Amazon AWS Certified DevOps Engineer — Professional DOP-C02 Exam Dumps and Practice Test Questions Set 7 Q91-105

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Question 91

A financial analytics platform runs on Amazon Redshift using multiple data sources, including transactional databases, streaming feeds, and S3 data lakes. The team needs a mechanism that automatically enforces data access policies column-by-column and row-by-row for different user groups. Analysts must only see data that matches their department permissions, and administrators want to maintain these rules in one central place without rewriting SQL queries. Which solution best satisfies these requirements?

A) Amazon Redshift data sharing
B) Amazon Redshift RA3 node scaling
C) Amazon Redshift row-level and column-level security with AWS Lake Formation
D) Amazon Redshift concurrency scaling

Answer: C) Amazon Redshift row-level and column-level security with AWS Lake Formation

Explanation:

Amazon Redshift row-level and column-level security combined with AWS Lake Formation provides a unified access control mechanism designed to control what data users can see without requiring developers to modify SQL queries or restructure existing analytics workflows. Lake Formation centralizes data governance definitions, allowing administrators to create and enforce fine-grained access policies across data stored in Redshift, S3-based data lakes, Athena queries, and integrated analytics environments. These permissions can be tied to IAM identities, SSO groups, or Redshift database users. The rules determine which rows and columns each user is allowed to access, ensuring sensitive information is hidden automatically from unauthorized analysts.

Row-level security restricts table records according to user attributes or defined filters. For example, a company’s finance analysts may only see rows where the department code matches “FIN.” Column-level security hides sensitive fields such as Social Security numbers, personal addresses, or salary values. Queries run normally without any modification from the analyst, and the Redshift engine automatically applies filters and masks according to Lake Formation authorization definitions. This separation of governance rules from application logic is critical for financial organizations that must comply with regulations like SOX, PCI DSS, and internal auditing controls.

Redshift integrates natively with Lake Formation to evaluate permissions every time a query is executed. This means organizations no longer need to maintain multiple duplicate datasets for different user groups, saving storage space and reducing operational complexity. Analysts continue using standard SQL while data engineers maintain centralized governance rules. This architecture improves both security and productivity.

Amazon Redshift data sharing provides a way to share datasets across Redshift clusters without moving data. Although it enhances performance and multi-cluster analytics, it does not enforce row-level or column-level security controls. Data sharing alone cannot satisfy fine-grained access requirements because it focuses on resource efficiency rather than governance.

Amazon Redshift RA3 node scaling helps separate compute and storage, allowing workloads to scale elastically. While useful for managing cluster performance, it does not apply access policies or govern user-level permissions. RA3 scaling addresses infrastructure needs but does not solve authorization challenges.

Amazon Redshift concurrency scaling automatically adds compute resources during periods of high query load. Although this improves cluster responsiveness, it does not offer any governance features. It cannot enforce row-based or column-based controls, and therefore cannot meet the security requirements described.

By combining Amazon Redshift’s built-in security capabilities with Lake Formation’s centralized governance model, organizations gain a scalable, maintainable, and fully integrated solution that ensures data access rules are enforced uniformly. This mechanism satisfies regulatory, operational, and compliance needs, making it the best fit for the given scenario. Thus, the correct answer is the integration of Redshift row-level and column-level security with AWS Lake Formation.

Question 92

A global e-commerce company stores millions of user images and product photos in Amazon S3. They need a solution to automatically scan new images for inappropriate content, detect objects, extract labels, and store the results in a searchable index. The process must scale automatically, operate in near real time, and require minimal operational overhead. Which solution meets these requirements?

A) Amazon Rekognition with AWS Lambda and Amazon OpenSearch Service
B) AWS Glue and Amazon SageMaker
C) Amazon Aurora global database
D) AWS Backup with S3 lifecycle policies

Answer:  A) Amazon Rekognition with AWS Lambda and Amazon OpenSearch Service

Explanation:

Amazon Rekognition integrates seamlessly with Amazon S3 to provide automated content detection for images. When new images are uploaded to S3, event notifications can trigger AWS Lambda functions to call Rekognition APIs. These APIs detect unsafe content, generate labels, perform object recognition, extract attributes, and even generate face analysis metadata. The output can be stored in Amazon OpenSearch Service, which provides real-time indexing, full-text search, and fast retrieval capabilities. This architecture enables near real-time processing as images flow into the system.

By using an event-driven design with S3 triggers and Lambda functions, the system automatically scales with incoming workloads. Lambda manages compute allocation automatically, meaning operators do not need to provision servers, manage clusters, or handle peak traffic patterns manually. OpenSearch Service scales horizontally, allowing high-speed indexing and efficient querying of metadata attributes such as detected objects, label confidence scores, moderation flags, and extracted text. The solution provides end-to-end automation with minimal operational effort.

AWS Glue and Amazon SageMaker are powerful for ETL pipelines and machine learning model training, but they do not provide turnkey image moderation or near real-time detection capabilities without extensive manual development. Glue is batch-oriented and unsuitable for event-driven scenarios. SageMaker would require constructing custom models, training pipelines, and deployment endpoints, significantly increasing complexity relative to the requirements.

Amazon Aurora global database is a managed relational database for multi-region applications. It delivers fast replication and global availability but has no image recognition capabilities. Aurora cannot detect content in images, perform labeling, or index metadata without external systems, making it unsuitable for this scenario.

AWS Backup with S3 lifecycle policies focuses on data protection and archival. It cannot analyze image content and does not provide indexing or search. Backup workflows are designed for recovery and compliance, not real-time content processing.

The combination of Amazon Rekognition, AWS Lambda, and Amazon OpenSearch Service delivers a fully automatic, scalable, and low-maintenance solution aligned with the company’s needs.

Question 93

A software company uses AWS Step Functions to orchestrate complex workflows. They need a workflow pattern that supports manual approvals, integrates with human decision points, pauses for external input, and then automatically resumes once the response is received. The solution must avoid polling and minimize cost while ensuring high reliability. What should they use?

A) Step Functions callback patterns using task tokens
B) Step Functions Express Workflows
C) Amazon SQS with long polling loops
D) Amazon MSK streaming triggers

Answer:  A) Step Functions callback patterns using task tokens

Explanation:

Step Functions callback patterns allow a workflow to pause execution while waiting for external human or system input. The workflow issues a task token, passed to an external system through services like Lambda, API Gateway, SNS, or event-based communication. The external system, such as a human approval portal or automated ticketing system, sends the task token back to Step Functions once the required decision has been made. This allows the workflow to resume precisely where it left off. The callback mechanism eliminates the need for polling loops, reducing cost and preventing unnecessary compute cycles.

Callback patterns are ideal for approval workflows, multi-person authorization processes, or systems that rely on external business inputs. Step Functions manages state transitions durably, ensuring that paused states persist across retries, service failures, or extended wait times. Unlike long polling or synchronous integrations, callback tokens keep costs low by not consuming resources while waiting. When integrated with services such as Amazon SNS or API Gateway, the callback design provides flexibility for both automated and manual decision flows.

Express Workflows are optimized for short-duration, high-throughput tasks. They are not suitable for long pauses or human-in-the-loop processes because they have limited execution duration and lack the durability required for extended approvals.

Amazon SQS long polling reduces empty receives but still requires continuous polling mechanisms. Polling is inefficient, incurs cost over time, and complicates workflow management. SQS does not store workflow state transitions or integrate natively with task tokens.

Amazon MSK is a streaming platform based on Apache Kafka. It is valuable for event streaming but does not handle workflow pause-and-resume logic. MSK does not offer an in-workflow approval mechanism and cannot serve as a durable state manager for multi-step processes.

Callback patterns are purpose-built for workflows requiring human interaction, making them the correct choice.

Question 94

A company deploys a microservices architecture using AWS ECS with Fargate. They need to enforce that every container image is scanned for vulnerabilities before deployment, automatically block noncompliant images, and integrate this process with their CI/CD pipelines across multiple accounts. Which solution meets these requirements?

A) Amazon ECR image scanning with enhanced scanning and AWS CodePipeline integration
B) Amazon S3 versioning with lifecycle policies
C) Amazon CloudFront caching policies
D) AWS CloudTrail event logging

Answer:  A) Amazon ECR image scanning with enhanced scanning and AWS CodePipeline integration

Explanation :

Amazon ECR enhanced image scanning provides automated vulnerability detection for container images as they are pushed to the registry. When integrated with AWS CodePipeline, ECR can enforce a policy gate within the CI/CD workflow to prevent deployment of containers with critical or high-severity vulnerabilities. Enhanced scanning leverages Amazon Inspector’s vulnerability database to analyze all layers of the container, producing detailed findings with severity levels and recommended remediations. The scanning process can be triggered on push or periodically to ensure that images remain compliant throughout their lifecycle. By integrating ECR findings into the CI/CD pipeline, teams can configure automated approvals or rejections, ensuring that only secure, vetted images progress to staging or production.

This solution is especially effective in multi-account environments where ECR repositories are replicated or shared using resource-based policies. IAM permissions ensure that only authorized teams can push or pull images, while ECR maintains audit logs for all access and scanning events, supporting compliance requirements. Using enhanced scanning with automated gating eliminates manual reviews, reducing operational overhead while maintaining security. The combination of CI/CD enforcement and centralized scanning ensures that vulnerabilities do not reach production, aligning with DevSecOps best practices.

Amazon S3 versioning with lifecycle policies is designed to maintain object versions and automatically manage storage retention. While useful for data durability, it does not analyze or scan container images, nor can it enforce deployment compliance. Lifecycle policies only manage storage, making it unsuitable for security or CI/CD integration.

Amazon CloudFront caching policies optimize content delivery performance by controlling caching behavior at edge locations. CloudFront cannot scan container images, enforce vulnerability gates, or integrate with ECS deployments. Its purpose is content acceleration, not security or image management.

AWS CloudTrail records API activity and provides audit logs for user and service actions. While CloudTrail logs may capture image pushes or pulls, it does not perform vulnerability scanning, enforce compliance gates, or integrate automatically with CI/CD pipelines to prevent deployment of insecure images. CloudTrail is an auditing tool, not a preventative enforcement mechanism.

By combining Amazon ECR enhanced scanning with CodePipeline, the DevOps team gains a fully managed, automated, and enforceable workflow for container image security. Vulnerabilities are detected before deployment, policy compliance is maintained centrally, and multi-account governance is supported. This ensures a consistent, repeatable, and secure microservices deployment process. The combination addresses all the requirements outlined, making it the correct solution.

Question 95

A company uses Amazon EKS to host multiple microservices. They need a solution to automatically detect configuration drift in the cluster, alert developers when unauthorized changes occur, and remediate drift while minimizing operational effort. Which service combination best meets these requirements?

A) AWS Config + Kubernetes ConfigMap/Secret monitoring
B) Amazon CloudFront + S3 logging
C) AWS Backup + EFS lifecycle policies
D) Amazon QuickSight + Athena

Answer:  A) AWS Config + Kubernetes ConfigMap/Secret monitoring

Explanation:

AWS Config provides continuous evaluation of AWS resources against defined rules. By integrating AWS Config with Kubernetes cluster monitoring for ConfigMaps and Secrets, organizations can automatically detect unauthorized changes in cluster configurations. Config records the current state of resources, including ECS clusters, EKS clusters, and their associated AWS components, creating a history of changes. When combined with a monitoring system that observes Kubernetes ConfigMaps and Secrets, Config can alert developers whenever deviations from approved configurations occur. These alerts are delivered through EventBridge, SNS, or integrated DevOps pipelines, enabling immediate visibility into drift events.

Remediation is achievable through AWS Systems Manager Automation or Lambda functions that apply preapproved configurations to the cluster when drift is detected. This approach ensures that all clusters remain compliant with organizational standards while reducing manual intervention. By using declarative configuration monitoring and automated remediation, operational overhead is minimized, and teams can enforce governance across multiple environments without constant manual checks. Config’s integration with CloudTrail ensures that all remediation actions are logged and auditable, providing compliance documentation.

Amazon CloudFront with S3 logging primarily focuses on content delivery and logging of edge requests. While it can provide insights into request patterns, it does not track cluster configuration, cannot detect drift in Kubernetes resources, and cannot remediate unauthorized changes. Its purpose is not operational governance.

AWS Backup with EFS lifecycle policies handles backup and retention of file systems and data. While useful for disaster recovery, it does not provide real-time monitoring of cluster configuration or alert developers to unauthorized changes. Backup tools are reactive, not proactive, and cannot enforce compliance within Kubernetes workloads.

Amazon QuickSight with Athena provides analytics and reporting capabilities. It can query structured data for trends or operational metrics, but it cannot detect configuration drift or remediate unauthorized changes in EKS clusters. QuickSight is visualization-focused and does not serve operational governance functions.

The combination of AWS Config and Kubernetes monitoring provides a complete solution for proactive drift detection, alerting, automated remediation, audit logging, and minimal operational overhead. Config evaluates cluster state continuously, while ConfigMap and Secret monitoring ensures sensitive and configuration data are aligned with approved policies. Automated remediation guarantees that deviations are corrected without human intervention. Event-driven notifications ensure timely awareness, and logs provide complete auditability. This approach is fully managed, scalable across multiple clusters and accounts, and aligns with DevOps and compliance best practices, making it the correct solution.

Question 96

A company runs multiple serverless applications on AWS Lambda. They want to centralize observability across functions, automatically detect anomalies in invocation metrics, trigger automated responses for failed executions, and provide detailed dashboards for developers. Which service combination best fulfills these requirements?

A) Amazon CloudWatch + CloudWatch Anomaly Detection + EventBridge
B) Amazon S3 + Athena
C) Amazon Kinesis Data Firehose + Redshift
D) AWS Config + Systems Manager

Answer:  A) Amazon CloudWatch + CloudWatch Anomaly Detection + EventBridge

Explanation:

Amazon CloudWatch provides centralized monitoring for Lambda functions, capturing metrics such as invocation count, duration, errors, throttles, and concurrency usage. Logs from Lambda functions are automatically delivered to CloudWatch Logs, where they can be aggregated, searched, and analyzed. CloudWatch dashboards provide real-time visibility into application health and performance across multiple functions, allowing developers to identify trends, bottlenecks, and anomalies. With these dashboards, developers gain a comprehensive operational view of all serverless applications from a single pane of glass.

CloudWatch Anomaly Detection leverages machine learning to identify deviations from expected metric patterns. By applying Anomaly Detection models to Lambda metrics, the service can detect unusual invocation patterns, increased error rates, latency spikes, or other operational anomalies automatically. Unlike static threshold alarms, anomaly detection adapts dynamically to normal variations, reducing false positives while improving responsiveness to actual operational issues. This is especially useful in production serverless environments where traffic may fluctuate widely, making static thresholds unreliable.

EventBridge integrates with CloudWatch to automate responses to anomalous events. For example, when a Lambda error rate exceeds the anomaly threshold, EventBridge can trigger a remediation workflow, such as invoking another Lambda function to retry failed tasks, updating configuration, sending notifications to the operations team, or scaling related services. This automation ensures faster reaction times, reduces manual intervention, and maintains application availability.

Amazon S3 and Athena are primarily used for storage and analytics. While they can store logs and allow batch queries for historical insights, they do not provide real-time anomaly detection, metrics monitoring, or automated responses. Analysts would need to run queries manually, which cannot satisfy immediate operational requirements for serverless applications.

Amazon Kinesis Data Firehose with Redshift is a streaming ingestion and analytics pipeline. It is suitable for high-throughput data aggregation and querying, but it does not provide centralized observability, anomaly detection, or automated workflow triggers for Lambda executions. This combination focuses on data analytics rather than operational monitoring.

AWS Config with Systems Manager focuses on resource configuration management and compliance. It can detect drift and apply automated remediation for infrastructure but does not provide detailed function-level metrics, dashboards, or anomaly detection for runtime Lambda invocations.

Combining CloudWatch, CloudWatch Anomaly Detection, and EventBridge delivers centralized observability, dynamic anomaly detection, automated responses, and operational dashboards. This approach minimizes operational effort, enables proactive management of serverless applications, and aligns with DevOps best practices, making it the correct solution.

Question 97

A DevOps team runs multiple containerized applications on Amazon ECS with Fargate. They want to automatically detect when tasks consume more CPU or memory than allowed, trigger alerts, and scale containers without manual intervention. Which AWS service combination best meets these requirements?

A) Amazon CloudWatch metrics + ECS Service Auto Scaling
B) AWS Config + Lambda
C) Amazon S3 + Athena
D) AWS Backup + SNS

Answer:  A) Amazon CloudWatch metrics + ECS Service Auto Scaling

Explanation:

Amazon CloudWatch monitors Amazon ECS Fargate tasks and services, providing metrics such as CPU utilization, memory utilization, task counts, and service health. These metrics allow DevOps teams to track the performance of each containerized application and identify when resources exceed thresholds. CloudWatch can automatically generate alarms when CPU or memory usage exceeds defined limits. For example, an alarm can trigger if average CPU utilization surpasses 80% for a given period, indicating that tasks may need more capacity to maintain performance. Memory utilization alarms serve a similar function, signaling when containers are under pressure, which is critical for maintaining stability and preventing throttling.

ECS Service Auto Scaling integrates with CloudWatch to dynamically adjust the number of running tasks. When CloudWatch metrics indicate resource saturation or underutilization, Auto Scaling policies can automatically increase or decrease task count. This ensures that applications maintain desired performance levels without manual intervention, improving reliability and cost efficiency. Policies can be defined to scale in small increments for gradual adjustment or more aggressively during high-load periods. Auto Scaling also integrates with multiple services such as Application Load Balancers, enabling traffic to be distributed evenly across scaled tasks.

AWS Config combined with Lambda can monitor resource configurations and trigger remediation, but it does not automatically detect runtime performance issues such as CPU or memory overutilization. Config is more suited for compliance and drift detection, not operational scaling. Therefore, it cannot meet the requirement for automated scaling based on real-time metrics.

Amazon S3 and Athena provide storage and analytics but cannot monitor ECS metrics or trigger scaling actions. S3 is a data store, and Athena allows queries on stored datasets. This combination has no capability for dynamic resource scaling or alerting for container workloads.

AWS Backup combined with SNS focuses on data protection and notification. Backup ensures recoverability and data integrity, while SNS can alert users to backup completion or failures. However, this combination does not monitor runtime ECS metrics, scale tasks, or respond to resource constraints, making it unsuitable for the described scenario.

By combining CloudWatch and ECS Service Auto Scaling, organizations gain real-time monitoring, alerting, and dynamic scaling, fulfilling all operational requirements. CloudWatch ensures visibility into resource usage, while Auto Scaling ensures applications respond automatically to demand, improving reliability and minimizing manual effort. This is the correct solution.

Question 98

A company deploys AWS Lambda functions across multiple accounts. They want to centrally enforce encryption for all environment variables, track changes to functions, and automatically remediate noncompliant functions. Which service combination best fulfills these requirements?

A) AWS Config + Lambda + Systems Manager Automation
B) Amazon S3 + Athena
C) AWS CloudTrail + CloudFront
D) Amazon RDS + IAM

Answer:  A) AWS Config + Lambda + Systems Manager Automation

Explanation:

AWS Config continuously monitors AWS resources and evaluates them against predefined compliance rules. For Lambda functions, Config can check whether environment variables are encrypted using AWS Key Management Service (KMS) keys. When a function is found to be noncompliant, Config generates findings and logs the event for auditing purposes. This continuous evaluation ensures that all Lambda functions across multiple accounts adhere to the organization’s security and encryption standards. Config supports multi-account deployment using AWS Organizations, allowing centralized enforcement across a distributed environment. Historical configurations are also maintained, providing a complete audit trail for compliance reporting.

Systems Manager Automation complements Config by providing the ability to remediate noncompliant resources automatically. Once Config identifies a function with unencrypted environment variables, a pre-defined automation runbook can be triggered. This runbook can modify the Lambda function to enable KMS encryption, update permissions, and verify compliance without manual intervention. Using Systems Manager in conjunction with Config ensures that remediation is both repeatable and auditable. Automation reduces the operational burden of manually checking and updating Lambda functions across multiple accounts.

Lambda functions themselves can be leveraged to perform additional custom remediation or logging tasks. For example, a Lambda function triggered by Config can handle advanced validation scenarios, alert specific teams, or orchestrate more complex changes.

Amazon S3 combined with Athena provides storage and querying capabilities but cannot enforce encryption on Lambda environment variables, monitor changes to functions, or trigger automatic remediation. It is primarily used for data analytics, not operational governance.

AWS CloudTrail combined with CloudFront provides audit logs and content delivery features. CloudTrail records API calls and can track changes to Lambda functions but does not evaluate compliance against encryption standards or apply automated remediation. CloudFront is unrelated to serverless governance.

Amazon RDS combined with IAM focuses on database management and access control. While IAM manages permissions and RDS provides secure database storage, neither service evaluates or enforces Lambda environment variable encryption or automates compliance workflows.

Using AWS Config with Systems Manager Automation ensures centralized enforcement, real-time compliance monitoring, automatic remediation, and full auditability across multiple AWS accounts. It is the only combination that meets all requirements for secure and compliant Lambda function management, making it the correct solution.

Question 99

A company is building a global application on AWS that requires low-latency API access for users worldwide. The DevOps team wants to automatically route requests to the nearest healthy region, monitor API performance, and failover to secondary regions during outages. Which combination of AWS services is best suited for this architecture?

A) Amazon Route 53 latency-based routing + Health Checks + CloudWatch
B) Amazon CloudFront + S3
C) AWS Direct Connect + VPC Peering
D) Amazon SNS + Lambda

Answer:  A) Amazon Route 53 latency-based routing + Health Checks + CloudWatch

Explanation:

Amazon Route 53 latency-based routing allows requests to be directed to the region that provides the lowest network latency to the end user. This ensures optimal performance for global applications by minimizing response times and improving user experience. By defining multiple endpoints in different AWS regions, latency-based routing ensures that users are automatically connected to the nearest healthy endpoint. Route 53 health checks continuously monitor the status of endpoints, automatically detecting failures or degraded performance. If an endpoint becomes unhealthy, Route 53 can redirect traffic to the next best-performing region without manual intervention, supporting automatic failover for high availability.

CloudWatch complements Route 53 by providing real-time monitoring of API performance, error rates, latency, and traffic patterns across all endpoints. CloudWatch metrics allow DevOps teams to set alarms and receive notifications when performance thresholds are exceeded. These alarms can trigger automated remediation actions or operational workflows to resolve issues quickly. Together, Route 53 and CloudWatch provide a complete solution for global performance optimization, high availability, and observability.

Amazon CloudFront combined with S3 is primarily a content delivery solution. CloudFront improves latency for static and dynamic web content but does not provide full DNS-based global failover, latency-based routing, or regional health checks. It cannot dynamically redirect API requests based on endpoint health across multiple regions.

AWS Direct Connect with VPC Peering provides private connectivity and secure networking between data centers and VPCs. While useful for dedicated connections and cross-VPC communication, this combination does not handle global routing, endpoint health monitoring, or automatic failover for public APIs.

Amazon SNS with Lambda provides messaging and event-driven compute capabilities. While it can trigger automated workflows and notifications, it does not route global API traffic or provide latency-based DNS failover.

Using Route 53 latency-based routing, integrated health checks, and CloudWatch monitoring allows automated, low-latency request routing to healthy regions, supports failover, and provides observability across the global application. This approach satisfies all stated requirements, making it the correct solution.

Question 100

A DevOps team manages multiple serverless applications running on AWS Lambda. They want to reduce cold-start latency, ensure high availability, and maintain predictable response times. Which strategy should they implement?

A) Lambda Provisioned Concurrency
B) Lambda Reserved Concurrency
C) Lambda Event Source Mapping
D) Lambda Destinations

Answer:  A) Lambda Provisioned Concurrency

Explanation:

Lambda Provisioned Concurrency initializes a specified number of execution environments in advance so that they are ready to respond immediately to requests. Cold starts occur when a Lambda function is invoked for the first time or after a period of inactivity, resulting in initialization latency. Provisioned Concurrency eliminates cold starts by pre-warming the function with a consistent number of ready environments. This approach ensures predictable performance, low latency, and higher availability during sudden traffic spikes. Provisioned Concurrency integrates with AWS Auto Scaling to adjust the number of pre-initialized instances automatically based on traffic patterns. This provides both operational efficiency and predictable user experience for serverless applications. It is especially valuable for latency-sensitive applications like financial services or real-time APIs.

Reserved Concurrency allows setting the maximum number of concurrent Lambda executions. While it prevents throttling and ensures that other functions are not impacted by excessive usage, it does not pre-warm environments. Therefore, it does not reduce cold-start latency or improve predictability of response times.

Event Source Mapping enables Lambda to process events from services like SQS or DynamoDB Streams. While it ensures event-driven execution, it does not affect cold-start behavior or provide pre-initialized execution environments. It focuses on integrating Lambda with data sources, not performance tuning.

Lambda Destinations allow asynchronous Lambda functions to send execution results to targets such as SNS or SQS. While useful for post-processing and workflow orchestration, destinations do not address latency, availability, or cold starts.

Provisioned Concurrency is the only mechanism that pre-warms Lambda execution environments, ensuring immediate response and high availability. Combined with Auto Scaling policies, it provides predictable performance, making it the correct solution.

Question 101

A company uses Amazon API Gateway and AWS Lambda for a public-facing API. They want to prevent abuse, limit request rates per customer, and enforce quotas while minimizing latency. Which combination of services best meets these requirements?

A) API Gateway usage plans + API keys + CloudWatch metrics
B) AWS WAF + S3 versioning
C) Amazon CloudFront + Lambda Destinations
D) AWS Config + Systems Manager Automation

Answer:  A) API Gateway usage plans + API keys + CloudWatch metrics

Explanation:

API Gateway usage plans allow administrators to define throttling limits and quotas for individual customers or API clients. API keys are issued to clients, allowing the gateway to associate each request with a specific key. The usage plan enforces request limits (rate limits) and daily, weekly, or monthly quotas. This prevents abusive traffic, ensures fair resource utilization, and protects backend services from overload. CloudWatch metrics collect request counts, latencies, error rates, and throttling events. Metrics enable monitoring, alerting, and operational insight, ensuring teams can detect and respond to anomalies.

AWS WAF protects against web exploits and common attack patterns but does not provide per-customer throttling or API quota enforcement. WAF operates at the application layer for security, not for client-specific rate limiting.

Amazon CloudFront provides caching and low-latency content delivery but cannot enforce per-client quotas or usage plans. Lambda Destinations help with asynchronous post-processing but do not control API request rates.

AWS Config and Systems Manager Automation enforce compliance rules and infrastructure governance. They cannot track API requests or throttle client usage.

By combining API Gateway usage plans with API keys, the company can enforce precise rate limits and quotas while CloudWatch provides operational visibility. This approach minimizes latency while protecting backend resources.

Question 102

A company runs multiple Amazon RDS databases across AWS accounts. They want to ensure backups are retained for at least 365 days, encrypt all snapshots, and automatically detect noncompliant snapshots. Which combination of AWS services best meets these requirements?

A) AWS Backup + AWS Config + KMS encryption
B) Amazon S3 lifecycle policies + Athena
C) CloudFront + Lambda
D) Amazon QuickSight + CloudTrail

Answer:  A) AWS Backup + AWS Config + KMS encryption

Explanation:

AWS Backup provides centralized management for backup policies across RDS and other AWS services. Backup plans can enforce retention periods, ensuring backups are retained for the required duration. By integrating with AWS Key Management Service (KMS), backups and snapshots are encrypted automatically using organizational keys, protecting sensitive data at rest. AWS Config evaluates resources against compliance rules. For RDS snapshots, Config can detect unencrypted snapshots, snapshots that violate retention policies, or snapshots not created under AWS Backup plans. Config triggers alerts or automated remediation using Systems Manager Automation, ensuring all RDS backups adhere to security and retention standards.

Amazon S3 lifecycle policies automate object expiration and transitions in S3 buckets. While suitable for object retention, S3 lifecycle policies do not control RDS backups, enforce encryption, or provide automated compliance evaluation.

CloudFront with Lambda focuses on content delivery and event-driven processing. This combination cannot manage RDS backups or enforce retention or encryption policies.

QuickSight and CloudTrail provide analytics and auditing. CloudTrail logs backup activities, and QuickSight can visualize trends. However, they cannot enforce retention, encryption, or automatic compliance for RDS snapshots.

Using AWS Backup with Config and KMS ensures centralized management, automated encryption, retention enforcement, and compliance monitoring. This combination addresses all operational and governance requirements, making it the correct solution.

Question 103

A company uses AWS Fargate for containerized applications. They want to ensure that every container logs are automatically centralized, searchable, and monitored for anomalies without managing logging infrastructure. Which combination of services best fulfills these requirements?

A) CloudWatch Logs + CloudWatch Logs Insights + CloudWatch Anomaly Detection
B) Amazon S3 + Athena
C) Amazon RDS + IAM
D) AWS Config + Systems Manager

Answer:  A) CloudWatch Logs + CloudWatch Logs Insights + CloudWatch Anomaly Detection

Explanation:

CloudWatch Logs collects logs from ECS Fargate tasks automatically. Containers send logs to CloudWatch without the need for self-managed logging infrastructure. Logs are centralized and durable, allowing developers to query them at any time. CloudWatch Logs Insights provides interactive, ad hoc querying capabilities. Queries allow filtering, parsing, aggregating, and analyzing logs to identify patterns, errors, or operational issues. Developers can quickly extract meaningful insights from vast log volumes without complex infrastructure or indexing.

CloudWatch Anomaly Detection applies machine learning to metrics derived from logs, enabling automatic detection of unusual behavior. Anomaly detection identifies deviations from expected patterns, such as unexpected errors, latency spikes, or unusual throughput. Combined with alarms, this triggers notifications or automated remediation when anomalies occur. This approach minimizes operational effort while maintaining observability, performance monitoring, and proactive incident detection.

Amazon S3 with Athena is suitable for batch log analysis but does not provide real-time monitoring, anomaly detection, or operational dashboards. It requires manual query execution, making it unsuitable for proactive monitoring.

Amazon RDS and IAM focus on relational data management and access control. They do not collect, analyze, or monitor container logs in real time.

AWS Config and Systems Manager focus on resource compliance and automation. They cannot provide centralized logging, real-time search, or anomaly detection for container workloads.

CloudWatch Logs, Logs Insights, and Anomaly Detection provide a fully managed, real-time, and scalable observability solution for ECS Fargate containers. This combination fulfills centralized logging, interactive analysis, anomaly detection, and minimal operational overhead, making it the correct solution.

Question 104

A company runs multiple AWS Lambda functions that process sensitive financial data. They want to ensure all functions enforce encryption of environment variables, prevent unauthorized code changes, and provide centralized auditing for compliance. Which combination of services meets these requirements?

A) AWS Config + Lambda + AWS Key Management Service (KMS)
B) Amazon S3 + Athena
C) AWS CloudFront + WAF
D) Amazon QuickSight + CloudTrail

Answer:  A) AWS Config + Lambda + AWS Key Management Service (KMS)

Explanation:

AWS Config provides continuous monitoring of AWS resources, including Lambda functions, to ensure they comply with predefined rules. For functions processing sensitive data, Config can enforce that all environment variables are encrypted with AWS Key Management Service (KMS). When a Lambda function violates the encryption requirement, Config can flag the noncompliance, generate audit logs, and optionally trigger automated remediation workflows. This ensures that sensitive information stored in environment variables is always protected and meets organizational security standards.

AWS KMS provides the cryptographic backbone for securing environment variables in Lambda functions. By enforcing encryption with KMS keys, the organization ensures that data at rest is encrypted and only accessible to authorized entities. Each key is auditable, allowing teams to track which principal accessed or decrypted data, which is critical for regulatory compliance in industries like finance. Integration with Config enables automatic monitoring and enforcement, reducing the risk of accidental exposure.

Lambda itself can be configured with execution policies and roles that restrict changes to function code and permissions. By combining Lambda with Config and KMS, organizations achieve a multi-layered security posture. Config ensures compliance is continuously evaluated, KMS secures sensitive variables, and Lambda enforces role-based access to code. The combination also allows integration with Systems Manager or EventBridge to trigger notifications or automated actions whenever noncompliance is detected.

Amazon S3 combined with Athena provides storage and analytics capabilities. While it can be used to store logs or query structured data, it does not enforce encryption of Lambda environment variables, monitor function compliance, or prevent unauthorized code changes. This combination is more suited for analytics than security governance.

AWS CloudFront and WAF focus on web application delivery and protection against common attacks. They do not monitor Lambda functions, enforce environment variable encryption, or provide centralized auditing for compliance.

Amazon QuickSight and CloudTrail provide visualization and auditing capabilities. CloudTrail logs API activity, which is useful for monitoring changes, but it does not enforce encryption or prevent unauthorized code changes automatically. QuickSight allows dashboards and reporting but does not enforce compliance or secure sensitive environment data.

The combination of AWS Config, Lambda, and KMS provides continuous compliance evaluation, strong encryption enforcement, auditable logs, and the ability to prevent unauthorized changes. This approach ensures that sensitive financial data is protected while meeting regulatory and organizational requirements, making it the correct solution.

Question 105

A company uses Amazon ECS with Fargate for multiple microservices. They want to centralize logs from all containers, query logs in near real time, detect anomalies, and visualize trends for operational insights. Which combination of AWS services best meets these requirements?

A) Amazon CloudWatch Logs + CloudWatch Logs Insights + CloudWatch Anomaly Detection
B) Amazon S3 + Athena
C) AWS Config + Lambda
D) Amazon RDS + QuickSight

Answer:  A) Amazon CloudWatch Logs + CloudWatch Logs Insights + CloudWatch Anomaly Detection

Explanation:

Amazon CloudWatch Logs centralizes logging from ECS Fargate containers without requiring any self-managed infrastructure. Each container can be configured to automatically send logs to CloudWatch Logs, which stores them durably and makes them available for querying. This eliminates the need to maintain log aggregation systems within the cluster, reducing operational complexity and costs. Centralized logs enable DevOps teams to monitor container behavior, track application performance, and troubleshoot issues efficiently.

CloudWatch Logs Insights allows interactive, near real-time querying of logs. Users can filter, parse, aggregate, and analyze log data without building complex indexes or additional infrastructure. Queries can detect trends, error patterns, and latency issues, providing actionable operational insights. Insights can be run ad hoc or scheduled to support continuous monitoring of container workloads.

CloudWatch Anomaly Detection uses machine learning to automatically identify deviations from normal operational behavior. Metrics derived from logs, such as error rates, request counts, or latency, are analyzed to detect unusual patterns. When anomalies occur, CloudWatch can trigger alarms and notifications to developers or automated workflows for remediation. This reduces the need for manual monitoring, allows proactive incident response, and ensures high reliability and availability for microservices.

Amazon S3 combined with Athena is suitable for batch log analysis and querying of historical data. However, this approach does not provide near real-time insights, anomaly detection, or active monitoring. Logs must be written to S3 and queried manually, which introduces latency and operational overhead.

AWS Config and Lambda are primarily used for infrastructure compliance and automation. Config monitors resources for compliance with predefined rules, and Lambda can remediate noncompliant configurations. This combination does not provide centralized container logging, real-time querying, or operational dashboards.

Amazon RDS and QuickSight can store structured data and visualize trends, but they are not designed to handle container log streams in near real time. RDS stores relational data, and QuickSight provides analytics visualization. Neither service supports automated anomaly detection for live operational logs.

The combination of CloudWatch Logs, Logs Insights, and Anomaly Detection provides a fully managed, scalable solution for centralized logging, real-time query capabilities, anomaly detection, and operational dashboards. This ensures microservices are monitored continuously, potential issues are detected early, and DevOps teams gain actionable insights, making it the correct solution.