Amazon AWS Certified Developer — Associate DVA-C02 Exam Dumps and Practice Test Questions Set3 Q31-45

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Question 31: 

Which DynamoDB capacity mode automatically scales based on traffic?

A) Provisioned capacity

B) On-demand capacity

C) Reserved capacity

D) Burst capacity

Correct Answer: B

Explanation:

DynamoDB on-demand capacity mode automatically scales to accommodate application traffic without capacity planning or provisioning. This mode instantly handles thousands of requests per second, automatically adjusting to workload increases or decreases. On-demand eliminates the need to predict capacity requirements, making it ideal for unpredictable workloads, new applications without traffic history, or applications with sporadic usage patterns.

With on-demand mode, you pay per request rather than provisioning specific read and write capacity units. DynamoDB charges for actual read and write requests consumed, with separate pricing for standard and transactional operations. This pricing model simplifies cost management for variable workloads since you only pay for what you use without over-provisioning capacity or risking throttling from under-provisioning.

On-demand mode handles traffic spikes gracefully by instantly scaling to accommodate demand. Tables can handle up to double their previous peak traffic within 30 minutes, and DynamoDB continues scaling as long as traffic increases gradually. This automatic scaling eliminates manual intervention during traffic surges, though sudden extreme spikes beyond double the previous peak might experience brief throttling while DynamoDB allocates additional capacity.

Provisioned capacity mode requires specifying read and write capacity units, with optional auto-scaling to adjust capacity based on utilization. Reserved capacity provides cost savings for provisioned mode by committing to baseline throughput for extended periods. Burst capacity temporarily allows exceeding provisioned limits using accumulated unused capacity. Only on-demand mode provides truly automatic, unlimited scaling without capacity specifications.

Switching between on-demand and provisioned modes is possible but limited to once per 24 hours per table. Choose on-demand for unpredictable workloads, applications with low traffic, or when operational simplicity outweighs potential cost savings from provisioned capacity. Use provisioned mode for predictable, consistent traffic where auto-scaling and reserved capacity provide cost optimization. Monitoring CloudWatch metrics helps evaluate mode effectiveness and identify opportunities for optimization based on actual usage patterns.

Question 32: 

What is the purpose of Lambda dead letter queues?

A) Store successful invocations

B) Capture failed asynchronous invocations

C) Queue pending invocations

D) Backup function code

Correct Answer: B

Explanation:

Lambda dead letter queues (DLQs) capture failed asynchronous invocations after exhausting all retry attempts, preventing message loss and enabling error investigation. When Lambda invokes functions asynchronously through services like S3, SNS, or EventBridge, failures trigger automatic retries. After the maximum retry attempts (twice by default), Lambda sends event details to the configured DLQ for later analysis or reprocessing.

DLQs can be either SQS queues or SNS topics, depending on your error handling strategy. SQS queues store failed events for manual inspection, automated reprocessing, or alerting systems. SNS topics enable notifications to multiple subscribers, triggering alerts, logging systems, or remediation workflows. Configuring a DLQ requires granting Lambda permission to send messages to the destination through the function’s execution role.

Failed events in DLQs contain the original event payload, error information, and metadata about the invocation. This information enables root cause analysis, identifying systematic errors versus transient failures. Processing DLQ messages might involve fixing data issues, updating function code, adjusting permissions, or manually reprocessing after resolving underlying problems. Monitoring DLQ message counts alerts teams to recurring failures requiring attention.

Lambda doesn’t use DLQs for storing successful invocations or queuing pending invocations. Successful executions simply complete without DLQ interaction. Pending invocations are handled by Lambda’s internal queuing for asynchronous invocations or the event source for synchronous invocations. DLQs specifically address failure scenarios in asynchronous invocation patterns. Lambda doesn’t backup function code through DLQs; version control and deployment pipelines manage code backups.

DLQs only apply to asynchronous invocations. Synchronous invocations return errors directly to callers without retry logic or DLQ processing. Stream-based event sources like Kinesis and DynamoDB Streams have different failure handling using bisect on error and retry behavior configurations. Understanding invocation types and corresponding error handling mechanisms ensures appropriate failure management for each use case, maintaining system reliability and preventing data loss during error conditions.

Question 33: 

Which AWS service provides API authentication using OAuth 2.0 and OpenID Connect?

A) AWS IAM

B) Amazon Cognito

C) AWS STS

D) AWS Directory Service

Correct Answer: B

Explanation:

Amazon Cognito provides user authentication and authorization for web and mobile applications, supporting OAuth 2.0 and OpenID Connect (OIDC) standard protocols. Cognito offers two main components: User Pools for user directory and authentication, and Identity Pools for granting AWS resource access. User Pools handle user registration, sign-in, multi-factor authentication, and integration with social identity providers like Facebook, Google, and Amazon, plus enterprise identity providers via SAML 2.0.

Cognito User Pools issue JSON Web Tokens (JWTs) after successful authentication, including ID tokens containing user identity information, access tokens for API authorization, and refresh tokens for obtaining new tokens without re-authentication. These tokens follow standard OAuth 2.0 and OIDC specifications, enabling interoperability with various applications and libraries. API Gateway natively integrates with Cognito User Pools, automatically validating JWTs and authorizing API requests.

Identity Pools provide temporary AWS credentials to authenticated or unauthenticated users through AWS Security Token Service (STS), enabling direct access to AWS resources like S3, DynamoDB, or Lambda. Identity Pools support multiple authentication providers including Cognito User Pools, social providers, SAML providers, and custom authentication systems. This flexibility allows building applications with diverse authentication requirements while maintaining consistent AWS resource access control.

AWS IAM manages AWS resource access for AWS accounts, services, and applications but doesn’t provide user authentication for application end-users. AWS STS generates temporary credentials but requires another authentication mechanism. AWS Directory Service provides managed Active Directory for enterprise applications but doesn’t offer OAuth 2.0 or OIDC protocols. Cognito specifically addresses application authentication needs with modern standard protocols.

Cognito simplifies implementing secure authentication, eliminating custom authentication system development and maintenance. Built-in features include password policies, account verification via email or SMS, forgot password flows, customizable UI for hosted authentication pages, and Lambda triggers for customizing authentication flows. CloudWatch Logs capture authentication events for monitoring and security analysis. Using Cognito with API Gateway creates secure, scalable APIs with minimal authentication code in Lambda functions.

Question 34: 

What is the maximum size of an SQS message?

A) 64 KB

B) 128 KB

C) 256 KB

D) 512 KB

Correct Answer: C

Explanation:

Amazon SQS supports messages up to 256 KB in size, sufficient for most messaging scenarios including JSON payloads, XML documents, and small binary data. This limit applies to the message body itself, with message attributes consuming additional space counted separately. Understanding message size limits is essential for designing message-driven architectures and avoiding size-related errors during message publishing.

When applications need to send larger payloads, the SQS Extended Client Library for Java provides a solution by automatically storing message bodies in Amazon S3 and sending only references through SQS. This pattern supports payloads up to 2 GB, combining SQS’s reliable message delivery with S3’s large object storage capabilities. The library handles uploading to S3 during message sending and downloading during message receiving transparently.

Message size affects SQS pricing and throughput. SQS bills in 64 KB chunks, so a 256 KB message consumes four billing units. Larger messages increase costs and reduce effective throughput since each message transfer consumes more bandwidth and processing time. Optimizing message size through compression or storing large content externally improves efficiency and reduces costs.

SQS message attributes allow adding metadata without modifying message bodies, supporting up to 10 attributes with combined size up to 256 KB including names, types, and values. Attributes enable message routing, filtering, and processing decisions without parsing message bodies. When designing message formats, consider separating routing information into attributes for efficiency.

For applications requiring larger message sizes natively, consider alternative services like Amazon Kinesis Data Streams which supports records up to 1 MB, or Amazon SNS which supports messages up to 256 KB with similar S3 extension capabilities. Evaluate tradeoffs between message size, throughput requirements, ordering guarantees, and delivery semantics when selecting messaging services. Implementing proper error handling for size limit exceptions prevents message publishing failures and ensures application reliability.

Question 35: 

Which Lambda permission statement allows S3 to invoke a function?

A) Execution role policy

B) Resource-based policy

C) IAM user policy

D) Bucket policy

Correct Answer: B

Explanation:

Lambda resource-based policies grant permissions for AWS services, other accounts, or applications to invoke functions. When S3 needs to invoke a Lambda function in response to bucket events, a resource-based policy statement on the function grants S3 this permission. This policy type is distinct from execution roles, which grant the function permission to access AWS services during execution.

Resource-based policies are attached directly to Lambda functions through the AddPermission API, CLI, or CloudFormation. A typical policy statement specifying S3 as the principal includes the function ARN, principal service (s3.amazonaws.com), source ARN identifying the specific S3 bucket, and allowed action (lambda:InvokeFunction). This configuration ensures only the specified bucket can invoke the function, preventing unauthorized invocations from other buckets.

Creating S3 event notifications automatically adds necessary resource-based policy statements when configured through the S3 console or CloudFormation. Manual configuration through CLI or API requires explicitly adding permission statements before configuring event notifications, otherwise invocations fail with permission errors. Verifying policy statements through the Lambda console or get-policy API command confirms correct configuration.

Execution role policies grant the Lambda function permissions to access other AWS services like DynamoDB, S3, or CloudWatch during execution, flowing in the opposite direction from resource-based policies. IAM user policies control human user permissions, not service-to-service authorization. Bucket policies control access to S3 objects but don’t grant S3 permission to invoke Lambda functions. Understanding these policy types and their purposes ensures correct permission configuration for serverless applications.

Cross-account invocations also use resource-based policies, allowing Lambda functions in other AWS accounts to invoke your functions. Conditions in policy statements restrict invocations based on source ARN, source account, or other context information, enhancing security. Regularly reviewing resource-based policies through IAM Access Analyzer identifies overly permissive configurations. Proper permission configuration prevents security issues while enabling necessary service integrations in event-driven architectures.

Question 36: 

What is the purpose of CloudFormation stack outputs?

A) Delete resources

B) Share values between stacks

C) Validate templates

D) Monitor resources

Correct Answer: B

Explanation:

CloudFormation stack outputs export values from stacks, enabling resource sharing across multiple stacks without tight coupling. Outputs typically expose resource identifiers like ARNs, IDs, endpoints, or connection strings that other stacks need for references. This modular approach allows building complex infrastructure from smaller, manageable stacks with clear dependencies and interfaces between them.

Output sections in CloudFormation templates define exported values using the Export property with unique names within a region. Other stacks import these values using the Fn::ImportValue intrinsic function, creating cross-stack references. For example, a networking stack might export VPC and subnet IDs that application stacks import for resource placement. This pattern promotes reusability and separation of concerns in infrastructure management.

Cross-stack references create dependencies preventing deletion of exporting stacks while importing stacks exist. CloudFormation enforces these dependencies, requiring cleanup in proper order. This protection prevents accidentally deleting shared resources still in use. However, it also means updating exported values requires coordination since changes might impact all importing stacks simultaneously.

Outputs also provide convenient access to important resource properties through the CloudFormation console, CLI, or API without querying individual resources. Deployment automation scripts often retrieve outputs for integration testing, configuration generation, or deployment verification. Outputs complement stack parameters, which pass values into stacks, while outputs expose values from stacks.

Stack outputs don’t delete resources, validate templates, or monitor resources directly. Deletion policies control resource cleanup behavior. Template validation uses validate-template command or linting tools. Monitoring uses CloudWatch, not outputs. Outputs specifically enable value sharing and cross-stack communication. Best practices include documenting output purposes, using consistent naming conventions, avoiding unnecessary exports that create coupling, and considering AWS Systems Manager Parameter Store for more dynamic value sharing across accounts or regions where cross-stack references don’t work.

Question 37: 

Which API Gateway caching location stores responses temporarily?

A) CloudFront

B) Client browser

C) API Gateway cache

D) Lambda function memory

Correct Answer: C

Explanation:

API Gateway provides built-in caching capabilities that store endpoint responses temporarily within the API Gateway infrastructure, reducing latency and backend load for repeated identical requests. When caching is enabled for a stage, API Gateway checks the cache for responses matching request parameters before invoking the backend. Cache hits return responses immediately without backend invocation, improving performance and reducing costs.

Cache configuration specifies cache capacity from 0.5 GB to 237 GB, affecting how many responses the cache stores. Cache entries have configurable time-to-live (TTL) values from 0 to 3600 seconds, determining how long responses remain cached. After TTL expiration, the next request invokes the backend to refresh cached data. Different methods within an API can have different cache settings, enabling fine-grained control over caching behavior.

Cache keys determine cache uniqueness, typically incorporating request paths, query strings, and headers. Configuring appropriate cache keys ensures different requests don’t incorrectly receive cached responses intended for other requests. For example, user-specific data should include user identifiers in cache keys, while public data might cache broadly. Encryption protects cached data, and per-client cache invalidation enables users to force cache refreshes through special headers.

CloudFront provides edge caching for APIs when used with API Gateway regional endpoints, caching responses at edge locations worldwide. However, this is separate from API Gateway’s built-in cache. Client browser caching depends on Cache-Control headers in responses. Lambda function memory persists across invocations within execution contexts but doesn’t provide request-level caching across different clients. API Gateway cache specifically addresses API-level response caching.

Monitoring cache hit rates through CloudWatch metrics evaluates caching effectiveness. High hit rates indicate effective caching reducing backend load. Low hit rates might indicate inappropriate TTL values, highly dynamic data, or cache keys causing excessive fragmentation. Invalidating cache entries manually through flush-stage-cache API command or per-method invalidation helps during deployments or data updates. Balancing cache effectiveness with data freshness requirements optimizes API performance while maintaining acceptable staleness levels.

Question 38: 

What is the minimum memory allocation for Lambda functions?

A) 64 MB

B) 128 MB

C) 256 MB

D) 512 MB

Correct Answer: B

Explanation:

AWS Lambda supports memory allocations ranging from 128 MB minimum to 10,240 MB maximum, configurable in 1 MB increments. This range accommodates diverse workload requirements from lightweight functions processing simple events to memory-intensive applications handling large datasets or complex computations. The minimum 128 MB allocation provides sufficient memory for basic operations while keeping costs minimal for simple functions.

Memory allocation directly impacts function performance and cost. As mentioned previously, CPU power scales proportionally with memory, meaning 128 MB allocations receive relatively low CPU allocation suitable for I/O-bound operations but potentially slow for CPU-intensive tasks. Functions that primarily wait for external services like database queries, API calls, or S3 operations often perform adequately with minimal memory allocations.

Choosing optimal memory requires balancing performance and cost. While lower memory reduces per-GB-second costs, slower execution from limited CPU might increase total duration, potentially increasing overall cost. Conversely, over-provisioning memory wastes resources and increases costs without performance benefits. Performance testing across different memory configurations identifies the sweet spot where execution time and cost optimize together.

Lambda bills based on GB-seconds, calculated by multiplying allocated memory by execution duration. A function configured with 128 MB running for 1 second consumes 0.125 GB-seconds, while the same function with 512 MB consumes 0.5 GB-seconds. However, if higher memory reduces execution time proportionally, total cost might remain similar or even decrease while performance improves significantly.

CloudWatch Logs report actual memory usage during function execution, helping identify under-utilized or over-utilized allocations. Functions consistently using far less than allocated memory are candidates for reduction. Functions approaching memory limits risk out-of-memory errors requiring increased allocation. Lambda Power Tuning automates testing various memory configurations and recommends optimal settings based on execution profiles. Starting with 512-1024 MB for general functions provides reasonable performance for testing before optimization through systematic performance analysis.

Question 39: 

Which service enables running containers without managing servers?

A) Amazon ECS

B) AWS Fargate

C) Amazon EKS

D) AWS Batch

Correct Answer: B

Explanation:

AWS Fargate is a serverless compute engine for containers that eliminates infrastructure management when running Docker containers. Fargate works with both Amazon ECS (Elastic Container Service) and Amazon EKS (Elastic Kubernetes Service), handling server provisioning, scaling, and management automatically. Developers define container images, CPU and memory requirements, networking, and IAM policies, while Fargate handles all underlying infrastructure.

Fargate simplifies container operations by removing cluster management responsibilities. Unlike traditional ECS or EKS deployments on EC2 instances requiring capacity planning, patching, and scaling infrastructure, Fargate automatically provisions right-sized compute resources for each task. This serverless approach aligns container operations with modern cloud-native practices, paying only for resources consumed without managing underlying instances.

Each Fargate task runs in isolated compute environments with dedicated CPU, memory, and elastic network interfaces. This isolation enhances security by preventing shared infrastructure vulnerabilities. Fargate integrates with VPC networking, security groups, and IAM roles, providing comprehensive security controls. Tasks can run in public or private subnets, access AWS services, and communicate with external systems through standard networking constructs.

Amazon ECS is a container orchestration service but requires choosing between EC2 launch type (managing instances) or Fargate launch type (serverless). Amazon EKS runs Kubernetes clusters but similarly supports both EC2 and Fargate launch types. AWS Batch processes batch computing workloads, optionally using Fargate for serverless execution but isn’t exclusively serverless. Only Fargate specifically provides serverless container execution as its core purpose.

Fargate pricing is based on vCPU and memory resources consumed per second with no minimum duration charges. Task definitions specify resource requirements, and Fargate provisions precisely those amounts. This granular pricing eliminates over-provisioning costs common with instance-based deployments. Fargate Spot offers cost savings using spare capacity for fault-tolerant workloads. Understanding ECS or EKS task definitions, networking configurations, and Fargate platform versions ensures successful serverless container deployments.

Question 40: 

What does eventual consistency mean in DynamoDB?

A) Data is immediately consistent

B) Data becomes consistent over time

C) Data is never consistent

D) Data requires manual synchronization

Correct Answer: B

Explanation:

Eventual consistency in DynamoDB means that after a successful write operation, reads might not immediately reflect the most recent write but will become consistent given sufficient time. DynamoDB achieves high availability and partition tolerance by replicating data across multiple servers within a region. After a write succeeds, changes propagate asynchronously to all replicas, typically completing within one second under normal conditions.

This consistency model provides performance benefits and higher throughput compared to strongly consistent reads. Eventually consistent reads access any available replica without coordination overhead, enabling faster response times and consuming fewer resources. For many applications, brief inconsistency windows are acceptable trade-offs for improved performance, especially when the application logic handles temporary stale data gracefully.

Understanding eventual consistency helps design appropriate application logic. Consider use cases like social media feed updates, product catalogs, or content management systems where displaying slightly outdated information briefly doesn’t significantly impact user experience. Applications can implement client-side strategies like optimistic UI updates, showing users their changes immediately while eventual consistency completes in the background.

Immediate consistency refers to strongly consistent reads where DynamoDB coordinates across replicas to ensure the latest data returns. Data never being consistent or requiring manual synchronization doesn’t describe DynamoDB’s behavior; consistency automatically occurs through replication without intervention. Eventual consistency is an intentional design choice enabling distributed system scalability while maintaining practical consistency guarantees for most applications.

DynamoDB’s eventual consistency aligns with the CAP theorem, prioritizing availability and partition tolerance while accepting eventual consistency rather than immediate consistency. For critical operations requiring reading immediately after writing, applications should explicitly request strongly consistent reads using the ConsistentRead parameter. Monitoring application behavior and understanding access patterns determines when strong consistency is necessary versus when eventual consistency suffices, enabling optimal performance while meeting application requirements.

Question 41: 

Which Lambda feature enables communication with resources in a private subnet?

A) Environment variables

B) VPC configuration

C) Execution role

D) Layers

Correct Answer: B

Explanation:

Lambda VPC configuration enables functions to access resources within Virtual Private Clouds, including databases in private subnets, internal APIs, or other AWS resources without public endpoints. When VPC access is configured, Lambda connects function instances to specified subnets using elastic network interfaces (ENIs), allowing functions to communicate with VPC resources as if they were running inside the VPC.

Configuring VPC access requires specifying subnet IDs and security group IDs. Lambda creates ENIs in specified subnets, consuming private IP addresses. Security groups control inbound and outbound traffic, determining which resources functions can access and which traffic functions receive. Functions can access resources in any subnet within the same VPC through route tables, not just subnets specified in configuration.

VPC-enabled functions can access internet resources through NAT gateways in public subnets or VPC endpoints for AWS services, avoiding internet traffic. Without NAT gateways or VPC endpoints, VPC-configured functions cannot reach the internet or AWS service public endpoints. This behavior commonly causes issues when functions need both VPC resource access and internet connectivity, requiring proper network architecture.

Lambda manages ENI creation and deletion automatically, but initial cold starts for VPC-enabled functions historically experienced delays during ENI creation. Recent improvements eliminated this penalty through Hyperplane ENI management, which shares ENIs across function executions. Modern VPC-enabled Lambda functions start as quickly as non-VPC functions while maintaining full VPC connectivity.

Environment variables store configuration but don’t enable VPC access. Execution roles grant AWS API permissions, not network connectivity. Layers share code across functions. Only VPC configuration provides network-level connectivity to VPC resources. Best practices include using VPC endpoints for AWS services to avoid NAT gateway costs, configuring security groups with least privilege, deploying functions in multiple availability zones for resilience, and carefully managing VPC resource quotas like available IP addresses in subnets.

Question 42: 

What is the purpose of API Gateway stages?

A) Version control

B) Environment separation

C) Load balancing

D) Authentication

Correct Answer: B

Explanation:

API Gateway stages represent different environments or versions of an API deployment, enabling separation between development, testing, and production environments within a single API definition. Each stage has unique configurations including endpoint URLs, throttle settings, caching, logging, and stage variables, allowing consistent API definitions to behave differently across environments without duplicating API resources.

Deploying an API to a stage creates a snapshot of that API’s configuration at deployment time. Stages point to specific deployments, and multiple stages can reference different deployments or the same deployment with varying configurations. Common stage names include dev, test, staging, and prod, though any naming convention works. Each stage receives a unique invoke URL enabling environment-specific client configuration.

Stage variables function like environment variables, storing configuration values varying across stages. For example, backend endpoint URLs might differ between development and production, stored as stage variables that integration requests reference. Lambda function aliases are often combined with stage variables, enabling different stages to invoke different Lambda versions without changing API configuration. This pattern supports safe deployment practices and gradual rollouts.

While stages enable versioning strategies, they primarily address environment separation rather than pure version control. Load balancing is handled by AWS infrastructure automatically, not through stages. Authentication uses authorizers, API keys, or other mechanisms configured per method or stage but isn’t the primary stage purpose. Stages specifically enable managing multiple environments efficiently within single API definitions.

Canary deployments use stages to gradually shift traffic from one deployment version to another, reducing risk during updates. A stage can direct a percentage of traffic to a canary deployment while sending the remainder to the base deployment, enabling testing new versions with production traffic before full rollout. CloudWatch metrics track stage-level performance, errors, and throttling, facilitating environment-specific monitoring. Stage configuration through infrastructure as code with CloudFormation or SAM ensures consistent deployment practices and environment parity.

Question 43: 

Which DynamoDB feature enables creating alternate query patterns?

A) Partitions

B) Sort keys

C) Secondary indexes

D) Streams

Correct Answer: C

Explanation:

DynamoDB secondary indexes enable querying data using attributes other than the primary key, providing alternate access patterns without scanning entire tables. Indexes contain projections of table attributes, organized by alternate keys for efficient queries. DynamoDB supports two index types: Global Secondary Indexes (GSI) spanning all partitions, and Local Secondary Indexes (LSI) colocated with base table partitions sharing the same partition key.

Global Secondary Indexes define completely different partition and optional sort keys from the base table, enabling queries on any attributes. GSIs have their own provisioned throughput separate from base tables in provisioned mode or scale independently in on-demand mode. Creating GSIs doesn’t affect base table performance since they’re separate data structures. GSIs support eventual consistency only, reflecting base table changes asynchronously.

Local Secondary Indexes share the base table’s partition key but define alternate sort keys, enabling different query patterns within partition key groupings. LSIs support both eventually consistent and strongly consistent reads. However, LSIs must be defined during table creation and cannot be added later, unlike GSIs which support dynamic creation. LSI items consume space in the same partition as base table items, subject to 10 GB per partition key limit.

Partitions are internal data distribution mechanisms, not user-facing query features. Sort keys enable range queries within partitions but don’t provide alternate query patterns on different attributes. Streams capture changes but don’t enable querying. Secondary indexes specifically address querying data by non-primary-key attributes efficiently without full table scans.

Effective DynamoDB schema design identifies access patterns during planning and creates appropriate indexes supporting those patterns. Over-indexing wastes storage and write capacity, while under-indexing forces inefficient scans. Sparse indexes, containing entries only for items with index key attributes, efficiently support queries for uncommon attribute combinations. Projection types control which attributes indexes include, balancing query flexibility with storage costs. Understanding index patterns and limitations enables designing performant DynamoDB applications supporting diverse query requirements.

Question 44: 

What is the maximum duration for Step Functions Standard workflows?

A) 15 minutes

B) 1 hour

C) 1 year

D) 5 minutes

Correct Answer: C

Explanation:

AWS Step Functions Standard Workflows support executions lasting up to one year, enabling long-running workflows that would be impractical with Lambda’s 15-minute maximum duration. This extended duration makes Step Functions ideal for orchestrating complex business processes, human approval workflows, long-running data processing pipelines, or any process requiring extended execution times with durable state management.

Standard Workflows provide exactly-once execution semantics, meaning each state transition executes precisely once even if failures occur. Step Functions records execution history, capturing every state transition, input, output, and timing information. This detailed history supports auditing, debugging, and process analysis. Executions remain visible and queryable for 90 days after completion, providing comprehensive operational insight.

Step Functions integrates with over 200 AWS services through direct service integrations, eliminating the need for intermediary Lambda functions for simple service calls. For example, workflows can directly invoke DynamoDB operations, start ECS tasks, publish SNS messages, or invoke other Step Functions without Lambda glue code. This direct integration reduces cost, latency, and complexity while maintaining clear workflow definitions.

Step Functions also offers Express Workflows supporting durations up to 5 minutes, designed for high-volume, short-duration workloads like streaming data processing or IoT data ingestion. Express Workflows provide at-least-once execution semantics with lower costs than Standard Workflows but without execution history recording. Choosing between Standard and Express depends on duration requirements, execution guarantees needed, and budget considerations.

Standard Workflows charge per state transition, making them cost-effective for long-running workflows with relatively few state changes. Complex workflows with many state transitions might incur significant costs. Understanding pricing helps design efficient workflows using appropriate patterns like parallel processing, wait states for delays instead of polling, and Map states for batch processing. Step Functions visual workflow editor simplifies building and understanding complex orchestrations while Amazon States Language provides programmatic workflow definitions.

Question 45: 

Which service provides DNS management for domain names?

A) CloudFront

B) Route 53

C) API Gateway

D) Elastic Load Balancing

Correct Answer: B

Explanation:

Amazon Route 53 is AWS’s highly available and scalable Domain Name System (DNS) web service, managing domain names and routing internet traffic to infrastructure. Route 53 performs three main functions: domain registration, DNS routing, and health checking. Understanding Route 53 capabilities is essential for developers deploying applications requiring custom domains, traffic management, or high availability routing strategies.

Route 53 DNS routing policies enable sophisticated traffic management beyond simple DNS. Simple routing returns single resources, weighted routing distributes traffic across multiple resources based on assigned weights, latency-based routing directs users to lowest-latency endpoints, failover routing provides active-passive failover, geolocation routing responds based on user geographic location, geoproximity routing considers resource and user locations, and multivalue answer routing returns multiple healthy resource IPs.

Health checks monitor endpoint availability, automatically removing unhealthy resources from DNS responses. Health checks support HTTP, HTTPS, and TCP protocols, checking endpoints at configurable intervals. Integrating health checks with routing policies enables automatic failover, traffic shifting away from unhealthy resources to healthy ones without manual intervention. CloudWatch alarms can trigger based on health check status, enabling proactive incident response.

CloudFront distributes content globally via edge locations but doesn’t manage DNS directly. API Gateway provides API endpoints but requires Route 53 or similar DNS services for custom domain configuration. Elastic Load Balancing distributes traffic across compute resources but operates at the load balancer level, not DNS. Route 53 specifically handles DNS resolution, translating human-readable domain names to IP addresses.

Route 53 integrates seamlessly with other AWS services. Alias records provide free DNS queries for AWS resources like CloudFront distributions, S3 buckets configured as websites, Elastic Load Balancers, and API Gateway custom domains. Traffic flow visual editor simplifies creating complex routing configurations. Private hosted zones enable DNS resolution within VPCs for internal resources. Route 53 resolver manages DNS resolution between VPCs, on-premises networks, and the internet, supporting hybrid cloud architectures with consistent DNS management.