures that updates to S3 content are propagated quickly to edge locations.

Lambda@Edge can be used to execute custom logic, such as user authentication, content personalization, or header modifications, closer to end users, enhancing performance and flexibility. CloudFront also supports origin failover, ensuring that if the primary S3 bucket becomes unavailable, traffic is routed to a secondary bucket, improving availability.

Logging and monitoring through CloudFront and CloudWatch provide insights into cache hits, latency, and request patterns, supporting operational optimization. Cost management is also achievable by optimizing caching strategies to reduce the frequency of origin fetches, minimizing S3 request costs.

This architecture exemplifies AWS best practices for delivering static web content globally: leveraging edge caching for performance, enforcing HTTPS for security, integrating WAF for protection, and enabling observability through logging and monitoring. Organizations can meet business and regulatory requirements while providing a fast, secure, and resilient experience for users worldwide.

Question 50:

A company wants to decouple microservices and ensure reliable communication between components with variable traffic. Which service should be used?

Answer:

A) Amazon SQS
B) Amazon RDS
C) DynamoDB Streams
D) CloudFront

Explanation:

Option A is correct. Amazon Simple Queue Service (SQS) provides a fully managed message queue, allowing decoupled components to communicate asynchronously. This ensures that producers and consumers do not need to be tightly coupled, providing resilience against variable workloads or temporary failures. Messages can be retained in the queue until processed, preventing data loss.

RDS provides a relational database and does not support decoupled messaging. DynamoDB Streams captures changes to DynamoDB tables but is limited to DynamoDB events, making it unsuitable for general-purpose messaging. CloudFront is a CDN, not a messaging service.

SQS offers features like FIFO queues for ordered message processing, dead-letter queues for handling failures, and visibility timeouts to prevent duplicate processing. Auto-scaling consumers can retrieve messages as they arrive, allowing the system to adapt to changing traffic patterns.

Security is enforced via IAM roles, ensuring that only authorized producers and consumers can interact with the queue. CloudWatch metrics provide observability into queue length, throughput, and processing lag, enabling operational efficiency. By decoupling microservices with SQS, organizations improve fault tolerance, scale components independently, and simplify operational management.

SQS is ideal for event-driven architectures, batch processing, and distributed systems where reliability and elasticity are critical. By buffering requests, SQS prevents system overload and ensures smooth operation during traffic spikes. Combined with Lambda, EC2, or ECS, SQS enables a highly resilient, serverless, or containerized architecture that aligns with AWS Well-Architected Framework best practices for operational excellence, reliability, performance efficiency, and cost optimization.

Question 51:

A company wants to encrypt sensitive data at rest in S3 and manage access with fine-grained permissions. Which configuration is recommended?

Answer:

A) SSE-KMS encryption with IAM policies restricting access
B) S3 Standard without encryption
C) Client-side encryption without IAM policies
D) Public S3 bucket

Explanation:

Option A is correct. SSE-KMS encrypts data at rest using AWS Key Management Service (KMS) keys. Fine-grained IAM policies control access to both S3 objects and the KMS keys used for encryption, ensuring only authorized users or roles can read or write data. This approach supports auditing via CloudTrail, showing who accessed or decrypted objects.

S3 Standard without encryption leaves data vulnerable. Client-side encryption can protect data but lacks centralized key management and auditing. Public S3 buckets expose data and violate security best practices.

Using SSE-KMS, administrators can create separate keys for different applications, departments, or environments, enforcing key policies and enabling rotation for compliance. IAM policies can restrict access based on roles, conditions, and resource ARNs. Combined with S3 bucket policies, organizations enforce the principle of least privilege. CloudTrail integration ensures that all access and decryption events are logged for compliance audits.

This configuration is recommended for meeting regulatory and industry compliance standards such as HIPAA, PCI DSS, and GDPR. It simplifies operational security by automating key management while providing granular access control. Organizations can also implement lifecycle policies, versioning, and MFA delete to further enhance security and data durability. By combining encryption, access control, and monitoring, companies can ensure sensitive data in S3 is secure, auditable, and compliant.

Question 52:

A company wants to provide globally low-latency access to dynamic web content stored on EC2 instances. Which AWS service combination is appropriate?

Answer:

A) CloudFront with EC2 origin and HTTPS
B) EC2 alone
C) S3 only
D) Direct Connect

Explanation:

Option A is correct. CloudFront caches static portions of dynamic content and routes requests to EC2 instances when needed. HTTPS ensures secure communication, and global edge locations reduce latency for end users. EC2 alone in a single region cannot provide global low-latency access. S3 only supports static content, and Direct Connect is a private network connection, not a CDN.

CloudFront supports dynamic content acceleration, caching strategies, and integration with Lambda@Edge for request manipulation. It reduces load on origin EC2 instances, enhances performance, and improves user experience. Security features include HTTPS, signed URLs, and WAF integration to protect against attacks.

The architecture also supports failover; CloudFront can route traffic to a secondary origin if the primary fails, increasing availability. Monitoring via CloudWatch enables tracking cache hits, request latency, and error rates, providing operational insights. Cost optimization is achieved by minimizing origin fetches and reducing data transfer costs.

This architecture is suitable for globally distributed applications, including e-commerce sites, media streaming platforms, and SaaS applications. It aligns with AWS best practices for performance, scalability, and security. By leveraging CloudFront with EC2 origins, organizations can deliver dynamic content quickly and securely, providing a high-quality user experience worldwide.

Question 53:

A company wants to automate compliance audits for changes in its AWS resources across multiple accounts. Which service combination is recommended?

Answer:

A) AWS Config and AWS Organizations
B) CloudTrail only
C) CloudFront only
D) EC2 instances only

Explanation:

Option A is correct. AWS Config monitors and records configuration changes across AWS resources, enabling compliance auditing. When combined with AWS Organizations, Config rules and aggregators can be applied across multiple accounts, providing centralized governance and visibility. CloudTrail alone captures API activity but does not enforce compliance. CloudFront is a CDN, and EC2 instances do not provide auditing capabilities.

Config rules can evaluate resources for compliance against organizational policies and regulatory standards. Aggregators consolidate compliance data from multiple accounts and regions, producing a unified view for auditing. Alerts can be triggered via SNS or CloudWatch when non-compliance is detected.

This solution supports continuous monitoring, automated remediation, and reporting for compliance audits. It simplifies managing multi-account AWS environments, reduces manual auditing efforts, and ensures alignment with internal and external regulatory requirements.

By using Config and Organizations, companies can enforce best practices such as encryption enforcement, security group compliance, IAM role policies, and tagging standards across all accounts. Automated compliance reporting enables proactive issue resolution, risk reduction, and operational efficiency. This architecture demonstrates AWS Well-Architected principles for security, operational excellence, and governance.

Question 54:

A company wants to store infrequently accessed backup data in S3 at the lowest cost but can tolerate retrieval times of several hours. Which storage class is ideal?

Answer:

A) S3 Glacier Deep Archive
B) S3 Standard
C) S3 Standard-IA
D) Amazon EBS

Explanation:

Option A is correct. S3 Glacier Deep Archive is the most cost-effective storage class for long-term retention of infrequently accessed data. Retrieval times range from 12–48 hours, which is acceptable for archival purposes. S3 Standard and Standard-IA provide lower retrieval times but at higher cost. EBS is block storage for EC2 and unsuitable for archival workloads.

Glacier Deep Archive supports lifecycle policies to transition data from S3 Standard or Standard-IA automatically, reducing administrative effort. Data is encrypted at rest using SSE-KMS or SSE-S3. It is highly durable, providing 99.999999999% (11 9s) of data durability across multiple AZs.

Ideal use cases include compliance archives, legal retention, and long-term backup storage. Retrieval can be expedited if needed, but cost is higher for faster access. CloudWatch can monitor storage and retrieval operations, providing visibility into costs and usage patterns. Organizations can combine Glacier Deep Archive with audit and compliance frameworks to meet regulatory requirements.

Question 55:

A company wants to migrate an on-premises Oracle database to AWS with minimal downtime. Which AWS service and deployment model should be recommended?

Answer:

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

Explanation:

Option A is correct. AWS Database Migration Service (DMS) allows seamless migration of databases to AWS with minimal downtime. For Oracle databases, DMS supports continuous replication, enabling near-zero downtime cutover. Using Amazon RDS Multi-AZ Oracle ensures high availability, automated backups, patching, monitoring, and failover, significantly reducing operational overhead.

EC2 with self-managed Oracle would require extensive planning for provisioning, scaling, backups, patch management, and failover, increasing complexity and operational risk. S3 is object storage and unsuitable for relational workloads. DynamoDB is a NoSQL database, incompatible with relational Oracle schemas and queries.

DMS supports schema conversion and data validation to ensure the migrated database maintains integrity. For heterogeneous migrations, the AWS Schema Conversion Tool (SCT) can transform database schema and application code. Multi-AZ RDS ensures synchronous replication to a standby instance in another AZ, providing automatic failover in case of hardware or AZ failures.

Security best practices include encrypting data at rest with KMS, restricting access via IAM policies, and monitoring database performance using CloudWatch. DMS enables continuous replication while the source database remains operational, reducing downtime and business disruption. Operational monitoring includes tracking replication lag, identifying transformation errors, and automating failover testing.

By combining DMS with RDS Multi-AZ Oracle, organizations achieve a fully managed, high-availability, scalable, and secure relational database solution on AWS. This approach reduces the operational burden on DBAs, ensures compliance, and accelerates migration while minimizing risk and downtime. It aligns with AWS Well-Architected Framework principles for operational excellence, reliability, performance efficiency, and security.

Question 56:

A company wants to automate security enforcement by detecting and remediating public S3 buckets across multiple accounts. Which service combination should be used?

Answer:

A) AWS Config with AWS Organizations
B) CloudTrail only
C) CloudFront
D) EC2 instances

Explanation:

Option A is correct. AWS Config can evaluate S3 bucket configurations against rules, such as “no public access allowed.” When combined with AWS Organizations, Config rules can be enforced across multiple accounts and regions. This ensures centralized governance, policy enforcement, and automated remediation.

CloudTrail alone captures API activity but does not enforce compliance or remediate misconfigurations. CloudFront is a CDN, and EC2 instances are not suitable for centralized security enforcement.

Config rules can trigger Lambda functions to remediate public buckets automatically, such as applying bucket policies to restrict access. Aggregators consolidate compliance status across accounts, enabling security teams to monitor the entire organization. CloudWatch can provide real-time alerts when non-compliance occurs.

This architecture enforces security best practices, such as least-privilege access and encryption, while providing visibility and auditability. Using IAM roles ensures only authorized actions are executed. Integration with AWS Security Hub can centralize findings from Config, CloudTrail, GuardDuty, and other sources, providing a unified security posture.

By combining Config and Organizations, companies can proactively detect and remediate public exposure of sensitive data, reducing risk and ensuring regulatory compliance. The architecture supports automation, scalability, and consistency across large multi-account AWS environments.

Question 57:

A company wants to implement a caching layer to reduce load on its RDS database and improve application performance. Which AWS service is recommended?

Answer:

A) Amazon ElastiCache (Redis or Memcached)
B) DynamoDB
C) CloudFront
D) S3

Explanation:

Option A is correct. Amazon ElastiCache provides an in-memory caching layer, reducing read pressure on RDS databases and improving application performance. Redis supports data persistence, advanced data structures, and replication for high availability. Memcached provides simple caching for read-heavy workloads.

DynamoDB is a NoSQL database and does not act as a caching layer for RDS. CloudFront is a CDN and only caches static content. S3 is object storage, not a caching solution.

Using ElastiCache reduces latency for frequently accessed data, supports auto-failover, replication, and backup strategies. Integration with application logic allows retrieving data from cache first, falling back to RDS only when necessary. This improves scalability, reduces database costs, and enhances user experience. Security is enforced using VPCs, security groups, and IAM policies. CloudWatch metrics allow monitoring cache hits, misses, latency, and resource utilization.

This architecture is ideal for read-heavy applications, session storage, leaderboard computations, and transient data storage. It aligns with AWS best practices for reliability, performance efficiency, and cost optimization. By reducing database load, ElastiCache improves operational efficiency and supports higher concurrency without scaling RDS instances unnecessarily.

Question 58:

A company wants to implement a centralized logging solution for applications running in multiple regions. Which AWS service combination should be recommended?

Answer:

A) Amazon CloudWatch Logs with CloudWatch cross-account and cross-region aggregation
B) EC2 only
C) S3 only
D) Direct Connect

Explanation:

Option A is correct. CloudWatch Logs allows centralized collection of logs from applications across multiple regions and accounts. Aggregation enables monitoring, alerting, and analytics from a single view, supporting operational efficiency and security compliance.

EC2 alone stores logs locally, creating operational overhead and potential data loss. S3 stores logs but does not provide real-time analysis or alerting. Direct Connect provides private connectivity but not centralized logging.

Cross-account and cross-region aggregation ensures all logs are centralized regardless of where resources reside, enabling unified monitoring and compliance reporting. CloudWatch Logs Insights supports interactive queries and analytics on log data. Integration with CloudWatch Alarms allows triggering actions when specific patterns or thresholds are detected. Logs can be encrypted with KMS for compliance, retained for long-term storage, and archived to S3 for cost optimization.

This architecture provides visibility, operational intelligence, and troubleshooting capabilities. Organizations can monitor application health, detect anomalies, and maintain regulatory compliance with minimal operational burden. By leveraging managed services, centralized logging becomes scalable, secure, and resilient.

Question 59:

A company wants to host a multi-tier web application on AWS with automatic scaling, high availability, and fault tolerance. Which architecture is most suitable?

Answer:

A) EC2 instances across multiple AZs with Auto Scaling, ALB, and RDS Multi-AZ
B) Single EC2 instance with EBS
C) Lambda only
D) S3 static hosting

Explanation:

Option A is correct. Deploying EC2 instances across multiple AZs ensures high availability. Auto Scaling adjusts the number of instances according to demand, optimizing cost and performance. An Application Load Balancer distributes traffic evenly and monitors instance health. RDS Multi-AZ provides a highly available relational database with automatic failover.

A single EC2 instance is a single point of failure. Lambda-only architectures may not handle stateful multi-tier applications. S3 only supports static websites.

This architecture also supports security best practices, including private subnets for EC2 and RDS, encryption at rest, and IAM roles for fine-grained access control. CloudWatch monitoring ensures visibility into system health, performance, and errors. Auto Scaling and Multi-AZ deployments reduce operational overhead and improve resilience against failures.

This design aligns with AWS Well-Architected Framework principles, providing operational excellence, reliability, performance efficiency, and cost optimization. Organizations can scale dynamically, ensure fault tolerance, and deliver a consistent, high-performance user experience. Integration with CloudFront, WAF, and Route 53 can further enhance security, global availability, and performance.

Question 60:

A company wants to migrate a large-scale on-premises data warehouse to AWS and enable fast analytical queries with minimal administrative effort. Which service should be recommended?

Answer:

A) Amazon Redshift with Spectrum and S3
B) RDS Multi-AZ
C) DynamoDB
D) S3 only

Explanation:

Option A is correct. Amazon Redshift is a fully managed data warehouse service designed for large-scale analytical workloads. Redshift Spectrum allows querying data directly in S3 without moving it into the cluster, reducing storage costs and increasing flexibility.

RDS is for transactional workloads, not optimized for analytics. DynamoDB is NoSQL and unsuitable for complex analytical queries. S3 alone is object storage and does not provide query capabilities.

Redshift provides columnar storage, data compression, and massively parallel processing (MPP), ensuring high performance for complex queries. Features like concurrency scaling and automatic workload management allow handling fluctuating workloads efficiently. Security is enforced with VPC isolation, IAM policies, KMS encryption, and auditing via CloudTrail.

By integrating with S3, Redshift Spectrum enables a hybrid approach where frequently accessed data resides in Redshift, while historical or infrequently queried data remains in S3. This reduces costs and maintains performance. Automated backups, snapshots, and monitoring through CloudWatch reduce administrative burden. Redshift integrates with BI tools like QuickSight, providing seamless reporting and analytics.

This architecture ensures high availability, fault tolerance, and operational efficiency, allowing organizations to migrate large datasets to the cloud while leveraging AWS managed services to optimize cost, performance, and scalability. It aligns with AWS best practices for analytical workloads, providing a secure, scalable, and high-performance solution for enterprise data analytics.