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Q181
A developer is deploying a Lambda function that requires access to resources in a VPC. What must be configured?
A) Internet Gateway
B) VPC configuration with subnets and security groups
C) NAT Gateway only
D) VPC peering
Answer: B
Explanation:
This question addresses Lambda VPC integration for accessing private resources. Understanding VPC configuration helps developers securely connect Lambda to VPC resources. VPC configuration with subnets and security groups must be configured to enable Lambda functions to access resources in VPC like RDS databases, ElastiCache clusters, or internal services. When Lambda is configured for VPC access, AWS creates elastic network interfaces in specified subnets allowing function to communicate with VPC resources. Configuration involves selecting VPC where resources exist, choosing subnets for ENI placement typically private subnets across multiple availability zones for high availability, configuring security groups controlling inbound and outbound traffic for Lambda function, ensuring security groups allow necessary traffic to target resources, and verifying IAM execution role has VPC permissions. Lambda creates and manages ENIs automatically but initial creation causes cold start delays as ENIs are provisioned. Benefits include secure access to private resources without internet exposure, network-level isolation and security controls, ability to access internal APIs and databases, and compliance with security requirements for private connectivity. Common use cases include accessing RDS databases in private subnets, connecting to ElastiCache for caching, calling internal microservices, and accessing resources requiring network-level security. Best practices include using private subnets for Lambda ENIs, implementing least privilege security group rules, configuring VPC endpoints for AWS services like DynamoDB avoiding internet traffic, monitoring ENI creation in CloudWatch, planning for cold start latency from ENI creation, ensuring adequate IP addresses in subnets, and testing connectivity thoroughly. Organizations should balance VPC integration benefits against cold start impacts, use VPC endpoints where possible, and monitor network configurations. Internet Gateway is incorrect because while VPC needs internet access routes for external calls, gateway alone doesn’t configure Lambda VPC integration. NAT Gateway only is incorrect because NAT enables internet access from private subnets but Lambda requires subnet and security group configuration. VPC peering is incorrect because peering connects VPCs but doesn’t configure Lambda function for VPC access.
Q182
A developer needs to implement request throttling for different API consumers. Which API Gateway feature should be used?
A) Stage variables
B) Usage plans with API keys
C) Resource policies
D) Request validators
Answer: B
Explanation:
This question tests understanding of API Gateway rate limiting. Knowledge of usage plans helps developers implement fair resource allocation and monetization strategies. Usage plans with API keys should be used to implement request throttling for different API consumers, enabling granular control over access rates and quotas per customer or tier. Usage plans define throttle rate limits controlling requests per second, burst capacity allowing temporary spikes, and quotas limiting total requests over time periods. API keys identify consumers enabling attribution to usage plans and enforcement of associated limits. Configuration involves creating usage plans with specific throttle and quota settings, generating API keys for consumers, associating keys with plans, requiring API key in API methods, and distributing keys to consumers. Consumers include X-API-Key header in requests enabling identification and limit enforcement. When limits exceed, API Gateway returns 429 Too Many Requests responses. Benefits include fair resource distribution preventing monopolization, service tier implementation with different limits for free, paid, and premium users, abuse prevention through rate limiting, monetization enablement based on usage, and protection of backend services from overload. Common patterns include free tier with conservative limits encouraging upgrades, paid tiers with higher throughput, partner integrations with preferential limits, and internal usage with unlimited access. Best practices include setting realistic default limits protecting backends, implementing appropriate burst capacity for spikes, monitoring usage patterns adjusting limits, clearly communicating limits to consumers, providing upgrade paths for higher limits, implementing graceful responses when throttled, using CloudWatch metrics tracking throttling, and testing enforcement mechanisms. Organizations should design usage plans matching business models, monitor effectiveness, and adjust based on capacity and demand. Usage plans enable sophisticated API access control beyond simple authentication. Stage variables is incorrect because stage variables pass configuration values to integrations but don’t implement throttling. Resource policies is incorrect because policies control who can invoke APIs but don’t implement rate limiting per consumer. Request validators is incorrect because validators check request format but don’t control request rates.
Q183
A developer is building an application that needs to execute code closer to users for low latency. Which AWS service should be used?
A) Lambda in single region
B) Lambda@Edge
C) EC2 in single region
D) Elastic Beanstalk
Answer: B
Explanation:
This question addresses edge computing for latency optimization. Understanding Lambda@Edge helps developers improve application performance globally. Lambda@Edge should be used to execute code closer to users for low latency by running functions at CloudFront edge locations worldwide. Lambda@Edge enables running code in response to CloudFront events including viewer requests, viewer responses, origin requests, and origin responses. This allows customizing content and responses at edge locations nearest to users reducing latency significantly compared to centralized compute. Common use cases include modifying request headers for A/B testing, generating dynamic content like personalized HTML, implementing authentication at edge, rewriting URLs for SEO, resizing images on-demand, and making content decisions based on request characteristics. Configuration involves creating CloudFront distribution, developing Lambda function meeting Lambda@Edge constraints including runtime support, memory limits, and package size restrictions, publishing specific function version as Lambda@Edge doesn’t support $LATEST, and associating function with CloudFront distribution behavior and trigger event. Functions execute at edge locations processing requests or responses before reaching users. Benefits include significantly reduced latency through edge execution, improved user experience with faster responses, offloading origin servers reducing backend load, global reach leveraging CloudFront’s edge network, and ability to customize content per region. Constraints include function size limits smaller than standard Lambda, limited runtime support, no VPC access, restricted execution time, and read-only access to /tmp. Best practices include keeping functions small and efficient, understanding which trigger point fits use case, testing across regions validating global behavior, monitoring through CloudWatch Logs in edge regions, implementing proper error handling, caching function results when appropriate, and considering costs as Lambda@Edge pricing differs from standard Lambda. Organizations should identify use cases benefiting from edge execution, understand limitations, and test thoroughly across geographies. Lambda@Edge excels for latency-sensitive operations processing requests at edge. Lambda in single region is incorrect because centralized Lambda experiences higher latency for distant users lacking edge execution benefits. EC2 in single region is incorrect because single region doesn’t provide edge execution and managing global EC2 infrastructure is complex. Elastic Beanstalk is incorrect because it deploys to specific regions not edge locations.
Q184
A developer needs to ensure DynamoDB items are automatically deleted after a specific time. What feature should be configured?
A) DynamoDB Streams
B) Time to Live (TTL)
C) Lambda scheduled trigger
D) CloudWatch Events
Answer: B
Explanation:
This question tests knowledge of DynamoDB data lifecycle management. Understanding TTL helps developers automatically manage data retention. Time to Live (TTL) should be configured to automatically delete DynamoDB items after specific time, enabling expiration management without manual deletion or application code. TTL marks items for deletion based on timestamp attribute containing epoch time when item should expire. DynamoDB automatically deletes expired items in background typically within 48 hours of expiration time. This provides cost-effective data lifecycle management deleting obsolete data automatically. Configuration involves enabling TTL on table, specifying TTL attribute name containing expiration timestamps, and setting appropriate expiration times when creating or updating items. Items without TTL attribute or with expired timestamps are deletion candidates. DynamoDB processes expirations as background task not consuming provisioned throughput. Benefits include automatic cleanup of temporary data, cost reduction by removing unnecessary items, simplified application logic eliminating custom deletion code, no throughput consumption for deletions, and support for various use cases like session management, temporary records, and log retention. Common scenarios include user sessions expiring after inactivity, temporary storage of verification codes, time-limited access tokens, audit logs with retention periods, and cache entries with automatic expiration. Best practices include using epoch seconds format for TTL attributes, setting reasonable expiration buffers accounting for 48-hour deletion window, not relying on exact deletion timing, implementing backup strategies for critical data, monitoring deletion metrics through CloudWatch, documenting TTL usage patterns, and testing expiration behavior. Organizations should identify data with lifecycle requirements, implement TTL consistently, understand deletion timing characteristics, and consider DynamoDB Streams capturing deleted items if needed. TTL provides elegant solution for automatic data expiration without operational overhead. DynamoDB Streams is incorrect because Streams capture item changes enabling downstream processing but don’t automatically delete items. Lambda scheduled trigger is incorrect because while Lambda can implement deletion logic, it requires custom code, consumes compute resources, and can’t match TTL’s simplicity. CloudWatch Events is incorrect because events trigger actions but don’t provide built-in expiration management like TTL.
Q185
A developer is implementing blue/green deployment for a Lambda function. What feature enables traffic shifting between versions?
A) Lambda aliases
B) Lambda layers
C) Environment variables
D) Reserved concurrency
Answer: A
Explanation:
This question addresses Lambda deployment strategies. Understanding aliases helps developers implement safe deployment patterns with gradual rollout. Lambda aliases enable traffic shifting between versions allowing blue/green deployment where new version is deployed alongside existing version with traffic gradually shifted. Aliases are pointers to specific function versions supporting weighted traffic routing across two versions. This enables testing new versions with subset of traffic before full rollout, quick rollback if issues arise, and zero-downtime deployments. Configuration involves publishing new Lambda version creating immutable snapshot of function code and configuration, updating alias to point to new version, optionally configuring weighted routing specifying percentage to each version, monitoring metrics per version identifying issues, gradually increasing weight to new version as confidence grows, and rolling back by adjusting weights if problems occur. For example, alias could route 90% traffic to version 1 and 10% to version 2 initially, then shift to 100% version 2 after validation. Benefits include safe deployments with gradual rollout, quick rollback without redeployment, testing in production with real traffic, zero downtime during deployments, and automatic traffic distribution. Common pattern is “prod” alias used by API Gateway or event sources, with deployment updating alias to new version after testing. Best practices include using aliases consistently for production traffic, implementing comprehensive monitoring comparing versions, defining rollback criteria and procedures, automating deployment pipelines including traffic shifting, testing new versions thoroughly before production, documenting deployment procedures, starting with small traffic percentages, monitoring error rates and latency, and maintaining version history. Organizations should standardize deployment practices, implement automated testing, monitor closely during shifts, and establish rollback procedures. CodeDeploy integrates with Lambda supporting automated traffic shifting with configurable deployment types including linear, canary, and all-at-once. Lambda layers is incorrect because layers package dependencies and code shared across functions but don’t control traffic routing. Environment variables is incorrect because they configure function behavior but don’t enable version-based traffic shifting. Reserved concurrency is incorrect because it controls maximum concurrent executions but doesn’t route traffic between versions.
Q186
A developer needs to implement authentication for a REST API using existing corporate user directory. Which Cognito feature should be used?
A) Cognito User Pools
B) Cognito Identity Pools
C) Cognito User Pools with SAML federation
D) Cognito Sync
Answer: C
Explanation:
This question addresses enterprise authentication integration. Understanding Cognito federation helps developers connect existing identity providers. Cognito User Pools with SAML federation should be used to implement authentication using existing corporate user directory by integrating with enterprise identity providers like Active Directory Federation Services, Okta, or other SAML 2.0 compliant systems. SAML federation enables users to authenticate using corporate credentials without creating separate user accounts in Cognito. User Pool acts as service provider receiving SAML assertions from corporate identity provider validating user identity. Configuration involves creating Cognito User Pool, configuring SAML identity provider integration including metadata exchange, mapping SAML attributes to User Pool attributes, configuring application to use User Pool for authentication through hosted UI or SDK, and implementing authentication flows redirecting users to corporate login. When users access application, they’re redirected to corporate identity provider for authentication, then returned with SAML assertion that Cognito validates and converts to JWT tokens for application use. Benefits include leveraging existing user directory eliminating duplicate accounts, centralizing user management in corporate system, supporting existing authentication policies including MFA, simplifying user experience with single sign-on, maintaining compliance with corporate security requirements, and reducing administrative overhead. Common enterprise scenarios include internal applications using corporate credentials, partner applications federating with partner identity systems, and multi-organization platforms supporting various identity providers. Best practices include securing SAML metadata exchange, implementing proper attribute mapping ensuring necessary user information, testing authentication flows thoroughly, monitoring for authentication issues, implementing session management appropriately, documenting federation configuration, maintaining identity provider relationships, and planning for identity provider changes. Organizations should coordinate with identity teams, ensure identity providers support SAML 2.0, test user journeys end-to-end, and monitor authentication metrics. Cognito supports multiple identity providers enabling flexible authentication architectures. Cognito User Pools alone is incorrect because basic User Pools manage users directly without corporate directory integration. Cognito Identity Pools is incorrect because Identity Pools provide AWS credentials for authenticated users but don’t handle authentication themselves. Cognito Sync is incorrect because Sync synchronizes user data across devices but doesn’t provide authentication.
Q187
A developer needs to process messages from SQS queue in order. Which queue type should be used?
A) Standard queue
B) FIFO queue
C) Priority queue
D) Delay queue
Answer: B
Explanation:
This question tests understanding of SQS queue types. Knowledge of FIFO queues helps developers implement ordered message processing. FIFO (First-In-First-Out) queue should be used to process messages in order, guaranteeing messages are processed exactly once in the order they are sent. FIFO queues maintain strict ordering within message groups and ensure no message is processed more than once until deletion or visibility timeout expiration. Standard queues provide best-effort ordering and at-least-once delivery but can deliver messages out of order or multiple times. FIFO queues use message group IDs grouping related messages ensuring messages in same group are processed sequentially while messages in different groups can be processed in parallel. Configuration involves creating FIFO queue with .fifo suffix in name, sending messages with message group ID, optionally including message deduplication ID preventing duplicates, configuring consumers to process messages within visibility timeout, and deleting messages after successful processing. FIFO queues support up to 300 transactions per second in batch mode or 3000 with high throughput mode. Benefits include guaranteed ordering within message groups, exactly-once processing preventing duplicates, and simplified application logic not needing to handle ordering or deduplication. Common use cases include order processing ensuring sequence, financial transactions requiring order, command processing where order matters, and event sourcing maintaining event order. Limitations include lower throughput than standard queues and requirement for message group IDs. Best practices include designing appropriate message groups balancing ordering with parallelism, implementing idempotent processing for additional safety, using content-based deduplication reducing application complexity, monitoring queue metrics including message counts and ages, configuring appropriate visibility timeout for processing duration, and testing ordering behavior thoroughly. Organizations should identify scenarios requiring strict ordering, understand throughput implications, design message grouping strategies, and choose appropriate queue type for use cases. Standard queue is incorrect because standard queues provide at-least-once delivery and best-effort ordering not guaranteeing strict message order. Priority queue is incorrect because SQS doesn’t provide priority queues though priority can be simulated with multiple queues. Delay queue is incorrect because delay queues postpone message availability but don’t guarantee ordering.
Q188
A developer needs to trace requests across distributed microservices. Which AWS service should be used?
A) CloudWatch Logs
B) AWS X-Ray
C) CloudTrail
D) VPC Flow Logs
Answer: B
Explanation:
This question addresses distributed tracing in microservices. Understanding X-Ray helps developers debug and optimize complex distributed applications. AWS X-Ray should be used to trace requests across distributed microservices by collecting trace data showing request flow through various services, identifying bottlenecks, and analyzing performance. X-Ray provides end-to-end view of requests as they travel through application components including API Gateway, Lambda, ECS, EC2, and external HTTP services. This enables identifying performance issues, understanding dependencies, analyzing errors, and optimizing distributed systems. Implementation involves installing X-Ray SDK in application code, enabling X-Ray tracing on AWS services like Lambda and API Gateway, instrumenting custom code with segments and subsegments tracking specific operations, sampling trace data controlling collection volume, and analyzing traces through X-Ray console or API. X-Ray daemon collects trace data and sends to X-Ray service for aggregation and analysis. Benefits include visual service map showing architecture and dependencies, detailed trace information including latencies and errors, identification of bottlenecks and slow components, filtering traces by specific characteristics, and integration with CloudWatch alarms. Common use cases include debugging latency issues in microservices, understanding service dependencies, identifying error sources, optimizing performance, and analyzing request patterns. Best practices include implementing appropriate sampling reducing costs while maintaining visibility, adding custom segments for important operations, using annotations and metadata for filtering, correlating traces with logs for comprehensive debugging, monitoring trace data in production, analyzing service maps regularly, documenting performance baselines, and training teams on X-Ray usage. Organizations should standardize tracing across services, integrate into development workflow, use for production troubleshooting, and analyze regularly for optimization opportunities. X-Ray integrates with many AWS services providing automatic instrumentation. CloudWatch Logs is incorrect because while logs provide detailed information, they don’t automatically trace requests across distributed services or provide service maps. CloudTrail is incorrect because CloudTrail logs API calls for auditing but doesn’t trace application request flows. VPC Flow Logs is incorrect because flow logs capture network traffic metadata but don’t trace application-level requests.
Q189
A developer is building a serverless application that needs to send email notifications. Which AWS service should be used?
A) Amazon SES
B) Amazon SNS
C) Amazon SQS
D) Amazon EventBridge
Answer: A
Explanation:
This question tests knowledge of AWS communication services. Understanding SES helps developers implement email functionality. Amazon SES (Simple Email Service) should be used to send email notifications from serverless applications, providing scalable, cost-effective email sending with high deliverability. SES enables sending transactional emails, marketing communications, and notifications through SMTP interface or API. Unlike SNS which sends simple notifications, SES provides full-featured email platform supporting HTML content, attachments, templates, and delivery tracking. Implementation involves verifying email addresses or domains proving ownership, configuring SMTP credentials or using AWS SDK for API access, creating email templates for consistent messaging, sending emails through SDK calls from Lambda or other services, implementing bounceand complaint handling, monitoring sending metrics and reputation, and managing unsubscribe requests. SES provides sandbox environment for testing limiting sending to verified addresses until production access is requested. Benefits include high deliverability with optimized sending infrastructure, cost-effectiveness with pay-per-use pricing, scalability handling any volume, comprehensive sending statistics and metrics, template support for consistent emails, and integration with AWS services. Common use cases include transactional emails like order confirmations and password resets, marketing campaigns, system notifications and alerts, and automated reporting. Best practices include verifying domains for better deliverability, implementing proper email authentication with SPF and DKIM, handling bounces and complaints promptly maintaining reputation, using templates for consistency, monitoring sending metrics and deliverability, testing emails before production sending, implementing unsubscribe functionality for compliance, respecting email best practices, and staying within sending quotas. Organizations should plan for sandbox to production transition, maintain email reputation, comply with anti-spam laws, and monitor delivery carefully. SES provides enterprise-grade email platform well-suited for application-generated emails. Amazon SNS is incorrect because while SNS supports email as delivery protocol, it sends simple text notifications not formatted emails with HTML and attachments that SES provides. Amazon SQS is incorrect because SQS is message queue for application integration not email service. Amazon EventBridge is incorrect because EventBridge routes events between services but doesn’t send emails.
Q190
A developer needs to implement caching for API Gateway to reduce backend load. Where should caching be enabled?
A) Lambda function
B) API Gateway stage
C) CloudFront distribution
D) DynamoDB table
Answer: B
Explanation:
This question addresses API performance optimization. Understanding API Gateway caching helps developers reduce latency and backend load. API Gateway stage should have caching enabled to cache API responses reducing backend invocations and improving performance. API Gateway can cache method responses for specified time-to-live period, serving subsequent identical requests from cache without calling backend. This reduces Lambda invocations, decreases latency, lowers costs, and protects backends from load spikes. Caching is configured per stage with cache capacity from 0.5GB to 237GB affecting cost. Configuration involves enabling caching on stage, setting cache capacity and TTL, optionally configuring per-method cache settings overriding stage defaults, implementing cache key parameters determining which request parameters affect caching, and optionally encrypting cache data. Cache keys typically include query strings and request headers differentiating similar requests. When request arrives, API Gateway checks cache for matching response based on cache key. If cached entry exists and hasn’t expired, cached response returns immediately. Otherwise, backend is called, response is cached, and then returned. Benefits include reduced backend invocations lowering costs, improved response times for cached requests, protection against traffic spikes, and reduced backend scaling requirements. Common use cases include frequently accessed data changing infrequently, public content serving many users, computationally expensive operations, and rate-limited backends. Best practices include setting appropriate TTL balancing freshness with performance, carefully selecting cache key parameters, implementing cache invalidation for critical updates, monitoring cache hit rates through CloudWatch metrics, using per-method cache settings for granular control, considering costs as caching adds charges, testing cache behavior thoroughly, and documenting caching strategy. Organizations should identify cacheable endpoints, configure appropriately, monitor effectiveness, and balance costs with benefits. API Gateway caching provides simple performance optimization without backend changes. Lambda function is incorrect because while Lambda can implement caching internally, API Gateway caching avoids Lambda invocations entirely providing better performance and cost savings. CloudFront distribution is incorrect because CloudFront caches content at edge locations which is complementary but caching at API Gateway stage reduces backend calls. DynamoDB table is incorrect because DynamoDB can cache through DAX but doesn’t address API response caching.
Q191
A developer needs to deploy a Lambda function written in a language not natively supported by Lambda. What should be used?
A) Lambda layers
B) Custom runtime with Lambda
C) EC2 instead
D) Container on ECS
Answer: B
Explanation:
This question tests knowledge of Lambda runtime extensibility. Understanding custom runtimes helps developers use any programming language with Lambda. Custom runtime with Lambda should be used to deploy functions in languages not natively supported by AWS Lambda, enabling use of any programming language or specific language version. Lambda provides runtime API allowing custom runtimes to receive invocations and return responses. Custom runtimes can be built for any language compiling to Linux-compatible binaries. Implementation involves creating bootstrap file implementing runtime API protocol, packaging runtime with function code, using provided.al2 runtime in function configuration, and deploying as usual. Runtime bootstraps, polls for invocations, executes handler code, and returns responses. Benefits include language flexibility using preferred languages, control over runtime versions and dependencies, ability to use experimental or proprietary languages, and legacy code support without rewriting. Common use cases include specialized languages like Rust, specialized requirements needing specific runtime configurations, legacy applications in unsupported languages, and organizations standardizing on particular language ecosystems. AWS provides sample custom runtimes simplifying development. Best practices include thoroughly testing custom runtimes, implementing proper error handling, monitoring performance and cold starts, documenting runtime behavior, maintaining runtime updates, considering startup time impacts, implementing efficient initialization, and evaluating if native runtimes could work instead. Organizations should assess whether custom runtime necessity justifies additional complexity, document maintenance procedures, and test thoroughly. Lambda layers can package custom runtimes sharing across functions. Custom runtimes enable Lambda’s serverless benefits with any language. Lambda layers is incorrect because while layers can include custom runtimes, the answer needs specifying custom runtime capability. EC2 instead is incorrect because EC2 would work but loses Lambda’s serverless benefits like automatic scaling and per-invocation billing. Container on ECS is incorrect because containerization works but Lambda with custom runtime provides simpler serverless approach.
Q192
A developer is implementing error handling for asynchronous Lambda invocations. Where should failed events be sent?
A) CloudWatch Logs
B) Dead letter queue
C) S3 bucket
D) SNS topic
Answer: B
Explanation:
This question addresses Lambda error handling for asynchronous invocations. Understanding dead letter queues helps developers capture and process failed events. Dead letter queue should be configured to receive failed events from asynchronous Lambda invocations, enabling error capture, investigation, and reprocessing. When Lambda function fails after maximum retry attempts for asynchronous invocations from services like S3, SNS, or EventBridge, events can be sent to SQS queue or SNS topic configured as dead letter queue. This prevents event loss and enables dedicated error handling. Configuration involves creating SQS queue or SNS topic for dead letter processing, configuring Lambda function with dead letter queue ARN, granting Lambda permission to send to queue, implementing monitoring for dead letter queue depth, and processing failed events appropriately. Lambda retries failed asynchronous invocations twice with delays before sending to DLQ if still failing. Benefits include capture of failed events for analysis, prevention of permanent event loss, dedicated error handling workflow, investigation capability examining failed events, and reprocessing opportunity after fixing issues. Common failure causes include function errors, timeout expiration, permission issues, and resource constraints. Best practices include monitoring DLQ depth alerting on buildup, implementing automated or manual error analysis, fixing underlying issues causing failures, reprocessing events after fixes, logging sufficient context for debugging, setting appropriate retention on DLQ, documenting error handling procedures, and testing failure scenarios. Organizations should implement comprehensive error handling, monitor proactively, investigate failures promptly, and establish escalation procedures. Dead letter queues are essential for production reliability capturing events that would otherwise be lost. CloudWatch Logs is incorrect because while errors are logged, logs don’t capture events for reprocessing after failures. S3 bucket is incorrect because S3 can store events but isn’t the native Lambda DLQ mechanism. SNS topic is incorrect because while SNS can be DLQ destination, SQS queue is more common for reprocessing workflows and the question asks where events should be sent generally, meaning DLQ concept.
Q193
A developer needs to implement blue/green deployment for containerized application. Which AWS service provides this capability?
A) Elastic Beanstalk
B) ECS with CodeDeploy
C) Lambda
D) EC2 Auto Scaling
Answer: B
Explanation:
This question addresses deployment strategies for containers. Understanding ECS with CodeDeploy helps developers implement safe container deployments. ECS with CodeDeploy provides blue/green deployment capability for containerized applications enabling zero-downtime deployments with automatic traffic shifting and rollback. CodeDeploy integrates with ECS managing deployment process including standing up new task set with updated containers, gradually shifting load balancer traffic from old to new tasks, monitoring deployment health, and automatically rolling back if issues detected. This provides production-safe deployment pattern. Configuration involves defining ECS service using CodeDeploy as deployment controller, creating CodeDeploy application and deployment group, specifying deployment configuration controlling traffic shift timing like linear, canary, or all-at-once, configuring alarms triggering automatic rollback, and initiating deployments through CodeDeploy. Load balancer routes traffic to active task set during deployment. Benefits include zero-downtime deployments maintaining service availability, gradual traffic shifting enabling early issue detection, automatic rollback on failures, testing in production with real traffic, and simplified deployment management. Common patterns include deploying new versions alongside existing with gradual cutover, testing with subset of traffic before full rollout, and quick rollback if issues arise. Best practices include implementing comprehensive health checks detecting issues early, defining clear rollback criteria and alarms, starting with small traffic percentages, monitoring metrics during deployment, automating deployment pipeline including testing, documenting deployment procedures, testing rollback scenarios, and maintaining deployment history. Organizations should standardize deployment strategies, automate thoroughly, monitor closely during deployments, and establish deployment policies. CodeDeploy supports sophisticated deployment patterns with ECS. Elastic Beanstalk is incorrect because while Beanstalk supports deployment strategies, question specifically asks for containerized applications where ECS with CodeDeploy is more appropriate. Lambda is incorrect because Lambda is serverless compute not containerized application platform. EC2 Auto Scaling is incorrect because Auto Scaling manages instance counts but doesn’t provide blue/green deployment for containers.
Q194
A developer needs to process large files uploaded to S3. The processing takes variable time up to 2 hours. What is the BEST solution?
A) Lambda function with maximum timeout
B) Step Functions coordinating multiple Lambdas
C) ECS task triggered by S3 event
D) EC2 instance with cron job
Answer: C
Explanation:
This question addresses selecting compute for long-running file processing. Understanding service limitations helps developers choose appropriate solutions. ECS task triggered by S3 event is the best solution for processing large files with variable duration up to 2 hours as Lambda has 15-minute maximum timeout insufficient for 2-hour processing. ECS (Elastic Container Service) runs Docker containers without time limits supporting hours-long or continuous processing with full control over execution environment. For file processing, ECS provides appropriate platform. Implementation involves containerizing processing application with dependencies, creating ECS task definition with resource requirements, setting up ECS cluster on EC2 or Fargate, configuring S3 event notification triggering Lambda or EventBridge, Lambda or EventBridge starting ECS task with S3 event details, ECS task processing file and storing results, and task terminating after completion. Benefits include no time limits supporting any duration, flexible resource allocation with appropriate CPU and memory, container portability, cost-effectiveness for long tasks, and full runtime control. Architecture typically has S3 event triggering Lambda that validates file and starts ECS task, with task processing independently. Fargate simplifies by managing infrastructure. Best practices include right-sizing task resources, implementing progress tracking for long processes, using Spot instances for cost optimization when timing flexibility exists, monitoring task execution, implementing proper error handling, cleaning up after completion, considering batch processing for multiple files, and documenting processing requirements. Organizations should select compute matching workload characteristics understanding service limits and costs. Lambda excels for short event-driven processing but long-running work needs different solutions. Lambda function with maximum timeout is incorrect because Lambda maximum is 15 minutes insufficient for 2-hour processing. Step Functions coordinating multiple Lambdas is incorrect because while Step Functions orchestrates workflows, splitting 2-hour processing across Lambdas may not be feasible depending on process nature. EC2 instance with cron job is incorrect because scheduled jobs don’t respond to events in real-time and cron-based processing adds latency.
Q195
A developer is implementing a REST API that needs to return different response formats based on Accept header. Which API Gateway feature should be used?
A) Request mapping
B) Response mapping
C) Content negotiation
D) Request validation
Answer: C
Explanation:
This question addresses API Gateway content negotiation. Understanding content negotiation helps developers implement flexible APIs supporting multiple response formats. Content negotiation should be used in API Gateway to return different response formats based on Accept header allowing clients to request preferred format like JSON or XML. API Gateway examines Accept header in requests and returns appropriately formatted response matching client preferences. This implements standard HTTP content negotiation enabling same endpoint to serve multiple formats. Configuration involves defining multiple response models for different content types, implementing backend integration returning data in flexible format, configuring response mapping templates for each content type transforming backend responses, associating templates with content types, and testing with different Accept headers. When client requests with Accept: application/json, JSON template applies; with Accept: application/xml, XML template applies. Benefits include API flexibility serving multiple client types, backward compatibility supporting legacy formats, client choice allowing optimal format, and standards compliance following HTTP specifications. Common scenarios include APIs serving both web browsers preferring JSON and legacy systems requiring XML, mobile apps optimizing for compact formats, and integration platforms needing specific formats. Best practices include supporting common formats clients need, implementing default format for unspecified Accept headers, testing with various content types, documenting supported formats, considering performance implications of transformations, validating template syntax, and monitoring format usage. Organizations should identify required formats, implement efficiently, document clearly, and maintain as API evolves. API Gateway content negotiation provides standard approach to multiple format support. Request mapping is incorrect because request mapping transforms incoming requests but doesn’t handle response format selection. Response mapping is incorrect because while response templates are involved, content negotiation is the specific feature selecting templates based on Accept header. Request validation is incorrect because validation checks request format but doesn’t determine response format.
Q196
A developer needs to share common code across multiple Lambda functions. What is the BEST approach?
A) Copy code into each function
B) Lambda layers
C) S3 bucket with code
D) Shared VPC endpoint
Answer: B
Explanation:
This question tests knowledge of Lambda code sharing mechanisms. Understanding Lambda layers helps developers manage shared dependencies efficiently. Lambda layers is the best approach to share common code across multiple Lambda functions enabling centralized management of shared libraries, dependencies, and custom runtime code. Layers are ZIP archives containing libraries, custom runtimes, or other function dependencies that can be included in Lambda functions without packaging in deployment package. Functions can reference up to five layers, with layers providing shared code, SDKs, custom runtimes, and configuration. Implementation involves creating layer ZIP with contents in specific directory structure, publishing layer with compatible runtimes, adding layer to functions needing shared code, accessing layer contents from function code, and versioning layers for controlled updates. Layers extract to /opt directory in function execution environment. Benefits include reduced deployment package size, shared dependency management updating once benefiting all functions, faster deployments uploading only function code, separation of concerns between business logic and dependencies, and centralized version control for shared code. Common use cases include sharing utility libraries, custom logging frameworks, database connectors, third-party SDKs, and custom runtimes. Best practices include organizing layers by purpose, versioning layers for compatibility control, documenting layer contents and usage, testing layer updates before production, limiting layer count per function for manageable dependencies, using specific layer versions for stability, maintaining layer permissions appropriately, and monitoring layer usage. Organizations should standardize layers across teams, implement governance for layer publishing, maintain documentation, and automate layer deployment. Layers simplify dependency management in serverless architectures. Copy code into each function is incorrect because duplicating code is maintenance nightmare requiring updates across all functions and increasing deployment sizes. S3 bucket with code is incorrect because while functions could download from S3, it adds complexity, latency, and doesn’t provide the clean integration layers offer. Shared VPC endpoint is incorrect because VPC endpoints provide network connectivity not code sharing.
Q197
A developer needs to deploy a web application to AWS Elastic Beanstalk. The application requires specific environment variables to be set during deployment. Which file should the developer use to define these environment variables?
A)yml
B)yml
C) .ebextensions config file
D) Dockerfile
Answer: C
Explanation:
When deploying applications to AWS Elastic Beanstalk, developers often need to configure environment-specific settings such as environment variables, which are essential for application functionality.
The .ebextensions configuration files are specifically designed for customizing Elastic Beanstalk environments. These files are placed in a folder named .ebextensions at the root of your application source bundle. They use YAML or JSON format and allow you to set environment variables, install packages, run commands, and configure various aspects of the Elastic Beanstalk environment. Environment variables can be defined using the option_settings key in these configuration files.
The appspec.yml file is used with AWS CodeDeploy for defining deployment actions, not with Elastic Beanstalk. It specifies how CodeDeploy should deploy your application to EC2 instances or on-premises servers.
The buildspec.yml file is used with AWS CodeBuild to define build commands and settings for continuous integration pipelines. While it can define environment variables for the build process, it does not configure environment variables for the Elastic Beanstalk runtime environment.
A Dockerfile is used to define container images for Docker-based deployments. While Elastic Beanstalk supports Docker containers, the Dockerfile itself is not the primary method for setting Elastic Beanstalk environment variables.
Additionally, environment variables can also be set through the Elastic Beanstalk console or CLI, but for version-controlled, repeatable deployments, using .ebextensions configuration files is the recommended approach as it keeps configuration as code.
Q198
A developer is using Amazon DynamoDB for a mobile application backend. The application requires the ability to retrieve multiple items from different tables in a single request to minimize latency. Which DynamoDB API operation should be used?
A) GetItem
B) BatchGetItem
C) Query
D) Scan
Answer: B
Explanation:
When working with DynamoDB in applications that require efficient data retrieval across multiple items or tables, selecting the appropriate API operation is essential for optimizing performance and reducing latency.
BatchGetItem is the correct API operation for retrieving multiple items from one or more tables in a single request. It allows you to specify up to 100 items across multiple tables, with each item identified by its primary key. This operation significantly reduces the number of network round trips compared to making individual GetItem calls, which is particularly beneficial for mobile applications where network latency can impact user experience. BatchGetItem returns the items in an unordered fashion and can retrieve up to 16 MB of data per request.
GetItem retrieves a single item from a table based on its primary key. While efficient for single-item retrieval, it would require multiple API calls to fetch multiple items, increasing latency and potentially impacting application performance.
Query is used to retrieve items with the same partition key value, optionally filtered by sort key conditions. It operates on a single table and is designed for retrieving related items that share a partition key, not for retrieving arbitrary items across multiple tables.
Scan examines every item in a table and is the least efficient operation for targeted item retrieval. It’s typically used when you need to retrieve all items or when you don’t know the primary keys in advance.
For mobile applications, BatchGetItem provides optimal performance by reducing network overhead, supporting parallel processing of requests, and allowing retrieval from multiple tables simultaneously, making it ideal for scenarios requiring aggregated data from different sources.
Q199
A company is developing a REST API using Amazon API Gateway and AWS Lambda. The API needs to support CORS (Cross-Origin Resource Sharing) to allow web applications from different domains to access it. What must the developer configure?
A) Enable CORS in the Lambda function code only
B) Enable CORS in API Gateway and configure appropriate headers
C) Configure an Application Load Balancer with CORS rules
D) Use AWS WAF to allow cross-origin requests
Answer: B
Explanation:
Cross-Origin Resource Sharing (CORS) is a security feature implemented by web browsers to control how web applications from one domain can interact with resources from another domain. Proper CORS configuration is essential for REST APIs accessed by browser-based applications.
Enabling CORS in API Gateway and configuring appropriate headers is the correct approach. API Gateway provides built-in CORS support that can be enabled directly through the console or API. When enabled, API Gateway automatically handles preflight OPTIONS requests and adds necessary CORS headers to responses, including Access-Control-Allow-Origin, Access-Control-Allow-Methods, and Access-Control-Allow-Headers. This configuration ensures that browsers permit cross-origin requests from specified domains.
While enabling CORS in the Lambda function code alone might add CORS headers to responses, it does not handle preflight OPTIONS requests that browsers send before actual requests. API Gateway must be configured to respond to these preflight requests properly, which requires CORS configuration at the gateway level.
An Application Load Balancer is not typically used in front of API Gateway for REST APIs, and while ALBs can handle CORS headers, this adds unnecessary complexity when API Gateway already provides native CORS support.
AWS WAF is a web application firewall used for protecting APIs from common web exploits and attacks. It is not designed to handle CORS configuration, which is a different concern related to browser security policies.
For production deployments, developers should specify exact origins rather than using wildcards, configure only necessary HTTP methods, and ensure that credentials are handled securely when Access-Control-Allow-Credentials is enabled.
Q200
A developer is implementing a CI/CD pipeline using AWS CodePipeline. The pipeline needs to automatically deploy code changes to a staging environment after successful build and testing. Which AWS service should be used for the deployment stage?
A) AWS CodeCommit
B) AWS CodeBuild
C) AWS CodeDeploy
D) AWS CodeStar
Answer: C
Explanation:
Building effective continuous integration and continuous deployment pipelines requires understanding the specific roles of different AWS developer tools and how they integrate to automate the software release process.
AWS CodeDeploy is the correct service for the deployment stage of a CI/CD pipeline. It is specifically designed to automate application deployments to various compute services including Amazon EC2 instances, AWS Lambda functions, Amazon ECS services, and on-premises servers. CodeDeploy handles the complexities of updating applications, allows for different deployment strategies such as in-place, blue/green, and canary deployments, and provides rollback capabilities if issues are detected during deployment.
AWS CodeCommit is a source control service that hosts Git repositories. It serves as the source stage in a pipeline where code is stored and versioned, but it does not perform deployment operations.
AWS CodeBuild is a fully managed build service that compiles source code, runs tests, and produces deployable artifacts. It is used in the build and test stages of a pipeline but does not deploy applications to target environments.
AWS CodeStar is a unified user interface for managing software development activities. It provides project templates and integrates various developer tools but is not itself a deployment service. CodeStar can create pipelines that use CodeDeploy, but it is not the deployment mechanism.
In a typical CodePipeline workflow, CodeCommit or another repository stores source code, CodeBuild compiles and tests it, and CodeDeploy deploys the artifacts to staging and production environments. This separation of concerns allows for flexible, scalable, and maintainable CI/CD workflows.
