Visit here for our full Amazon AWS Certified Developer – Associate DVA-C02 exam dumps and practice test questions.
Question 101
A developer needs to implement canary deployment for a Lambda function to minimize risk. Which feature should be used?
A) Lambda versions only
B) Lambda aliases with traffic shifting
C) Environment variables
D) Separate functions for each version
Answer: B
Explanation:
Progressive deployment strategies require controlled traffic distribution. Lambda aliases with traffic shifting enables canary deployments by routing percentage of traffic to new version, gradually increasing exposure, monitoring for errors, enabling automatic rollback, and minimizing deployment risk through gradual rollout.
Lambda aliases are pointers to specific function versions supporting weighted traffic distribution between two versions, enabling gradual migration, maintaining stable endpoint, and facilitating safe deployments.
Traffic shifting patterns include canary deployment routing small percentage (10%) to new version initially, linear deployment increasing percentage gradually over time, all-at-once switching traffic completely, and custom percentages for specific requirements.
Implementation approach involves publishing new function version, updating alias to point to both old and new versions, specifying traffic weights, monitoring metrics during shift, adjusting weights based on performance, and completing rollout or rolling back.
CloudWatch integration monitors error rates, tracks invocation metrics, compares version performance, triggers alarms on issues, and enables data-driven decisions.
CodeDeploy integration automates traffic shifting, implements predefined deployment strategies, handles rollback automatically on alarm triggers, provides deployment history, and enables sophisticated deployment patterns.
Rollback capabilities show automatic rollback on CloudWatch alarms, manual rollback by adjusting weights, instant traffic redirect to stable version, and zero-downtime recovery.
Use cases include testing new features with subset of users, validating performance improvements, ensuring backward compatibility, minimizing blast radius, and meeting deployment safety requirements.
Best practices recommend starting with small traffic percentage, monitoring error rates closely, setting CloudWatch alarms, automating with CodeDeploy, testing rollback procedures, and documenting deployment strategy.
Why other options are incorrect:
A) Lambda versions provide immutable snapshots, enable versioning, but don’t support traffic splitting, require alias for routing, and insufficient alone for canary deployment.
C) Environment variables configure function behavior, don’t control traffic routing, different purpose, and not relevant to deployment strategy.
D) Separate functions for versions creates management complexity, no automatic traffic distribution, manual switching required, and violates deployment best practices.
Question 102
A developer must implement exponential backoff for API retries. Which SDK feature provides this automatically?
A) Custom retry logic only
B) AWS SDK automatic retry with backoff
C) Manual sleep implementation
D) CloudWatch Events
Answer: B
Explanation:
Resilient applications require intelligent retry mechanisms. AWS SDK automatic retry with backoff implements exponential backoff automatically, handles transient failures, prevents overwhelming services, uses jitter to avoid thundering herd, configures retry attempts, and provides production-ready error handling.
AWS SDKs include built-in retry logic with exponential backoff, automatically retrying failed requests on throttling or server errors, increasing delay between attempts exponentially, adding random jitter, and respecting service-specific retry policies.
Retry scenarios include throttling errors (HTTP 429), server errors (5xx), timeout exceptions, network connectivity issues, and transient failures requiring retry.
Backoff calculation uses exponential delay starting with short wait (100ms), doubling each retry (200ms, 400ms, 800ms), adding random jitter preventing simultaneous retries, capping maximum delay, and limiting total attempts.
SDK configuration allows setting maximum retry attempts, configuring retry mode (legacy, standard, adaptive), customizing retry strategy, disabling retries if needed, and monitoring retry metrics.
Adaptive retry mode analyzes service responses, adjusts retry behavior dynamically, improves efficiency, reduces unnecessary retries, and optimizes for service conditions.
Error categorization shows retryable errors triggering backoff, non-retryable errors failing immediately, understanding error types, and implementing appropriate handling.
Best practices recommend using SDK defaults, enabling adaptive mode for efficiency, monitoring retry metrics, setting appropriate timeout values, testing failure scenarios, and logging retry attempts for debugging.
Why other options are incorrect:
A) Custom retry logic requires manual implementation, more complex, error-prone, misses SDK optimizations, and reinvents existing functionality.
C) Manual sleep implementation lacks exponential calculation, no jitter, requires extensive coding, misses service-specific optimizations, and SDK provides better solution.
D) CloudWatch Events schedules actions, not retry mechanism, different service purpose, doesn’t handle API retries, and unrelated to SDK retry logic.
Question 103
A developer needs to store application logs centrally for analysis. Which AWS service provides managed log aggregation?
A) S3
B) CloudWatch Logs
C) DynamoDB
D) RDS
Answer: B
Explanation:
Centralized logging requires managed log aggregation service. CloudWatch Logs collects logs from applications and services, provides centralized storage, enables searching and filtering, supports real-time monitoring, integrates with Lambda for processing, and offers comprehensive log management solution.
CloudWatch Logs receives log data from applications, EC2 instances, Lambda functions, and other services, organizes into log groups and streams, retains data based on policies, enables queries using Logs Insights, and provides metric filters.
Log organization uses log groups containing related streams, log streams for specific sources (instances, functions), hierarchical structure for management, retention policies per group, and access control via IAM.
Collection methods include CloudWatch Logs agent on EC2/on-premises, unified CloudWatch agent with advanced features, Lambda automatic logging, container logging drivers, and SDK PutLogEvents API.
Logs Insights provides SQL-like query language, searches across log groups, aggregates data, visualizes results, identifies patterns, and enables interactive analysis.
Metric filters extract metrics from logs, create CloudWatch metrics, enable alarms on log patterns, track specific events, and support automated responses.
Integration capabilities work with Lambda for processing, Kinesis for streaming, S3 for archival, Elasticsearch for advanced search, and Athena for SQL queries.
Retention management configures retention periods (1 day to 10 years or indefinite), automatically deletes old logs, balances cost against retention needs, and archives to S3 for long-term storage.
Best practices recommend organizing log groups logically, setting appropriate retention, using metric filters for monitoring, implementing encryption, controlling access with IAM, monitoring costs, and leveraging Logs Insights for analysis.
Why other options are incorrect:
A) S3 provides object storage, suitable for log archival, no built-in log aggregation features, requires custom processing, and CloudWatch Logs better for active log management.
C) DynamoDB is NoSQL database, not designed for logs, expensive for log volume, lacks log-specific features, and inappropriate for log storage.
D) RDS is relational database, not for log storage, inappropriate data model, performance issues with log volume, and designed for transactional data.
Question 104
A developer must implement request tracing across multiple Lambda functions. Which approach provides end-to-end visibility?
A) CloudWatch Logs only
B) X-Ray active tracing
C) CloudTrail
D) Manual correlation IDs
Answer: B
Explanation:
Distributed tracing requires correlation across services. X-Ray active tracing automatically traces requests across Lambda functions, captures detailed execution data, creates service maps, identifies bottlenecks, enables debugging distributed applications, and provides comprehensive observability.
X-Ray tracing for Lambda enabled through function configuration, automatically captures invocation data, traces downstream calls to AWS services and HTTP endpoints, correlates distributed requests, and visualizes execution flow.
Trace data collection shows Lambda automatically creating segments, capturing function execution details, recording downstream calls as subsegments, including errors and exceptions, and maintaining trace context across services.
Service map visualization displays Lambda functions and dependencies automatically, shows latency distribution, indicates error rates, identifies performance issues, and updates in real-time.
Active tracing enables detailed request tracing, captures all invocations, provides sampling control, traces across service boundaries, maintains context through X-Ray headers, and enables comprehensive debugging.
Trace analysis identifies slow operations, finds error sources, analyzes latency breakdown, compares traces, filters by criteria, and enables root cause analysis.
Integration benefits work with API Gateway propagating trace context, SDK instrumenting custom code, automatic AWS service instrumentation, downstream HTTP call tracing, and Lambda-to-Lambda tracing.
Sampling rules control trace volume, balance cost against visibility, configure rates per service, use default rules, and customize for requirements.
Best practices recommend enabling active tracing on all functions, adding custom annotations, using subsegments for detail, implementing proper error handling, monitoring X-Ray service map, analyzing trace patterns, and setting appropriate sampling.
Why other options are incorrect:
A) CloudWatch Logs captures logs, no automatic correlation across functions, requires manual implementation, lacks service map, and doesn’t provide distributed tracing.
C) CloudTrail audits API calls, management plane events, not application tracing, doesn’t track request flow, and serves governance not debugging purpose.
D) Manual correlation IDs require custom implementation, no automatic instrumentation, lacks visualization, extensive coding needed, and X-Ray provides better built-in solution.
Question 105
A developer needs to implement pagination for DynamoDB query results. Which approach should be used?
A) Retrieve all results at once
B) Use LastEvaluatedKey for pagination
C) Implement client-side filtering
D) Use Scan with filters
Answer: B
Explanation:
Efficient data retrieval requires proper pagination. Using LastEvaluatedKey for pagination enables retrieving results in pages, prevents memory issues, reduces response time, optimizes read capacity consumption, handles large result sets, and represents proper DynamoDB pagination pattern.
DynamoDB returns maximum 1 MB per Query/Scan operation, provides LastEvaluatedKey when more results exist, uses ExclusiveStartKey in subsequent requests, maintains consistency across pages, and enables efficient pagination.
Pagination workflow shows initial query without StartKey, receiving first page of results, checking for LastEvaluatedKey presence, using it as ExclusiveStartKey for next query, repeating until no LastEvaluatedKey, and collecting all pages.
Implementation pattern involves making initial Query/Scan request, examining response for LastEvaluatedKey, passing as ExclusiveStartKey to next request, accumulating results, handling properly in application code, and processing page by page.
Read capacity management shows pagination consuming RCUs per page, predictable capacity usage, avoiding large burst consumption, enabling better capacity planning, and optimizing costs.
Client implementation requires tracking pagination state, handling async operations, managing memory efficiently, displaying loading indicators, and implementing infinite scroll or page-based navigation.
Performance optimization limits page size with Limit parameter, processes results incrementally, avoids loading entire dataset, enables responsive UIs, and balances latency against throughput.
Error handling implements retry logic per page, handles throttling gracefully, maintains pagination state on failures, and ensures data completeness.
Best practices recommend implementing pagination for all queries, processing results incrementally, handling LastEvaluatedKey properly, setting appropriate Limit values, monitoring consumed capacity, and testing with large datasets.
Why other options are incorrect:
A) Retrieving all results at once hits 1MB limit, causes memory issues, inefficient for large datasets, high latency, and violates best practices.
C) Client-side filtering wastes read capacity, retrieves unnecessary data, inefficient, expensive, and filtering should occur in query when possible.
D) Scan with filters reads entire table, extremely inefficient, high cost, slow performance, and Query with proper keys preferred over Scan.
Question 106
A developer must implement fine-grained access control for DynamoDB items. Which feature enables item-level permissions?
A) IAM policies with conditions
B) Security groups
C) Network ACLs
D) Resource-based policies
Answer: A
Explanation:
Item-level security requires conditional access control. IAM policies with conditions enable fine-grained permissions based on attributes, control access to specific items, implement row-level security, use condition keys for filtering, enable multi-tenancy, and provide granular authorization.
IAM condition keys for DynamoDB enable restricting access based on partition key values, sort key ranges, attribute values, request attributes, and user context, implementing sophisticated access patterns.
Condition operators include string conditions matching partition keys, numeric conditions for ranges, date/time conditions for temporal access, boolean conditions for flags, and ARN conditions for resource-based rules.
Leading key conditions use dynamodb:LeadingKeys matching partition key prefix, enable multi-tenant isolation, restrict users to own data, implement hierarchical access, and maintain security boundaries.
Attribute-based access leverages dynamodb:Attributes limiting returned attributes, implements column-level security, prevents exposure of sensitive data, enables need-to-know access, and supports compliance requirements.
Multi-tenancy patterns show users accessing only their partition, tenant ID in partition key, IAM policy restricting to user’s tenant, isolation between customers, and scalable security model.
Policy examples include allowing access where partition key equals user ID, restricting queries to specific key ranges, permitting read of certain attributes only, and combining multiple conditions.
Implementation considerations require designing partition keys supporting access patterns, incorporating user context in keys, planning for scale, testing permissions thoroughly, and documenting security model.
Best practices recommend using leading keys for isolation, implementing least privilege, testing access policies, monitoring unauthorized attempts, combining with application-level security, and regularly auditing permissions.
Why other options are incorrect:
B) Security groups control network access, operate at instance/ENI level, don’t provide item-level permissions, and serve network security purpose.
C) Network ACLs filter subnet traffic, network-layer security, no item-level control, and unrelated to DynamoDB authorization.
D) DynamoDB doesn’t support resource-based policies, IAM policies control access, and resource policies are for services like S3 and Lambda.
Question 107
A developer needs to reduce Lambda cold start time. Which optimization technique is most effective?
A) Increase memory allocation
B) Use provisioned concurrency
C) Add more code
D) Use larger deployment packages
Answer: B
Explanation:
Cold start optimization requires pre-initialized execution environments. Provisioned concurrency maintains pre-initialized execution environments, eliminates cold starts for configured concurrency, ensures consistent low latency, keeps functions warm, enables predictable performance, and represents most effective cold start solution.
Provisioned concurrency initializes configured number of execution environments, keeps them warm and ready, runs initialization code once, maintains state, and provides instant response to invocations.
Cold start components include downloading deployment package, starting execution environment, initializing runtime, executing initialization code, and invoking handler function.
Provisioned concurrency benefits show pre-initialized environments eliminating cold start, consistent performance, reduced latency variance, improved user experience, and meeting SLA requirements.
Configuration options set provisioned concurrency per alias or version, configure auto-scaling, schedule based on traffic patterns, manage costs effectively, and balance performance against cost.
Cost considerations show provisioned concurrency charged for configured capacity, regardless of invocations, different pricing from on-demand, requiring cost-benefit analysis, and appropriate for latency-sensitive applications.
Use cases include latency-sensitive APIs requiring consistent response times, scheduled events with predictable load, high-traffic applications, meeting strict SLAs, and production workloads demanding reliability.
Alternative optimizations include reducing deployment package size, minimizing initialization code, using compiled languages (Go, Java with GraalVM), avoiding VPC when possible, optimizing dependencies, and increasing memory (also improves CPU).
Best practices recommend enabling for production-critical functions, configuring auto-scaling, monitoring utilization, optimizing initialization code, testing cold start scenarios, balancing cost against requirements, and documenting decisions.
Why other options are incorrect:
A) Increasing memory improves CPU performance, helps somewhat, but doesn’t eliminate cold starts, and provisioned concurrency more effective specifically for cold start issue.
C) Adding more code increases cold start time, counterproductive, should minimize code, and worsens the problem.
D) Larger deployment packages increase download time, worsen cold starts, should minimize package size, and opposite of optimization.
Question 108
A developer must implement idempotency for Lambda processing SQS messages. Which approach ensures messages are processed exactly once?
A) Process all messages without tracking
B) Use DynamoDB for idempotency tracking
C) Delete messages immediately
D) Increase batch size
Answer: B
Explanation:
Reliable message processing requires idempotency. Using DynamoDB for idempotency tracking stores processed message IDs, prevents duplicate processing, handles retries safely, maintains processing state, enables exactly-once semantics, and ensures reliable message handling.
Idempotency ensures processing same message multiple times produces identical results, prevents duplicate transactions, maintains data consistency, handles SQS at-least-once delivery, and implements reliable processing patterns.
Implementation pattern extracts unique message ID, checks DynamoDB for previous processing, conditionally writes record if new, processes message only if not previously handled, deletes from queue after successful processing, and handles failures appropriately.
DynamoDB schema uses message ID as partition key, includes processing timestamp, stores processing status, maintains TTL for cleanup, and enables quick lookup.
Conditional writes use PutItem with condition expression, prevent race conditions, ensure atomic operations, detect duplicate processing attempts, and maintain consistency.
Error handling distinguishes new failures from duplicate deliveries, implements retry logic, maintains idempotency through retries, logs processing attempts, and ensures message eventually processes successfully.
SQS integration shows Lambda receiving batch of messages, processing each idempotently, deleting successfully processed messages, leaving failed messages for retry, and handling visibility timeout.
TTL management automatically removes old tracking records, prevents table growth, maintains efficiency, configures appropriate retention period, and balances auditability against storage costs.
Best practices recommend always implementing idempotency for queue processing, using unique message IDs, cleaning up with TTL, handling errors gracefully, monitoring processing patterns, testing duplicate scenarios, and documenting idempotency strategy.
Why other options are incorrect:
A) Processing without tracking allows duplicates, violates idempotency, causes data inconsistency, inappropriate for reliable systems, and creates business logic issues.
C) Deleting messages immediately risks data loss on failures, doesn’t ensure processing, violates message handling best practices, and loses retry capability.
D) Increasing batch size improves throughput, doesn’t provide idempotency, unrelated to duplicate prevention, and different optimization concern.
Question 109
A developer needs to securely access on-premises database from Lambda. Which solution provides secure connectivity?
A) Public internet
B) VPN or Direct Connect with Lambda in VPC
C) Expose database publicly
D) Use SSH tunnel
Answer: B
Explanation:
Hybrid connectivity requires secure networking. VPN or Direct Connect with Lambda in VPC establishes secure connection between AWS and on-premises, enables Lambda accessing private resources, maintains security through private networking, supports hybrid architectures, and provides enterprise-grade connectivity.
Lambda VPC configuration with VPN/Direct Connect allows accessing on-premises databases securely, maintains private connectivity, avoids public exposure, implements network-level security, and enables hybrid cloud patterns.
Architecture components include VPN or Direct Connect linking networks, Lambda functions in VPC with private subnets, route tables directing on-premises traffic, security groups controlling access, and NAT Gateway for internet access if needed.
VPN connection uses IPsec tunnel, provides encrypted connectivity, supports up to 1.25 Gbps per tunnel, cost-effective solution, and suitable for moderate bandwidth requirements.
Direct Connect offers dedicated network connection, consistent network performance, higher bandwidth options, reduced data transfer costs, and preferred for production workloads.
Lambda VPC configuration places ENI in private subnets, provides IP address in VPC CIDR, enables routing to on-premises, maintains security isolation, and accesses through private connectivity.
Security considerations implement security groups restricting traffic, network ACLs for subnet-level control, on-premises firewall rules, encryption in transit, and least privilege access.
Performance optimization uses connection pooling in Lambda, maintains persistent connections, configures appropriate timeouts, monitors latency, and considers data transfer costs.
Best practices recommend using Direct Connect for production, implementing redundant connections, configuring proper routing, testing failover scenarios, monitoring connectivity, optimizing for latency, and documenting network architecture.
Why other options are incorrect:
A) Public internet lacks security, exposes database, unencrypted by default, violates security best practices, and inappropriate for enterprise data.
C) Exposing database publicly creates massive security risk, violates compliance, enables attacks, inappropriate approach, and should never be done.
D) SSH tunnel not supported in Lambda, workaround complex, not scalable, and VPN/Direct Connect proper solution.
Question 110
A developer must implement request validation for API Gateway to reduce Lambda invocations. Which feature provides this capability?
A) Lambda authorization
B) Request validation using models
C) CloudWatch monitoring
D) WAF rules
Answer: B
Explanation:
Input validation at API layer reduces costs and improves security. Request validation using models validates requests against JSON schema, rejects invalid requests before reaching backend, reduces Lambda invocations, lowers costs, improves security, and implements early validation pattern.
API Gateway request validators check request parameters and body against defined models, return 400 errors for invalid requests, prevent unnecessary Lambda invocations, provide immediate feedback, and enforce API contracts.
JSON Schema models define expected request structure, specify required properties, validate data types, enforce format constraints, define pattern matching, and document API contract.
Validation types include validating request body, validating query string parameters, validating headers, checking path parameters, and enforcing required fields.
Validator configuration sets validators per method, chooses validation scope (body only, params only, or both), associates with request models, configures error responses, and enables/disables as needed.
Cost reduction shows rejected requests not invoking Lambda, reducing compute costs, saving on Lambda invocations, optimizing resource usage, and preventing wasted processing.
Security benefits prevent malformed data reaching backend, reduce attack surface, enforce input constraints, implement defense in depth, and validate early in request flow.
Error handling returns descriptive 400 errors, provides validation failure details, helps API consumers, documents expectations, and improves developer experience.
Best practices recommend implementing validation for all public APIs, defining comprehensive models, validating both params and body, documenting schemas, testing validation rules, monitoring rejection rates, and maintaining schema documentation.
Why other options are incorrect:
A) Lambda authorization validates identity/permissions, doesn’t validate request structure, serves authentication purpose, and happens after request validation.
C) CloudWatch monitoring observes requests, doesn’t prevent invalid ones, serves observability purpose, and reactive not preventive.
D) WAF rules protect against web exploits, different security layer, doesn’t validate API contract, and serves DDoS/attack protection purpose.
Question 111
A developer needs to implement cross-region disaster recovery for DynamoDB. Which feature provides automated replication?
A) Manual backups
B) Global Tables
C) DynamoDB Streams
D) Point-in-time recovery
Answer: B
Explanation:
Multi-region availability requires automated replication. Global Tables provide fully managed multi-region, multi-active replication, enable automatic failover, maintain eventual consistency, support bi-directional replication, provide disaster recovery, and enable globally distributed applications.
Global Tables create replica tables in multiple regions, automatically replicate data changes, maintain low-latency local reads and writes, handle conflict resolution, and provide active-active configuration.
Replication architecture uses DynamoDB Streams capturing changes, replicates to all regions automatically, maintains eventual consistency globally, handles concurrent updates, and resolves conflicts using last-writer-wins strategy.
Configuration requirements enable streams on table, select regions for replicas, DynamoDB manages replication automatically, maintains schema consistency, and handles infrastructure complexity.
Consistency model provides eventual consistency across regions, typically sub-second replication, local strongly consistent reads in each region, eventually consistent global reads, and automatic conflict resolution.
Disaster recovery enables application failover to another region, maintains data availability, provides RTO/RPO near zero, requires no manual intervention, and supports business continuity.
Use cases include globally distributed applications, disaster recovery requirements, reducing latency for global users, active-active architectures, and compliance with data residency.
Cost considerations show charges for replicated write capacity, storage in all regions, data transfer between regions, replication adds costs, and balancing against availability benefits.
Best practices recommend enabling in regions close to users, monitoring replication lag, implementing proper conflict resolution logic, testing failover procedures, planning capacity in all regions, and documenting DR procedures.
Why other options are incorrect:
A) Manual backups require restoration, not automatic failover, point-in-time copy, not continuous replication, and don’t provide disaster recovery automation.
C) DynamoDB Streams capture changes, enable custom replication, requires manual implementation, not automatic like Global Tables, and more complex.
D) Point-in-time recovery restores to moment within window, same-region feature, backup/restore tool, not disaster recovery solution, and doesn’t provide cross-region replication.
Question 112
A developer must implement request signing for API calls. Which SDK feature handles authentication automatically?
A) AWS SDK with credentials
B) Manual signature calculation
C) API keys only
D) Basic authentication
Answer: A
Explanation:
Secure API communication requires proper request signing. AWS SDK with credentials automatically signs requests using Signature Version 4, handles authentication, includes credentials securely, manages signing process, prevents tampering, and provides secure API communication.
AWS SDKs automatically sign requests when configured with credentials, calculate authentication signature, include in request headers, handle complexity, prevent manual errors, and ensure secure communication.
Signing process uses AWS credentials (access key, secret key), includes request details in signature, adds timestamp preventing replay, hashes request payload, and creates authorization header.
Credential sources include environment variables, credentials file, IAM roles for EC2/ECS/Lambda, instance metadata, and credential chain in preferred order.
Security benefits authenticate requests preventing unauthorized access, ensure request integrity preventing tampering, include expiration preventing replay attacks, and maintain AWS security standards.
SDK functionality abstracts signing complexity, handles automatically, supports all AWS services, manages credential rotation, and provides consistent authentication.
Best practices recommend never hardcoding credentials, using IAM roles when possible, rotating credentials regularly, following least privilege, enabling MFA for sensitive operations, and monitoring credential usage.
Why other options are incorrect:
B) Manual signature calculation extremely complex, error-prone, reinvents wheel, SDK handles properly, and unnecessary manual work.
C) API keys used for API Gateway identification, don’t authenticate AWS API requests, different purpose, and insufficient for AWS service authentication.
D) Basic authentication not used by AWS APIs, insecure for API authentication, different authentication method, and AWS uses Signature Version 4.
Question 113
A developer needs to test Lambda functions locally before deployment. Which tool enables local testing?
A) CloudFormation
B) SAM CLI
C) CodeDeploy
D) AWS Console only
Answer: B
Explanation:
Local development improves productivity. SAM CLI (Serverless Application Model CLI) enables local Lambda testing, simulates API Gateway locally, provides local debugging, supports step-through debugging, accelerates development cycle, and offers comprehensive local testing capabilities.
SAM CLI runs Lambda functions locally, simulates AWS environments, enables rapid iteration, supports debugging with IDE integration, tests without deployment, and reduces development time.
Local invocation executes functions on development machine, uses sam local invoke, passes test events, debugs with breakpoints, and provides immediate feedback.
Local API simulates API Gateway using sam local start-api, runs local HTTP server, routes requests to functions, enables integration testing, and mirrors production behavior.
Local debugging integrates with VS Code, PyCharm, IntelliJ, supports breakpoints, inspects variables, steps through code, and provides full debugging experience.
Docker integration uses Docker containers simulating Lambda environment, maintains consistency with AWS, supports various runtimes, and requires Docker installation.
Test event generation creates sample events for testing, simulates S3, DynamoDB, SQS events, customizes test data, and enables comprehensive testing.
Environment simulation uses local environment variables, simulates IAM policies (limited), tests with local resources, and validates logic before deployment.
Best practices recommend testing locally frequently, creating comprehensive test events, using debugger effectively, validating with AWS integration tests, documenting test scenarios, and combining local testing with CI/CD.
Why other options are incorrect:
A) CloudFormation deploys infrastructure, doesn’t enable local testing, manages resources, and serves deployment not local development purpose.
C) CodeDeploy automates deployments, handles application deployment, doesn’t provide local testing, and serves deployment not development purpose.
D) AWS Console for management, doesn’t enable local testing, requires deployment to test, and SAM CLI provides better development experience.
Question 114
A developer must implement least privilege access for Lambda functions. Which IAM component defines function permissions?
A) Resource policy
B) Execution role
C) Trust policy
D) Service control policy
Answer: B
Explanation:
Lambda security requires proper permission configuration. Execution role grants Lambda permission to access AWS services, defines function’s capabilities, implements least privilege, specifies allowed actions, enables secure service integration, and represents core permission mechanism.
Execution role is IAM role assumed by Lambda during execution, includes permissions policy, defines what function can do, limits access to necessary resources, and maintains security boundaries.
Permission types show execution role controlling Lambda’s AWS access, resource policy controlling who invokes function, separation of concerns, different permission purposes, and comprehensive security model.
Policy structure includes service principal (lambda.amazonaws.com), trust relationship allowing Lambda to assume role, permissions policy with allowed actions, resource specifications, and condition keys if needed.
Least privilege implementation grants only necessary permissions, uses specific resources not wildcards, enables only required actions, reviews periodically, and minimizes security risk.
Common permissions include CloudWatch Logs for logging, S3 for object access, DynamoDB for table operations, SQS/SNS for messaging, and other service integrations.
Service-specific access uses managed policies for common patterns (AWSLambdaBasicExecutionRole for logs), creates custom policies for specific needs, combines multiple policies, and maintains granular control.
Testing permissions validates required access works, identifies missing permissions, removes unnecessary permissions, uses IAM policy simulator, and ensures proper configuration.
Best practices recommend starting with minimal permissions, adding only what’s needed, using managed policies when appropriate, avoiding wildcards, documenting permission requirements, reviewing regularly, and implementing automated permission audits.
Why other options are incorrect:
A) Resource policy controls who invokes function, not function’s permissions, different purpose, and both are needed but execution role for function’s access.
C) Trust policy allows Lambda to assume execution role, establishes trust relationship, not the permissions themselves, and enables role assumption.
D) Service control policies are organizational boundaries, not function-specific, apply across accounts, and serve different governance purpose.
Question 115
A developer needs to handle large file uploads efficiently. Which S3 feature should be used for files over 100 MB?
A) Single PUT operation
B) Multipart upload
C) Transfer Acceleration
D) Cross-region replication
Answer: B
Explanation:
Efficient large file handling requires specialized upload mechanism. Multipart upload divides large files into parts, uploads concurrently, improves throughput, enables pause/resume, handles failures gracefully, and required for files over 5 GB.
Multipart upload splits file into parts (5 MB to 5 GB each), uploads parts independently and in parallel, assembles after all parts upload, provides resilience, and optimizes large file transfers.
Upload process initiates multipart upload receiving upload ID, uploads parts with part numbers, each part receives ETag, completes upload assembling parts, and S3 creates final object.
Performance benefits show parallel uploads improving throughput, concurrent transfer utilizing bandwidth, faster completion than serial, network efficiency, and scalability.
Reliability features enable retrying failed parts only, pause and resume uploads, recover from network issues, independent part failures, and improved success rate.
Size requirements recommend multipart for files over 100 MB, require for files over 5 GB, support up to 5 TB, maximum 10,000 parts, and part size 5 MB to 5 GB.
SDK support provides high-level APIs automating multipart, handles complexity, manages part sizing, implements retries, and simplifies development.
Cost considerations charge for incomplete multipart uploads, lifecycle policies cleaning up, monitoring in-progress uploads, and completing or aborting appropriately.
Best practices recommend using multipart for large files, leveraging SDK high-level APIs, implementing lifecycle policies for cleanup, monitoring upload success, handling errors appropriately, setting appropriate part sizes, and testing with production file sizes.
Why other options are incorrect:
A) Single PUT limited to 5 GB maximum, inefficient for large files, no pause/resume, complete re-upload on failure, and not recommended over 100 MB.
C) Transfer Acceleration speeds uploads using CloudFront edge locations, complements multipart, different feature, and addresses network latency not upload mechanism.
D) Cross-region replication copies objects between regions, not upload mechanism, automatic replication, and serves different purpose than initial upload.
Question 116
A developer must implement API throttling per user. Which API Gateway feature provides this capability?
A) Stage variables
B) Usage Plans with API Keys
C) Request validation
D) Lambda authorizers
Answer: B
Explanation:
Per-user throttling requires identifying and rate-limiting individual consumers. Usage Plans with API Keys enable creating throttle limits per API key, associating keys with specific users, defining rate and burst limits, implementing quotas, tracking consumption per key, and providing granular throttling control.
Usage plans define throttle rates, burst capacity, and quota limits, associate with API stages, require API keys for requests, track usage per key, and enable different service tiers per customer.
Throttling components include rate limit controlling requests per second, burst limit handling temporary spikes, quota limiting total requests per day/month, per-key enforcement, and automatic throttling responses (429 errors).
Implementation pattern shows creating usage plan with limits, generating API keys for users, associating keys with plans, requiring x-api-key header in requests, API Gateway enforcing limits per key, and tracking consumption automatically.
Service tiers enable free tier with lower limits, paid tiers with higher quotas, premium unlimited access, per-customer custom limits, and flexible pricing models based on consumption.
Rate limit parameters set requests per second per key, configure burst capacity for spikes, define daily/monthly quotas, apply across associated stages, and cascade from plan to stage to method levels.
Monitoring capabilities track API calls per key, identify quota violations, analyze usage patterns, generate billing data, support capacity planning, and enable proactive limit adjustments.
Quota management automatically resets on schedule, provides consumption visibility, alerts on approaching limits, enables self-service upgrades, and supports automated provisioning.
Best practices recommend setting appropriate initial limits, monitoring usage patterns closely, implementing gradual limit increases, providing clear documentation, alerting users approaching quotas, enabling self-service management, and testing throttling behavior thoroughly.
Why other options are incorrect:
A) Stage variables configure environment-specific values, enable dynamic configuration, don’t implement throttling, and serve deployment configuration purpose.
C) Request validation checks request structure, validates against models, doesn’t implement throttling, and serves input validation purpose.
D) Lambda authorizers handle authentication/authorization, determine access permissions, don’t implement rate limiting, and serve identity verification purpose.
Question 117
A developer needs to implement long-running workflows with error handling. Which AWS service provides orchestration capabilities?
A) Lambda only
B) Step Functions
C) SQS
D) EventBridge
Answer: B
Explanation:
Complex workflow orchestration requires state management. Step Functions coordinates distributed applications and microservices, manages workflow state, implements error handling and retry logic, enables long-running processes, visualizes execution flow, and provides comprehensive orchestration capabilities.
Step Functions defines workflows as state machines, executes steps in order, handles transitions, manages state, implements branching logic, and coordinates multiple services.
State types include Task states calling services, Choice states for branching, Wait states for delays, Parallel states for concurrent execution, Map states for iterations, and Pass/Succeed/Fail states for flow control.
Error handling implements automatic retries with exponential backoff, catch blocks for specific errors, fallback states on failures, timeout handling, and comprehensive error management.
Integration patterns show request-response for synchronous calls, run-a-job for asynchronous tasks with completion callbacks, wait-for-callback enabling human approval or external events, and optimized integrations with AWS services.
Workflow types include Standard workflows for long-running (up to 1 year), exactly-once execution, detailed execution history, and Express workflows for high-volume (up to 5 minutes), at-least-once or exactly-once, and lower cost.
Service integrations coordinate Lambda functions, ECS tasks, Batch jobs, DynamoDB operations, SNS/SQS messaging, and 200+ AWS services directly.
Use cases show order processing workflows, ETL pipelines, batch job orchestration, human approval workflows, microservices coordination, and complex business processes.
Monitoring capabilities provide visual workflow execution, detailed execution history, CloudWatch integration, X-Ray tracing, and comprehensive observability.
Best practices recommend using for complex workflows, implementing proper error handling, choosing appropriate workflow type, monitoring execution metrics, testing failure scenarios, optimizing for cost, and documenting state machines.
Why other options are incorrect:
A) Lambda executes individual functions, 15-minute timeout, no built-in orchestration, requires custom coordination, and insufficient for long workflows.
C) SQS provides message queuing, no workflow orchestration, no state management, requires custom logic, and serves messaging not orchestration purpose.
D) EventBridge routes events, enables event-driven architecture, no workflow state management, simpler event routing, and Step Functions better for complex workflows.
Question 118
A developer must implement blue-green deployment for ECS services. Which feature enables this pattern?
A) Task definitions only
B) CodeDeploy with ECS
C) CloudFormation stack updates
D) Manual container replacement
Answer: B
Explanation:
Container deployment strategies require automated traffic management. CodeDeploy with ECS automates blue-green deployments, manages traffic shifting between task sets, monitors deployment health, enables automatic rollback, integrates with load balancers, and provides production-grade container deployment.
CodeDeploy creates replacement task set (green), registers with load balancer, shifts traffic from original (blue) to new task set, monitors CloudWatch alarms, automatically rolls back on issues, and maintains zero-downtime deployment.
Deployment process shows creating new task set with updated definition, running green environment alongside blue, performing health checks, shifting traffic gradually or all-at-once, monitoring for issues, and completing or rolling back automatically.
Traffic shifting methods include Canary deployment with specified percentage, Linear deployment with gradual increases, All-at-once for immediate switch, and configurable time intervals between shifts.
Load balancer integration uses Application Load Balancer with target groups, creates test listener for validation, manages target group weights, shifts production traffic, and maintains both environments during deployment.
Health checks monitor ECS service health, evaluate CloudWatch alarms, track deployment success rate, detect task failures, and trigger automatic rollback when thresholds breached.
Rollback capabilities show automatic rollback on alarm triggers, manual rollback option, instant traffic redirection, maintaining blue environment for safety, and minimal downtime on issues.
Deployment configuration defines traffic shift pattern, sets alarm thresholds, configures rollback behavior, specifies health check parameters, and customizes for requirements.
Best practices recommend testing in non-production first, setting appropriate CloudWatch alarms, monitoring during deployment, maintaining capacity for both environments, implementing comprehensive health checks, documenting rollback procedures, and automating through CI/CD.
Why other options are incorrect:
A) Task definitions specify container configuration, don’t provide deployment orchestration, no traffic management, and require deployment mechanism like CodeDeploy.
C) CloudFormation manages infrastructure, supports blue-green at stack level, more manual orchestration, doesn’t provide automated traffic shifting, and CodeDeploy specialized for this pattern.
D) Manual container replacement error-prone, no automated traffic management, requires downtime, lacks rollback automation, and violates deployment best practices.
Question 119
A developer needs to implement custom metrics for application monitoring. Which CloudWatch feature enables this?
A) CloudWatch Logs only
B) PutMetricData API
C) X-Ray segments
D) CloudTrail events
Answer: B
Explanation:
Application-specific monitoring requires custom metric publishing. PutMetricData API enables sending custom metrics to CloudWatch, tracks business metrics, publishes application KPIs, creates custom dashboards, enables alarms on custom data, and provides comprehensive application monitoring.
PutMetricData allows applications publishing metrics programmatically, defining metric names and namespaces, specifying dimensions for filtering, providing values and timestamps, and enabling custom monitoring beyond default AWS metrics.
Metric components include namespace organizing related metrics, metric name identifying specific measurement, dimensions for filtering (key-value pairs), value and unit for measurement, and timestamp for data point.
Publishing patterns show synchronous publishing for critical metrics, asynchronous for non-critical, batching for efficiency (up to 20 metrics per request), high-resolution metrics (1-second granularity), and standard resolution (1-minute).
Common use cases track business KPIs, monitor user activity, measure transaction volumes, track application errors, monitor queue lengths, and publish custom performance metrics.
Metric math enables creating derived metrics, calculating rates and percentages, comparing across dimensions, aggregating multiple metrics, and building complex calculations.
Alarm integration creates alarms on custom metrics, triggers notifications, implements auto-scaling, automates responses, and enables proactive monitoring.
Cost considerations show first 10 custom metrics free, charges for additional metrics, API call costs, storage costs, and balancing monitoring needs against cost.
Best practices recommend using meaningful namespaces, consistent naming conventions, appropriate dimensions, publishing regularly, avoiding too many unique dimension combinations, monitoring costs, and documenting metrics.
Why other options are incorrect:
A) CloudWatch Logs stores log data, can extract metrics with filters, requires additional configuration, less direct than PutMetricData, and logs serve different primary purpose.
C) X-Ray segments provide distributed tracing, capture request paths, don’t create CloudWatch metrics directly, and serve tracing not metrics purpose.
D) CloudTrail records API calls, audit trail, not application metrics, tracks management events, and serves governance not application monitoring.
Question 120
A developer must implement CORS for API Gateway to allow browser requests. Which configuration is required?
A) Lambda function only
B) Enable CORS on API Gateway methods
C) CloudFront distribution
D) S3 bucket policy
Answer: B
Explanation:
Browser-based API access requires CORS configuration. Enabling CORS on API Gateway methods allows cross-origin requests, adds necessary headers, handles preflight OPTIONS requests, permits browser access from different domains, implements web security standards, and enables modern web applications.
CORS (Cross-Origin Resource Sharing) enables web browsers making requests to different domain than hosting page, requires server configuration, implements security headers, handles OPTIONS preflight, and permits controlled cross-origin access.
CORS headers include Access-Control-Allow-Origin specifying allowed domains, Access-Control-Allow-Methods listing permitted HTTP methods, Access-Control-Allow-Headers defining acceptable request headers, Access-Control-Allow-Credentials enabling cookies, and Access-Control-Max-Age caching preflight results.
Preflight requests show browsers sending OPTIONS request before actual request, checking if cross-origin allowed, receiving CORS headers in response, proceeding with actual request if permitted, and caching preflight results.
API Gateway configuration enables CORS through console or API, automatically creates OPTIONS method, returns required headers, integrates with mock integration, and simplifies CORS setup.
Integration response requires backend (Lambda) returning CORS headers, headers must match API Gateway configuration, consistent header values, enabling cookies if needed, and proper origin validation.
Common configurations allow specific origins for security, wildcard (*) for public APIs, multiple origins with logic, credentialed requests with specific origins, and appropriate methods (GET, POST, etc.).
Troubleshooting checks OPTIONS method exists, validates header consistency, ensures origin matches exactly, verifies method allowed, inspects browser console, and tests with different browsers.
Best practices recommend specifying exact origins when possible, avoiding wildcards in production, enabling only necessary methods, implementing origin validation, testing thoroughly in browsers, documenting CORS configuration, and considering security implications.
Why other options are incorrect:
A) Lambda functions can return headers, but API Gateway must handle OPTIONS preflight, both required for complete CORS, and API Gateway configuration essential.
C) CloudFront can serve API, provides edge caching, doesn’t eliminate CORS need, API Gateway still requires configuration, and serves different purpose.
D) S3 bucket policy controls bucket access, S3 has separate CORS configuration, not relevant for API Gateway CORS, and serves static hosting purpose.
