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Question 61
A developer needs to deploy a serverless application that processes uploaded images. Which AWS service combination is most appropriate?
A) EC2 + EBS
B) S3 + Lambda + DynamoDB
C) ECS + RDS
D) Elastic Beanstalk + Aurora
Answer: B
Explanation:
Serverless architectures eliminate server management overhead. S3 + Lambda + DynamoDB provides complete serverless solution where S3 stores uploaded images, Lambda processes them automatically, DynamoDB stores metadata, eliminates server provisioning, scales automatically, and represents cost-effective serverless pattern.
S3 event notifications trigger Lambda functions when images upload, Lambda executes processing code in response, DynamoDB stores processing results and metadata, all services scaling automatically based on demand, and requiring no infrastructure management.
Architecture benefits include automatic scaling handling variable workloads, pay-per-use pricing reducing costs, no server management eliminating operational overhead, high availability built into services, and rapid development cycles.
S3 integration provides object storage for images, event notifications triggering processing, versioning for safety, lifecycle policies for cost optimization, and secure access control.
Lambda advantages show event-driven execution responding to S3 events, automatic scaling for concurrent uploads, millisecond billing for cost efficiency, integrated monitoring through CloudWatch, and support for multiple programming languages.
DynamoDB benefits include serverless NoSQL database, automatic scaling, single-digit millisecond latency, DAX for caching, and integration with Lambda.
Common patterns involve image thumbnail generation, format conversion, metadata extraction, facial recognition, and content moderation using managed AI services.
Implementation considerations require configuring S3 event notifications, granting Lambda S3 read permissions, writing processing logic, storing results in DynamoDB, and monitoring execution.
Best practices recommend using S3 lifecycle policies, implementing error handling in Lambda, enabling DynamoDB auto-scaling, monitoring with CloudWatch, using environment variables, and implementing least privilege IAM.
Why other options are incorrect:
A) EC2 + EBS requires server management, manual scaling, higher operational overhead, not serverless, and doesn’t align with serverless requirement.
B) ECS requires container orchestration, more complex than needed, not fully serverless, and adds unnecessary infrastructure management.
D) Elastic Beanstalk manages infrastructure but not serverless, Aurora requires instance sizing, and both involve more management than pure serverless options.
Question 62
A developer must implement application configuration that changes without redeployment. Which AWS service should be used?
A) EC2 user data
B) Systems Manager Parameter Store
C) CloudFormation parameters
D) Hard-coded configuration files
Answer: B
Explanation:
Dynamic configuration management requires centralized parameter storage. Systems Manager Parameter Store provides centralized configuration storage, enables runtime parameter retrieval, supports versioning and change tracking, integrates with IAM for access control, offers encryption for sensitive data, and enables configuration changes without application redeployment.
Parameter Store stores configuration data and secrets, supports hierarchical organization, provides version history, encrypts sensitive parameters with KMS, enables cross-account and cross-region access, and integrates seamlessly with AWS services.
Configuration patterns include database connection strings, API endpoints, feature flags, application settings, and secrets management with secure strings.
Access methods show applications retrieving parameters via AWS SDK, Lambda functions accessing at runtime, EC2 instances pulling on startup, ECS tasks loading from Parameter Store, and CLI access for management.
Security features include encryption using AWS KMS, IAM policies controlling access, parameter policies for advanced control, audit trail via CloudTrail, and secure string type for secrets.
Version management maintains parameter history, enables rollback to previous versions, tracks changes over time, supports parameter policies, and facilitates auditing.
Cost optimization offers free tier for standard parameters, charges for advanced parameters with higher throughput, and provides cost-effective solution compared to third-party tools.
Integration benefits work with CloudFormation for dynamic references, Lambda environment variables, ECS task definitions, CodePipeline for CI/CD, and other AWS services.
Best practices recommend organizing parameters hierarchically, using secure strings for sensitive data, implementing least privilege access, enabling notifications for changes, versioning important parameters, and documenting parameter purposes.
Why other options are incorrect:
A) EC2 user data runs at instance launch only, requires instance restart for changes, doesn’t support dynamic updates, and limited to EC2 instances.
C) CloudFormation parameters are deployment-time values, require stack updates to change, involve downtime or complex updates, and not designed for runtime configuration.
D) Hard-coded configuration files require redeployment, lack centralized management, no encryption support, difficult to update across environments, and violate best practices.
Question 63
A developer needs to implement distributed tracing for a microservices application. Which AWS service provides this capability?
A) CloudWatch Logs
B) X-Ray
C) CloudTrail
D) VPC Flow Logs
Answer: B
Explanation:
Microservices observability requires end-to-end request tracking. X-Ray provides distributed tracing analyzing application behavior, visualizes service maps, identifies performance bottlenecks, traces requests across services, debugs distributed applications, and offers comprehensive insights into application architecture.
X-Ray collects trace data from applications, creates service graphs showing dependencies, identifies errors and latency issues, provides detailed trace information, analyzes performance bottlenecks, and enables root cause analysis.
Tracing components include trace segments representing work done, subsegments for granular operations, annotations for searchable data, metadata for additional context, and sampling rules controlling data collection.
Service map visualizes application architecture automatically, shows service dependencies, displays latency distribution, indicates error rates per service, identifies problematic components, and updates in real-time.
Integration support includes automatic instrumentation for Lambda, SDK integration for custom applications, support for various languages and frameworks, integration with ALB and API Gateway, and ECS and EKS compatibility.
Performance analysis enables identifying slow operations, analyzing latency distribution, finding inefficient code paths, detecting service dependencies, and optimizing application performance.
Debugging capabilities provide request-level details, exception tracking, custom annotations for business context, filtering traces by criteria, and analyzing specific error patterns.
Implementation steps involve installing X-Ray daemon or using built-in support, instrumenting application code, configuring sampling rules, granting necessary permissions, and analyzing traces in console.
Best practices recommend using sampling to control costs, adding meaningful annotations, implementing exception handling, monitoring service maps, setting alarms for latency, and documenting trace analysis procedures.
Why other options are incorrect:
A) CloudWatch Logs collects log data, doesn’t provide distributed tracing, lacks service map visualization, doesn’t correlate requests across services, and serves different monitoring purpose.
C) CloudTrail audits API calls, tracks user activity, not application tracing, doesn’t analyze performance, and focuses on governance not application monitoring.
D) VPC Flow Logs capture network traffic, monitor IP communications, don’t trace application requests, operate at network layer, and serve security and network analysis purposes.
Question 64
A developer must implement blue-green deployment for a web application. Which AWS service simplifies this deployment strategy?
A) CloudFormation
B) Elastic Beanstalk
C) CodeDeploy
D) OpsWorks
Answer: C
Explanation:
Advanced deployment strategies require specialized tools. CodeDeploy provides automated deployment supporting blue-green strategy, manages traffic shifting between environments, enables instant rollback, minimizes downtime, integrates with load balancers, and offers production-grade deployment control.
CodeDeploy orchestrates application deployments, supports various deployment types, manages traffic routing, automates rollback on failures, integrates with CI/CD pipelines, and provides deployment health monitoring.
Blue-green deployment involves running two identical environments (blue=current, green=new), deploying to green environment, testing thoroughly, shifting traffic from blue to green, maintaining blue for quick rollback, and minimizing deployment risk.
Traffic shifting methods include all-at-once switching instantly, canary deploying to subset gradually, linear increasing percentage over time, and custom configurations for specific requirements.
Integration capabilities work with EC2 instances using in-place deployments, Lambda functions with traffic shifting, ECS services managing container deployments, on-premises servers for hybrid scenarios, and load balancers for traffic management.
Deployment controls provide automatic rollback on failures, manual approval gates for production, deployment groups organizing targets, lifecycle hooks for custom actions, and CloudWatch alarms triggering rollbacks.
Health monitoring tracks deployment success, monitors application metrics, integrates with CloudWatch, validates instance health, and triggers automatic rollbacks when issues detected.
Implementation steps include creating deployment application, defining deployment groups, configuring deployment strategy, specifying application revision, and monitoring deployment progress.
Best practices recommend testing in staging first, implementing gradual rollouts, monitoring health metrics, maintaining rollback plans, automating deployments, and documenting deployment procedures.
Why other options are incorrect:
A) CloudFormation manages infrastructure, supports blue-green at infrastructure level, requires manual orchestration, doesn’t provide automated traffic shifting, and serves infrastructure-as-code purpose.
B) Elastic Beanstalk supports deployment strategies, provides managed environment, but CodeDeploy offers more granular control, and Beanstalk focuses on platform management.
D) OpsWorks provides configuration management, uses Chef/Puppet, supports deployments, but CodeDeploy specialized for deployment strategies and offers simpler blue-green implementation.
Question 65
A developer needs to store session data for a web application across multiple EC2 instances. Which AWS service is most appropriate?
A) Instance Store
B) EBS
C) ElastiCache
D) S3
Answer: C
Explanation:
Distributed session management requires shared storage accessible across instances. ElastiCache provides in-memory data store for session management, offers microsecond latency, supports distributed caching, enables session sharing across instances, scales horizontally, and represents optimal solution for session storage.
ElastiCache supports Redis and Memcached engines, provides high-performance caching, maintains session consistency, enables automatic failover, offers data persistence with Redis, and integrates seamlessly with applications.
Session storage benefits include extremely low latency for reads/writes, horizontal scaling for high throughput, automatic data replication, persistence options with Redis, and TTL support for session expiration.
Redis advantages provide data persistence surviving restarts, automatic failover for high availability, backup and restore capabilities, complex data structures, and pub/sub messaging.
Memcached benefits show simpler architecture, horizontal scaling through sharding, multi-threaded performance, and appropriate for simple key-value caching.
Implementation pattern involves configuring session store in application, connecting to ElastiCache endpoint, storing session data on user requests, retrieving session across instances, and managing session lifecycle.
High availability uses Multi-AZ deployments, automatic failover detection, read replicas for scaling, cluster mode for Redis, and consistent hashing for Memcached.
Security features include VPC deployment, encryption in-transit and at-rest, IAM authentication for Redis, security groups controlling access, and compliance certifications.
Best practices recommend using Redis for persistence needs, implementing connection pooling, setting appropriate TTLs, monitoring cache metrics, sizing clusters appropriately, and testing failover scenarios.
Why other options are incorrect:
A) Instance Store is ephemeral, lost on instance stop, local to single instance, doesn’t share across instances, and inappropriate for session storage.
B) EBS attaches to single instance, doesn’t support concurrent multi-instance access, higher latency than in-memory, and not designed for session sharing.
D) S3 has higher latency, designed for object storage not session data, more expensive for frequent operations, eventual consistency limitations, and sub-optimal for real-time session management.
Question 66
A developer must implement API rate limiting and caching. Which AWS service provides these capabilities?
A) CloudFront
B) API Gateway
C) Application Load Balancer
D) Route 53
Answer: B
Explanation:
API management requires throttling and performance optimization. API Gateway provides comprehensive API management including request throttling, response caching, authentication, authorization, API versioning, and request/response transformation.
API Gateway manages API lifecycle, controls access, implements usage plans with rate limiting, caches responses reducing backend load, monitors API calls, and integrates with Lambda, EC2, and other backends.
Throttling capabilities include default rate limits protecting backend, burst handling for temporary spikes, usage plans setting per-client limits, API keys identifying consumers, and automatic throttling response (429 Too Many Requests).
Caching features reduce backend calls, improve response times, cache responses by TTL, invalidate cache manually, encrypt cached data, and configure per-stage basis.
Rate limiting implementation defines throttle limits per second/minute, creates usage plans for different tiers, associates API keys with plans, enforces limits automatically, and monitors usage through CloudWatch.
Integration patterns connect to Lambda for serverless backends, HTTP endpoints for existing services, AWS services directly, VPC links for private resources, and mock integrations for development.
Security features include IAM authentication, Cognito user pools, Lambda authorizers for custom logic, API keys for identification, resource policies for fine-grained control, and WAF integration.
Deployment stages support multiple environments, gradual rollouts, canary deployments, stage variables for configuration, and separate settings per stage.
Best practices recommend implementing throttling on all APIs, caching frequently accessed data, using usage plans for different customer tiers, monitoring CloudWatch metrics, securing with authorizers, and versioning APIs properly.
Why other options are incorrect:
A) CloudFront provides CDN caching for static content, DDoS protection, but doesn’t offer API throttling, no usage plans, and focuses on content delivery not API management.
C) Application Load Balancer routes traffic, provides basic request routing, no caching capabilities, no API throttling, and serves load balancing purpose not API management.
D) Route 53 is DNS service, provides routing policies, health checks, but no API throttling or caching, and operates at different layer.
Question 67
A developer needs to ensure DynamoDB queries are efficient and cost-effective. Which design principle should be followed?
A) Use scan operations for all queries
B) Design tables with appropriate partition keys
C) Store all data in a single table attribute
D) Avoid secondary indexes
Answer: B
Explanation:
DynamoDB performance depends on proper data modeling. Designing tables with appropriate partition keys ensures even data distribution, enables efficient queries, prevents hot partitions, optimizes read/write performance, reduces costs, and represents fundamental DynamoDB best practice.
Partition key selection determines data distribution across partitions, affects query efficiency, influences cost, prevents throttling from hot partitions, and requires careful planning based on access patterns.
Key design principles include choosing high-cardinality partition keys, avoiding hot keys from uneven access, understanding access patterns before design, using composite keys when needed, and considering LSI/GSI for alternative access patterns.
Partition key characteristics should have many distinct values, distribute requests evenly, enable Query operations, support application access patterns, and avoid sequential patterns causing hotspots.
Sort key benefits enable range queries, store related items together, support one-to-many relationships, allow flexible querying within partition, and optimize for access patterns.
Secondary indexes provide alternative query patterns, GSI enables different partition/sort keys, LSI uses same partition key with different sort key, but adds cost and complexity requiring justification.
Anti-patterns include using scan instead of query, low-cardinality partition keys, sequential keys like timestamps as partition key, storing unrelated data together, and ignoring access patterns.
Query optimization involves using Query instead of Scan operations, projecting only needed attributes, implementing pagination, using consistent reads only when necessary, and batch operations where appropriate.
Best practices recommend understanding access patterns first, using single-table design when appropriate, denormalizing data for performance, monitoring CloudWatch metrics, enabling auto-scaling, and testing with production-like data.
Why other options are incorrect:
A) Scan operations read entire table, extremely inefficient, costly at scale, should be avoided, and Query operations with proper keys far superior.
C) Storing all data in single attribute limits query capabilities, prevents efficient access, complicates data retrieval, violates design principles, and causes maintenance issues.
D) Avoiding secondary indexes limits query flexibility, may force inefficient scans, appropriate indexes improve performance, though should be used judiciously considering cost.
Question 68
A developer must implement asynchronous message processing between microservices. Which AWS service provides reliable message queuing?
A) SNS
B) SQS
C) Kinesis
D) EventBridge
Answer: B
Explanation:
Asynchronous communication patterns require reliable queuing. SQS (Simple Queue Service) provides fully managed message queuing service, ensures reliable message delivery, decouples microservices, supports distributed systems, scales automatically, and enables asynchronous processing patterns.
SQS stores messages reliably, guarantees at-least-once delivery, supports message ordering with FIFO queues, enables delayed processing, implements visibility timeout, and provides dead-letter queues for error handling.
Queue types include Standard queues offering unlimited throughput and at-least-once delivery with possible duplicates, and FIFO queues providing exactly-once processing with strict ordering.
Messaging patterns show producers sending messages asynchronously, consumers polling for messages, processing independently, deleting after successful processing, and achieving loose coupling.
Visibility timeout prevents multiple consumers processing same message, gives processing time, automatically returns message if not deleted, configurable per queue, and ensures reliable processing.
Dead-letter queues capture failed messages after max receive count, enable error analysis, prevent message loss, facilitate debugging, and improve reliability.
Integration capabilities work with Lambda for serverless processing, EC2/ECS for container-based consumers, EventBridge for event routing, SNS for fan-out patterns, and other AWS services.
Scalability features include automatic scaling, no throughput limits for Standard queues, message batching for efficiency, long polling reducing empty receives, and cost-effective at any scale.
Best practices recommend using FIFO when ordering critical, implementing idempotent processing, setting appropriate visibility timeouts, configuring dead-letter queues, enabling long polling, monitoring queue metrics, and handling duplicates gracefully.
Why other options are incorrect:
A) SNS is pub/sub notification service, pushes messages to subscribers, fan-out pattern, not queuing with consumer polling, and serves different messaging pattern.
C) Kinesis handles streaming data, real-time processing, ordered shard-based delivery, more complex than needed for simple queuing, and optimized for high-throughput streaming.
D) EventBridge routes events between services, schema registry, event matching, more complex event routing, and SQS simpler for basic message queuing.
Question 69
A developer needs to execute code in response to S3 object creation without managing servers. Which AWS service should be used?
A) EC2
B) ECS
C) Lambda
D) Elastic Beanstalk
Answer: C
Explanation:
Event-driven serverless computing requires function-as-a-service capability. Lambda executes code in response to events without server management, integrates with S3 events natively, scales automatically, bills per execution, supports multiple languages, and represents ideal solution for event-driven processing.
Lambda functions triggered by S3 events execute automatically when objects created, modified, or deleted, process events asynchronously, scale to thousands of concurrent executions, and require zero infrastructure management.
S3 event integration configures notifications triggering Lambda, passes object metadata to function, supports filtering by prefix/suffix, enables multiple functions per event, and processes in near real-time.
Execution model shows S3 invoking Lambda on events, Lambda scaling automatically, executing handler function, processing object data, and completing without managing infrastructure.
Use cases include image thumbnail generation, data transformation, file validation, metadata extraction, triggering workflows, and data archiving.
Concurrency management provides automatic scaling, configurable reserved concurrency, provisioned concurrency for predictable latency, and throttling protection for downstream services.
Integration capabilities access S3 objects for processing, write results to DynamoDB or S3, invoke other Lambda functions, publish to SNS/SQS, and integrate with services ecosystem.
Performance optimization includes configuring appropriate memory, using environment variables, implementing connection reuse, optimizing dependencies, and monitoring execution duration.
Best practices recommend implementing idempotent functions, handling failures gracefully, using environment variables, enabling dead-letter queues, monitoring CloudWatch metrics, setting appropriate timeouts, and optimizing package size.
Why other options are incorrect:
A) EC2 requires server management, manual scaling, paying for idle time, more operational overhead, and not serverless as required.
B) ECS manages containers, requires cluster management, more complex than needed, not event-driven by default, and involves infrastructure management.
D) Elastic Beanstalk provides managed platform, simplifies deployment, but manages infrastructure, not event-driven, and more overhead than Lambda for simple event processing.
Question 70
A developer must store application secrets securely with automatic rotation. Which AWS service provides this capability?
A) Parameter Store
B) Secrets Manager
C) KMS
D) IAM
Answer: B
Explanation:
Secrets management requires secure storage with rotation capabilities. Secrets Manager stores secrets securely, automatically rotates credentials, integrates with RDS and other services, encrypts data with KMS, enables programmatic retrieval, and provides comprehensive secrets lifecycle management.
Secrets Manager stores database credentials, API keys, OAuth tokens, and other secrets, encrypts at rest and in transit, supports automatic rotation, maintains version history, and integrates with applications seamlessly.
Automatic rotation enables scheduled credential changes, uses Lambda functions for rotation logic, updates credentials in target services, maintains previous versions during rotation, and reduces security risk from static credentials.
Rotation strategies include single-user rotation for basic scenarios, alternating users for zero-downtime, custom rotation logic via Lambda, and integration with RDS for automatic rotation.
Security features provide encryption using KMS, fine-grained access control with IAM, audit trail through CloudTrail, resource-based policies, and VPC endpoint for private access.
Integration capabilities work with RDS for database credentials, Redshift for data warehouse access, DocumentDB credentials, Lambda for application access, and ECS for container applications.
Retrieval patterns show applications calling GetSecretValue API, SDK handling decryption automatically, caching secrets for performance, and rotating transparently.
Version management maintains multiple versions, enables gradual rollover, supports testing new credentials, allows rollback if needed, and tracks version history.
Best practices recommend enabling automatic rotation, using least privilege access, implementing caching strategies, monitoring access in CloudTrail, configuring resource policies, and testing rotation procedures.
Why other options are incorrect:
A) Parameter Store stores configuration, supports secrets with SecureString, no automatic rotation built-in, suitable for config management, and simpler but less feature-rich than Secrets Manager.
C) KMS provides encryption keys, encrypts data, manages key lifecycle, doesn’t store application secrets, and works with other services for encryption.
D) IAM manages identities and access, provides temporary credentials, doesn’t store application secrets, and serves authentication/authorization purpose.
Question 71
A developer needs to implement a continuous delivery pipeline. Which AWS service orchestrates the deployment workflow?
A) CodeCommit
B) CodeBuild
C) CodePipeline
D) CodeDeploy
Answer: C
Explanation:
CI/CD automation requires workflow orchestration. CodePipeline orchestrates continuous delivery workflow, automates release process, integrates with source control and build/deploy services, enables automated testing, provides visualization, and coordinates entire deployment pipeline.
CodePipeline defines stages and actions, coordinates source, build, test, and deploy phases, integrates with AWS and third-party tools, provides status visualization, and automates software release process.
Pipeline stages include source stage pulling code from repository, build stage compiling and testing, test stage running automated tests, deploy stage releasing to environment, and custom stages for specific needs.
Integration capabilities work with CodeCommit/GitHub/Bitbucket for source, CodeBuild for compilation, CodeDeploy for deployment, Lambda for custom actions, and third-party tools via plugins.
Deployment strategies support single-environment deployments, multi-environment promotion, manual approval gates, parallel actions, and complex workflows.
Trigger mechanisms include automatic on code commits, scheduled pipelines, manual execution, and webhook integrations.
State management tracks execution history, displays pipeline status visually, enables debugging failures, maintains audit trail, and provides notifications.
Custom actions extend with Lambda functions, integrate custom tools, implement business logic, validate deployments, and enhance workflow.
Best practices recommend defining clear stages, implementing automated testing, using manual approvals for production, enabling notifications, monitoring pipeline metrics, maintaining separate pipelines per environment, and documenting pipeline design.
Why other options are incorrect:
A) CodeCommit is source control service, stores code repositories, supports Git, doesn’t orchestrate pipelines, and provides source stage input.
B) CodeBuild compiles source code, runs tests, produces artifacts, doesn’t orchestrate workflow, and serves as build stage component.
D) CodeDeploy automates deployments, handles blue-green strategies, doesn’t orchestrate entire pipeline, and serves as deploy stage component.
Question 72
A developer must implement single sign-on for a web application. Which AWS service provides user authentication and authorization?
A) IAM
B) Cognito
C) STS
D) Directory Service
Answer: B
Explanation:
User authentication for applications requires identity management service. Cognito provides user sign-up/sign-in, supports social identity providers, enables federated identities, manages user pools, provides tokens for API access, and offers comprehensive authentication solution for web and mobile applications.
Cognito User Pools manage user directories, handle authentication, support MFA, customize workflows, provide JWT tokens, and integrate with application backends.
User Pool features include user registration and verification, password policies, MFA support, custom attributes, hosted UI for authentication, Lambda triggers for customization, and group-based permissions.
Identity Pools provide AWS credentials for authenticated users, enable access to AWS services, support anonymous access, integrate with User Pools, and enable fine-grained IAM permissions.
Federation support integrates with social providers (Facebook, Google, Amazon), SAML identity providers for enterprise SSO, OpenID Connect providers, and custom authentication flows.
Token management issues JWT tokens, includes ID token for user info, access token for API calls, refresh token for renewal, and validates tokens automatically.
Security features provide password policies, MFA enforcement, compromised credential protection, adaptive authentication, and advanced security features.
Integration patterns show web applications using Cognito SDK, API Gateway validating Cognito tokens, Lambda accessing user context, and mobile apps using AWS Amplify.
Best practices recommend enabling MFA for security, implementing password policies, using hosted UI for faster implementation, customizing with Lambda triggers, monitoring sign-in analytics, implementing token refresh, and securing tokens properly.
Why other options are incorrect:
A) IAM manages AWS resource access, not application user authentication, designed for AWS services/users, and serves different purpose than user sign-in.
C) STS provides temporary security credentials, grants federated access, doesn’t manage user identities, and requires existing authentication mechanism.
D) Directory Service runs Microsoft AD, enterprise directory service, more complex than needed for web app authentication, and designed for enterprise scenarios.
Question 73
A developer needs to process streaming data in real-time with sub-second latency. Which AWS service is most appropriate?
A) SQS
B) Kinesis Data Streams
C) S3
D) RDS
Answer: B
Explanation:
Real-time data streaming requires high-throughput ingestion and processing. Kinesis Data Streams captures streaming data in real-time, provides sub-second processing latency, scales to gigabytes per second, enables multiple consumers, maintains data ordering within shards, and represents optimal solution for streaming analytics.
Kinesis Data Streams ingests real-time data from various sources, stores data for 24 hours to 365 days, enables multiple applications consuming simultaneously, processes data with low latency, and supports complex streaming analytics.
Stream architecture uses shards determining throughput capacity, producers writing records to stream, consumers processing in real-time, partition keys controlling shard assignment, and parallel processing for scalability.
Shard capacity provides 1 MB/sec write and 2 MB/sec read per shard, supports 1000 records/sec writes, enables horizontal scaling through additional shards, and allows dynamic resharding.
Consumer types include Kinesis Client Library applications, Lambda functions for serverless processing, Kinesis Data Analytics for SQL queries, custom applications using SDK, and Kinesis Data Firehose for data delivery.
Use cases show log aggregation, clickstream analysis, IoT telemetry, gaming data feeds, financial trading data, and real-time metrics collection.
Data retention configurable from 24 hours to 365 days, enables replay for debugging, supports disaster recovery, and allows multiple processing attempts.
Integration capabilities work with Lambda for processing, Kinesis Data Analytics for SQL, Kinesis Data Firehose for storage, EMR for big data, and custom applications.
Best practices recommend sizing shards appropriately, using partition keys for distribution, implementing error handling, monitoring shard metrics, enabling enhanced fan-out for consumers, and optimizing batch sizes.
Why other options are incorrect:
A) SQS provides message queuing, asynchronous processing, not optimized for streaming, higher latency than Kinesis, and serves different use case.
C) S3 is object storage, batch processing, not real-time streaming, higher latency, and designed for storage not stream processing.
D) RDS is relational database, transactional data storage, not streaming platform, inappropriate for high-throughput streaming, and serves database purpose.
Question 74
A developer must ensure Lambda functions can access resources in a private subnet. Which configuration is required?
A) Internet Gateway
B) VPC configuration with private subnet
C) Public IP address
D) Direct Connect
Answer: B
Explanation:
Lambda networking for private resources requires VPC integration. VPC configuration with private subnet enables Lambda accessing VPC resources, connects to RDS databases, reaches internal services, maintains security through private networking, uses ENI for connectivity, and provides controlled access to resources.
Lambda VPC integration creates Elastic Network Interfaces in specified subnets, provides private IP addresses, enables communication with VPC resources, supports security groups, and maintains network isolation.
Configuration requirements include specifying VPC, selecting private subnets (multiple AZs recommended), assigning security groups, configuring IAM permissions for ENI creation, and planning IP address capacity.
Network connectivity shows Lambda using ENI to communicate, accessing RDS in private subnets, reaching ElastiCache clusters, connecting to internal APIs, and maintaining private network isolation.
Internet access from VPC Lambda requires NAT Gateway in public subnet, route table configuration, enabling outbound internet access, while maintaining inbound isolation, and supporting hybrid architectures.
Security considerations implement security groups controlling traffic, network ACLs for subnet-level filtering, IAM policies for service access, VPC endpoints for AWS services, and maintaining least privilege.
Performance implications include cold start increase from ENI creation, warmed connections after initial invocation, provisioned concurrency mitigating cold starts, and monitoring for network issues.
Best practices recommend using multiple subnets across AZs, implementing VPC endpoints for AWS services, configuring appropriate security groups, sizing subnets for IP addresses, monitoring ENI usage, and testing connectivity thoroughly.
Why other options are incorrect:
A) Internet Gateway provides internet access, not required for private resource access, used for public connectivity, and Lambda needs VPC configuration not IGW directly.
C) Public IP address unnecessary, Lambda uses private IPs in VPC, actually contrary to private access requirement, and maintains security through private networking.
D) Direct Connect links on-premises to AWS, not required for Lambda VPC access, serves hybrid connectivity, and VPC configuration sufficient for AWS resources.
Question 75
A developer needs to implement request throttling for an API to prevent abuse. Which API Gateway feature should be used?
A) Caching
B) Usage Plans and API Keys
C) Lambda authorizers
D) Resource policies
Answer: B
Explanation:
API protection requires request rate limiting. Usage Plans and API Keys control API access through throttling limits, define request quotas, associate keys with plans, monitor usage, prevent abuse, and enable different service tiers.
Usage Plans define throttle limits per second/burst, set quota for requests per day/month, associate with API stages, enable API key requirement, and provide flexible rate limiting.
Throttling mechanisms include rate limits controlling requests per second, burst limits handling temporary spikes, quota limits setting total request caps, automatic throttling returning 429 errors, and protecting backend services.
API Keys identify API consumers, associate with usage plans, enable tracking per customer, support multiple keys per plan, and facilitate access management.
Implementation pattern involves creating usage plans with appropriate limits, generating API keys for consumers, associating keys with plans, requiring API key in requests, and monitoring usage through CloudWatch.
Service tiers enable free tier with lower limits, paid tiers with higher throughput, enterprise plans with dedicated capacity, custom limits per customer, and flexible pricing models.
Monitoring capabilities track requests per API key, identify quota violations, analyze usage patterns, generate billing data, and enable capacity planning.
Best practices recommend setting appropriate initial limits, monitoring usage patterns, gradually increasing limits, implementing alert thresholds, documenting API limits, communicating changes to consumers, and testing throttling behavior.
Why other options are incorrect:
A) Caching reduces backend calls, improves performance, doesn’t prevent abuse, no request counting, and serves optimization not throttling purpose.
C) Lambda authorizers validate authentication/authorization, control access decisions, don’t implement throttling, and serve security validation not rate limiting.
D) Resource policies control who can invoke API, provide access control, don’t implement request throttling, and serve authorization not rate limiting purpose.
Question 76
A developer must implement data encryption at rest for DynamoDB tables. Which feature should be enabled?
A) Client-side encryption only
B) KMS encryption
C) SSL/TLS
D) Security groups
Answer: B
Explanation:
Data protection requires encryption at rest. KMS (Key Management Service) encryption provides server-side encryption for DynamoDB, encrypts data automatically, manages keys securely, enables compliance requirements, integrates seamlessly, and protects data at rest comprehensively.
DynamoDB encryption uses KMS Customer Master Keys (CMK), encrypts tables and secondary indexes automatically, encrypts local secondary indexes, protects backup data, and maintains encryption transparently.
Encryption options include AWS owned keys (default, no cost), AWS managed keys (aws/dynamodb), and customer managed keys providing full control over key policies and rotation.
Key management enables custom key policies for access control, automatic key rotation annually, detailed audit trail via CloudTrail, cross-account access when needed, and centralized key management.
Encryption scope covers table data, local secondary indexes, global secondary indexes, streams, backups, and point-in-time recovery snapshots.
Performance impact shows minimal latency increase, transparent to applications, no code changes required, automatic encryption/decryption, and maintained throughput capacity.
Compliance benefits meet regulatory requirements, demonstrate data protection, provide audit trails, enable data sovereignty, and support security frameworks.
Implementation steps involve enabling encryption on new tables, choosing KMS key type, granting DynamoDB permissions, optionally encrypting existing tables, and verifying encryption status.
Best practices recommend using customer managed keys for control, enabling automatic rotation, implementing least privilege key policies, monitoring key usage, documenting encryption strategy, and testing key rotation procedures.
Why other options are incorrect:
A) Client-side encryption requires application implementation, more complex, doesn’t leverage DynamoDB features, and server-side encryption preferred for simplicity.
C) SSL/TLS encrypts data in transit not at rest, protects network communication, different security layer, and both needed for comprehensive protection.
D) Security groups control network access, don’t encrypt data, provide network-level security, and serve different security function.
Question 77
A developer needs to invoke Lambda functions synchronously and handle errors. Which invocation method should be used?
A) RequestResponse invocation
B) Event invocation
C) DryRun invocation
D) S3 trigger
Answer: A
Explanation:
Lambda invocation types determine execution patterns. RequestResponse invocation provides synchronous execution, waits for function completion, returns response immediately, enables error handling in code, maintains request-response flow, and represents appropriate method for synchronous operations.
Synchronous invocation blocks until function completes, returns result directly, throws errors to caller, enables immediate error handling, and supports retry logic in calling code.
Invocation characteristics show caller waiting for completion, receiving function response, getting error details immediately, handling exceptions directly, and maintaining execution context.
Use cases include API Gateway integrations, Application Load Balancer requests, SDK invocations requiring responses, synchronous microservice calls, and real-time processing needs.
Error handling provides immediate exception details, enables custom retry logic, returns error responses, supports debugging, and allows graceful degradation.
Response payload returns function output directly, includes execution metadata, provides status code, enables data passing, and supports complex response objects.
Performance considerations show execution blocking caller, requiring timeout configuration, planning for latency, implementing appropriate timeouts, and considering concurrency limits.
Best practices recommend implementing timeouts, handling errors gracefully, using exponential backoff for retries, monitoring synchronous metrics, considering asynchronous for long operations, and testing error scenarios.
Why other options are incorrect:
B) Event invocation is asynchronous, doesn’t wait for completion, no immediate response, Lambda retries automatically, and doesn’t meet synchronous requirement.
C) DryRun invocation validates parameters, doesn’t execute function, checks permissions, and serves testing not actual execution purpose.
D) S3 trigger is event-driven, asynchronous invocation, not direct synchronous call, and represents event source not invocation type.
Question 78
A developer must implement caching to reduce database load. Which ElastiCache engine supports complex data structures?
A) Memcached
B) Redis
C) DynamoDB Accelerator
D) CloudFront
Answer: B
Explanation:
Advanced caching requires sophisticated data structure support. Redis provides in-memory data store supporting complex data types including strings, hashes, lists, sets, sorted sets, bitmaps, and HyperLogLogs, enabling rich caching patterns, maintaining data persistence, supporting replication, and offering advanced features.
Redis supports atomic operations, pub/sub messaging, Lua scripting, transactions, automatic failover, and provides more features than simple key-value stores.
Data structures include strings for simple values, hashes for object storage, lists for queues, sets for unique collections, sorted sets for leaderboards, and specialized types for specific use cases.
Advanced features provide persistence with RDB snapshots and AOF logs, replication for read scaling and high availability, Redis Cluster for horizontal scaling, automatic failover, and multi-AZ support.
Use cases show session storage, real-time leaderboards, rate limiting, pub/sub messaging, queuing systems, and complex caching scenarios requiring data structure operations.
Persistence options include RDB snapshots for point-in-time backups, AOF logging for durability, backup and restore capabilities, and disaster recovery support.
Cluster mode enables horizontal scaling, partitions data across shards, supports up to 500 nodes, provides automatic failover, and handles larger datasets.
Best practices recommend enabling persistence for important data, using cluster mode for scale, implementing proper key expiration, monitoring memory usage, setting appropriate eviction policies, and planning for failover.
Why other options are incorrect:
A) Memcached simpler key-value store, no complex data structures, no persistence, multi-threaded, but lacks Redis advanced features for complex scenarios.
C) DynamoDB Accelerator (DAX) is DynamoDB-specific cache, microsecond latency, doesn’t support complex data structures like Redis, and serves different caching purpose.
D) CloudFront is CDN, caches static content, edge locations, doesn’t provide data structure support, and serves content delivery not database caching.
Question 79
A developer needs to process S3 data using SQL queries without loading data into database. Which AWS service provides this capability?
A) RDS
B) Athena
C) Redshift
D) EMR
Answer: B
Explanation:
Serverless data analytics requires query-in-place capabilities. Athena provides serverless interactive query service, analyzes data in S3 using SQL, requires no infrastructure management, supports standard SQL, integrates with Glue Data Catalog, and charges only for queries executed.
Athena queries data directly in S3 without data movement, uses Presto engine, supports various formats (CSV, JSON, Parquet, ORC), provides JDBC/ODBC connectivity, and integrates with visualization tools.
Query capabilities enable standard SQL queries, support complex joins, implement window functions, allow CREATE TABLE AS SELECT, provide federated queries, and enable data transformation.
Data formats support CSV files, JSON documents, Apache Parquet for columnar storage, ORC format, Avro, and custom formats with SerDes.
Glue integration uses Data Catalog for metadata, enables schema discovery, provides crawlers for automatic cataloging, maintains table definitions, and supports partitioning.
Performance optimization implements partitioning reducing scanned data, uses columnar formats (Parquet, ORC), compresses data, converts data types appropriately, and optimizes query patterns.
Cost optimization charges per data scanned, benefits from compression, partitioning reduces costs, columnar formats save significantly, and no infrastructure charges.
Use cases include log analysis, business intelligence, ad-hoc queries, data lake analytics, ETL validation, and exploratory data analysis.
Best practices recommend using columnar formats, implementing partitioning schemes, compressing data, optimizing table structures, monitoring query costs, using workgroups for governance, and caching results when appropriate.
Why other options are incorrect:
A) RDS requires loading data into database, managed relational database, not serverless analytics, involves data movement, and serves transactional not analytics purpose.
C) Redshift is data warehouse, requires loading data, cluster management, designed for complex analytics, but involves infrastructure and data movement unlike serverless query-in-place.
D) EMR provides big data processing, requires cluster management, more complex setup, used for large-scale processing, and not simple SQL queries on S3.
Question 80
A developer must implement a fan-out messaging pattern where multiple consumers receive the same message. Which AWS service combination is most appropriate?
A) SQS only
B) SNS with SQS subscriptions
C) Kinesis only
D) EventBridge only
Answer: B
Explanation:
Message fan-out requires pub/sub with queuing. SNS with SQS subscriptions provides optimal fan-out pattern where SNS publishes messages to multiple SQS queues, each consumer processes from own queue, enables independent scaling, guarantees message delivery, decouples components, and represents standard fan-out architecture.
SNS publishes messages to multiple subscribers simultaneously, each SQS queue receives copy independently, consumers process at own pace, failures isolated per consumer, and system maintains loose coupling.
Architecture pattern shows producer publishing to SNS topic, multiple SQS queues subscribed to topic, each consumer polling own queue, processing independently, and achieving parallel processing.
Benefits include message durability in queues, independent consumer scaling, retry handling per queue, dead-letter queues per subscriber, and operational flexibility.
Message filtering enables subscribers receiving relevant messages only, uses filter policies on subscriptions, reduces unnecessary processing, optimizes costs, and improves efficiency.
Scalability allows adding subscribers dynamically, scaling consumers independently, handling variable loads, and maintaining performance.
Reliability features provide message persistence in SQS, automatic retries, visibility timeout, dead-letter queues, and at-least-once delivery guarantee.
Use cases show order processing to multiple systems, notifications to various consumers, event distribution, workflow triggering, and microservices communication.
Implementation steps involve creating SNS topic, creating SQS queues for each consumer, subscribing queues to topic, configuring filter policies if needed, and implementing consumer applications.
Best practices recommend using FIFO for ordering requirements, implementing idempotent consumers, configuring appropriate visibility timeouts, enabling dead-letter queues, monitoring queue depths, and testing failure scenarios.
Why other options are incorrect:
A) SQS only provides queuing, one message consumed by one consumer, no native fan-out, would require custom implementation, and doesn’t support multiple independent consumers for same message.
C) Kinesis provides streaming, ordered processing, single shard read by multiple consumers, but more complex, designed for different use case, and SNS+SQS simpler for basic fan-out.
D) EventBridge provides event routing, powerful pattern matching, can fan-out, but SNS+SQS simpler for basic fan-out messaging, and EventBridge better for complex event-driven architectures.
