Amazon AWS Certified Developer — Associate DVA-C02 Exam Dumps and Practice Test Questions Set1 Q1-15
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Question 1:
Which AWS service provides a fully managed NoSQL database with single-digit millisecond performance?
A) Amazon RDS
B) Amazon DynamoDB
C) Amazon Redshift
D) Amazon Aurora
Correct Answer: B
Explanation:
Amazon DynamoDB is AWS’s fully managed NoSQL database service designed to deliver single-digit millisecond performance at any scale. This service is ideal for applications requiring consistent, fast performance with seamless scalability. DynamoDB automatically handles hardware provisioning, setup, configuration, replication, software patching, and cluster scaling, allowing developers to focus on application development rather than database management.
DynamoDB supports both key-value and document data models, making it flexible for various application architectures. The service offers features like automatic scaling, built-in security, backup and restore capabilities, and in-memory caching through DynamoDB Accelerator. Its distributed nature ensures high availability and durability by replicating data across multiple Availability Zones within a region.
The performance characteristics of DynamoDB make it particularly suitable for mobile applications, gaming platforms, IoT solutions, and any application requiring low-latency data access. With on-demand and provisioned capacity modes, developers can choose the billing model that best fits their workload patterns. The on-demand mode automatically scales to accommodate traffic, while provisioned mode allows for predictable performance and cost optimization.
Amazon RDS is a relational database service supporting multiple database engines like MySQL, PostgreSQL, and Oracle, but it doesn’t provide the same NoSQL capabilities or consistent single-digit millisecond latency. Amazon Redshift is a data warehousing solution optimized for analytics and complex queries on large datasets, not for low-latency transactional workloads. Amazon Aurora is a MySQL and PostgreSQL-compatible relational database with excellent performance, but it operates within the relational database paradigm rather than NoSQL.
DynamoDB integrates seamlessly with other AWS services like Lambda, API Gateway, and CloudWatch, enabling developers to build serverless applications with minimal operational overhead. Its global tables feature provides multi-region, fully replicated tables for globally distributed applications requiring local read and write access. This makes DynamoDB the optimal choice for applications demanding high performance, scalability, and reliability.
Question 2:
What is the maximum execution duration for an AWS Lambda function?
A) 5 minutes
B) 10 minutes
C) 15 minutes
D) 30 minutes
Correct Answer: C
Explanation:
AWS Lambda functions have a maximum execution duration of 15 minutes per invocation. This timeout limit is a crucial consideration when designing serverless applications and determines whether Lambda is appropriate for specific workloads. The execution timeout can be configured anywhere from 1 second to 900 seconds (15 minutes) depending on your application requirements.
Understanding this limitation is essential for AWS Certified Developer Associates because it influences architectural decisions. Tasks that require longer processing times must be broken down into smaller units, distributed across multiple Lambda invocations, or handled by alternative services like AWS Batch, Amazon ECS, or EC2 instances. For example, processing large files might require splitting the work into chunks that each complete within the 15-minute window.
Lambda’s pricing model charges based on the number of requests and the compute time consumed, measured in GB-seconds. Longer-running functions consume more compute time and incur higher costs, making it important to optimize function execution. Developers should implement efficient code, minimize cold starts, and use appropriate memory allocations to reduce execution time and costs.
The 15-minute limit also affects how you design retry logic and error handling. If a function approaches the timeout, it might be terminated abruptly without completing its work. Implementing proper logging with CloudWatch Logs helps track execution times and identify functions at risk of timing out. Setting alarms for functions approaching their configured timeout enables proactive optimization.
For workflows requiring extended processing, AWS Step Functions provides orchestration capabilities that chain multiple Lambda functions together, effectively bypassing the individual function timeout limitation. This approach maintains the benefits of serverless architecture while handling complex, long-running processes. Additionally, asynchronous invocation patterns can queue work for processing without blocking client applications, improving overall system responsiveness and reliability.
Question 3:
Which DynamoDB feature provides automatic replication across multiple AWS regions?
A) DynamoDB Streams
B) Global Tables
C) Cross-Region Replication
D) Multi-AZ Deployment
Correct Answer: B
Explanation:
DynamoDB Global Tables provide fully managed, multi-region, multi-master database replication, enabling developers to build globally distributed applications with local read and write access in multiple AWS regions. This feature automatically replicates data across selected regions, ensuring low-latency access for users regardless of their geographic location. Global Tables maintain eventual consistency across all replica tables, typically replicating changes within one second.
The implementation of Global Tables is straightforward through the AWS Management Console, CLI, or SDKs. When you create a global table, DynamoDB automatically creates replica tables in your specified regions and manages the replication process. All replicas have identical table schemas, including partition keys, sort keys, and secondary indexes. Applications can read and write to any replica, and DynamoDB handles conflict resolution using last-writer-wins reconciliation.
Global Tables are ideal for applications serving international users, disaster recovery scenarios, and regulatory compliance requirements mandating data residency in specific regions. They eliminate the need for custom replication solutions, reducing operational complexity and development time. The service integrates with DynamoDB’s other features, including auto-scaling, point-in-time recovery, and encryption at rest.
DynamoDB Streams capture item-level modifications but don’t automatically provide cross-region replication. Streams enable event-driven architectures and can trigger Lambda functions, but replication requires additional implementation. Cross-Region Replication isn’t a specific DynamoDB feature name. Multi-AZ Deployment refers to DynamoDB’s standard high availability within a single region, where data is automatically replicated across multiple Availability Zones for durability and fault tolerance, but this doesn’t extend across regions.
When using Global Tables, consider the cost implications of replicating data across regions and the network transfer charges. Monitor replication lag using CloudWatch metrics to ensure acceptable performance. Global Tables support version 2019.11.21 and later, offering improved performance and additional features compared to the original version.
Question 4:
Which service should you use to manage environment variables for Lambda functions securely?
A) AWS Systems Manager Parameter Store
B) Amazon S3
C) AWS CloudFormation
D) Amazon RDS
Correct Answer: A
Explanation:
AWS Systems Manager Parameter Store provides secure, hierarchical storage for configuration data and secrets management, making it the ideal service for managing Lambda function environment variables securely. Parameter Store offers centralized storage for plain-text data like database strings, passwords, and license codes, with optional encryption using AWS Key Management Service for sensitive information. This integration enables Lambda functions to retrieve configuration values at runtime without hardcoding credentials in code.
Parameter Store organizes parameters hierarchically using path-based naming, facilitating logical grouping and access control. For example, you might structure parameters as /production/database/endpoint or /development/api/key. IAM policies control access to specific parameter paths, ensuring functions only access necessary configuration data. This separation of configuration from code improves security, simplifies updates across environments, and supports compliance requirements.
Lambda functions retrieve parameters using the AWS SDK within the function code or through Lambda extensions. Caching retrieved parameters reduces API calls and improves performance. Parameter Store offers Standard and Advanced parameter tiers, with Advanced supporting larger values, parameter policies, and higher throughput. The service integrates seamlessly with other AWS services, including CloudFormation for infrastructure as code deployments.
While Lambda supports environment variables natively, storing sensitive values directly as environment variables poses security risks since they’re visible in the Lambda console and CloudFormation templates. Parameter Store adds an encryption layer and centralized management. AWS Secrets Manager offers similar capabilities with additional features like automatic rotation, but Parameter Store provides a cost-effective solution for many use cases.
Amazon S3 could store configuration files, but it requires additional code to retrieve and parse data, lacking the structured access and encryption features of Parameter Store. CloudFormation manages infrastructure deployment but isn’t designed for runtime configuration management. Amazon RDS is a database service unrelated to configuration management. Using Parameter Store with Lambda follows AWS best practices for secure, maintainable serverless applications.
Question 5:
What HTTP status code indicates a successful POST request that creates a new resource?
A) 200 OK
B) 201 Created
C) 204 No Content
D) 202 Accepted
Correct Answer: B
Explanation:
The HTTP status code 201 Created specifically indicates that a POST request successfully created a new resource on the server. This response code is semantically correct for REST API implementations when a client sends data to create a new entity, such as adding a user, creating an order, or inserting a database record. Understanding proper HTTP status codes is essential for AWS developers building APIs with services like API Gateway and Lambda.
When returning 201 Created, the server should include a Location header specifying the URI of the newly created resource, allowing clients to access it immediately. The response body typically contains a representation of the created resource, including any server-generated identifiers or timestamps. This follows RESTful principles and provides clients with complete information about the operation’s result.
Using 201 instead of 200 OK provides clearer semantic meaning in API responses. While 200 OK indicates general success, it doesn’t specifically convey that a new resource was created. This distinction helps API consumers understand the operation’s outcome and build more robust client applications. Properly implemented status codes improve API usability and align with industry standards expected by developers.
Status code 200 OK represents a generic successful request but doesn’t indicate resource creation. It’s appropriate for successful GET, PUT, or PATCH operations. Code 204 No Content indicates successful processing without returning content, commonly used for DELETE operations or updates where the client doesn’t need the modified resource. Code 202 Accepted means the request was received but processing hasn’t completed, useful for asynchronous operations queued for later processing.
In AWS serverless applications using API Gateway with Lambda proxy integration, developers must explicitly set the statusCode in the Lambda response object. Returning the correct status code improves API compliance with HTTP standards and enhances the developer experience for API consumers. CloudWatch metrics can track status code distributions, helping identify issues and monitor API health.
Question 6:
Which AWS service provides a message queue for decoupling application components?
A) Amazon SNS
B) Amazon SQS
C) Amazon Kinesis
D) AWS Step Functions
Correct Answer: B
Explanation:
Amazon Simple Queue Service (SQS) is AWS’s fully managed message queuing service that enables decoupling of application components by allowing asynchronous communication between distributed systems. SQS stores messages reliably until consuming applications retrieve and process them, eliminating direct dependencies between producers and consumers. This architectural pattern improves scalability, reliability, and maintainability of distributed applications.
SQS offers two queue types: Standard and FIFO (First-In-First-Out). Standard queues provide nearly unlimited throughput, best-effort ordering, and at-least-once delivery, making them suitable for high-volume scenarios where occasional duplicate messages are acceptable. FIFO queues guarantee exactly-once processing and preserve message order, essential for workflows where sequence matters, such as financial transactions or event processing pipelines.
Messages in SQS can contain up to 256 KB of text in any format. For larger payloads, the Extended Client Library for Java enables storing message bodies in S3 while passing references through SQS. Visibility timeout prevents multiple consumers from processing the same message simultaneously by hiding messages during processing. Dead-letter queues capture messages that fail processing repeatedly, enabling troubleshooting and preventing message loss.
Amazon SNS is a pub/sub messaging service for sending notifications to multiple subscribers simultaneously, not a queue for decoupling. Amazon Kinesis handles real-time data streaming for analytics, not message queuing. AWS Step Functions orchestrates workflows but doesn’t provide message queuing capabilities. SQS integrates with Lambda through event source mappings, enabling serverless message processing without managing polling infrastructure.
SQS supports long polling, reducing empty responses and decreasing costs by allowing receive requests to wait for messages to arrive. Message retention ranges from 1 minute to 14 days, with a default of 4 days. Delay queues postpone message delivery, and message timers set per-message delays. These features provide flexible message handling for various application patterns.
Question 7:
What is the default timeout for API Gateway REST APIs?
A) 10 seconds
B) 29 seconds
C) 30 seconds
D) 60 seconds
Correct Answer: B
Explanation:
Amazon API Gateway has a maximum integration timeout of 29 seconds for REST APIs and HTTP APIs. This hard limit means that backend integrations, including Lambda functions, HTTP endpoints, or AWS service integrations, must complete processing and return a response within 29 seconds. Understanding this constraint is critical for designing robust API architectures and choosing appropriate backend services.
The 29-second timeout applies to the entire request-response cycle, including time spent in the backend service and network transmission. If the backend doesn’t respond within this timeframe, API Gateway returns a 504 Gateway Timeout error to the client. This limitation influences architectural decisions, particularly for operations that might exceed this duration, such as large file processing, complex calculations, or extensive database queries.
For operations requiring longer processing times, developers should implement asynchronous patterns. One approach involves API Gateway accepting the request and immediately returning a 202 Accepted status with a job identifier. The backend processes the request asynchronously, and clients poll a separate endpoint or receive notifications through WebSocket APIs, SNS, or SQS when processing completes. This pattern improves user experience and system reliability.
Lambda functions themselves can run up to 15 minutes, but when invoked through API Gateway synchronously, they’re constrained by the 29-second timeout. For longer-running Lambda functions, use asynchronous invocation or Step Functions for orchestration. Consider breaking complex operations into smaller, chained functions or migrating long-running processes to services like ECS, EKS, or Batch.
Optimizing backend performance helps stay within timeout limits. This includes database query optimization, caching frequently accessed data with ElastiCache or DynamoDB Accelerator, using connection pooling for database connections, and minimizing external API calls. CloudWatch metrics track integration latency, helping identify slow endpoints requiring optimization. Implementing proper error handling and timeout management ensures applications gracefully handle timeout scenarios and provide meaningful feedback to users.
Question 8:
Which AWS service provides a managed Docker container registry?
A) Amazon ECS
B) Amazon ECR
C) AWS Fargate
D) Amazon EKS
Correct Answer: B
Explanation:
Amazon Elastic Container Registry (ECR) is AWS’s fully managed Docker container registry that stores, manages, and deploys Docker container images securely. ECR eliminates the need to operate your own container repositories or worry about scaling the underlying infrastructure. It integrates seamlessly with Amazon ECS, Amazon EKS, and AWS Fargate, simplifying the deployment pipeline for containerized applications.
ECR provides secure image storage with encryption at rest using AWS KMS and data transfer encryption using HTTPS. Access control is managed through IAM policies, allowing fine-grained permissions for pushing and pulling images. Repositories can be private or public, with public repositories available through the Amazon ECR Public Gallery for sharing images with the broader community. Image scanning capabilities detect software vulnerabilities in container images, enhancing security posture.
Lifecycle policies in ECR automate image cleanup by removing old or unused images based on rules you define, helping manage storage costs. ECR supports Docker Registry HTTP API V2, making it compatible with standard Docker CLI commands and other container tooling. Cross-region and cross-account replication ensures images are available where needed, reducing latency and improving deployment reliability.
Amazon ECS (Elastic Container Service) is a container orchestration service for running Docker containers but doesn’t provide registry functionality. AWS Fargate is a serverless compute engine for containers that works with ECS and EKS, handling infrastructure management but not image storage. Amazon EKS (Elastic Kubernetes Service) runs Kubernetes clusters but relies on separate registries like ECR for storing images.
ECR integrates with CI/CD pipelines through AWS CodePipeline, CodeBuild, and third-party tools, enabling automated image builds and deployments. Image tags and digests provide versioning and immutability, ensuring consistent deployments. The service charges based on storage used and data transfer, with no additional fees for private repositories. Using ECR simplifies container workflows while maintaining security, scalability, and integration with the broader AWS ecosystem.
Question 9:
Which Lambda feature allows you to allocate more CPU power to your function?
A) Execution timeout
B) Memory allocation
C) Concurrency limit
D) Reserved capacity
Correct Answer: B
Explanation:
In AWS Lambda, CPU power is directly proportional to the amount of memory allocated to a function. When you configure memory allocation between 128 MB and 10,240 MB, Lambda automatically allocates proportional CPU power, network bandwidth, and disk I/O. This design means that increasing memory not only provides more RAM but also makes your function execute faster by providing additional computational resources.
Lambda allocates CPU power linearly relative to memory. A function configured with 1,792 MB receives one full vCPU equivalent, while lower memory allocations receive fractional vCPU. Functions with more than 1,792 MB can access multiple vCPU cores, enabling true parallel processing for multi-threaded applications. This relationship between memory and CPU is unique to Lambda and crucial for performance optimization.
Performance testing helps determine the optimal memory configuration. While increasing memory incurs higher costs per GB-second, faster execution often results in lower overall costs because functions complete quicker. AWS Lambda Power Tuning is an open-source tool that automatically tests different memory configurations and recommends the optimal setting based on cost or performance preferences. Running benchmark tests ensures you’re not over-provisioning or under-provisioning resources.
Understanding this relationship helps optimize both performance and costs. For CPU-intensive workloads like image processing, video transcoding, or complex calculations, allocating more memory significantly improves performance. For I/O-bound operations like database queries or API calls, additional memory might not improve performance since the function waits for external resources.
Execution timeout controls how long a function can run but doesn’t affect CPU power. Concurrency limits control how many function instances run simultaneously. Reserved capacity (Reserved Concurrency) guarantees available concurrency for specific functions. Only memory allocation directly impacts CPU power. Monitoring CloudWatch metrics like duration, memory utilization, and throttles helps identify optimization opportunities and ensures functions have appropriate resource allocations for their workloads.
Question 10:
What is the purpose of AWS X-Ray in serverless applications?
A) Load balancing
B) Distributed tracing
C) Code deployment
D) Access management
Correct Answer: B
Explanation:
AWS X-Ray provides distributed tracing capabilities for serverless and microservices applications, enabling developers to analyze and debug production environments by visualizing request flows across multiple services. X-Ray collects data about requests your application serves, creating service maps that show request paths, latencies, and errors across distributed components including Lambda functions, API Gateway, DynamoDB, and other AWS services.
X-Ray helps identify performance bottlenecks by showing where time is spent during request processing. The service graph visualizes relationships between application components, displaying latency distributions and error rates for each service. Trace details reveal the complete request lifecycle, including time spent in each component, annotations developers add for custom tracking, and metadata about the execution environment.
Implementing X-Ray in Lambda functions requires minimal code changes. Enable tracing in the Lambda configuration, and the runtime automatically captures information about function invocations, including initialization duration, execution duration, and downstream calls to AWS services. The X-Ray SDK provides additional capabilities for custom instrumentation, allowing developers to create subsegments for specific code blocks, add annotations for filtering traces, and capture metadata for debugging.
X-Ray integrates with other AWS services seamlessly. API Gateway automatically creates traces when X-Ray tracing is enabled. DynamoDB, S3, SNS, SQS, and other services appear in service maps when applications interact with them. This comprehensive visibility across the entire request path simplifies troubleshooting complex, distributed applications where issues might span multiple services.
Load balancing is handled by services like Elastic Load Balancing, not X-Ray. Code deployment uses services like CodeDeploy and CodePipeline. Access management relies on IAM. X-Ray specifically focuses on observability and distributed tracing. Analyzing traces helps optimize application performance, understand user experience, identify errors, and validate that applications meet performance requirements. Filtering expressions enable finding specific traces matching criteria like error conditions, slow requests, or particular user segments.
Question 11:
Which DynamoDB read consistency option provides the most up-to-date data?
A) Eventually consistent reads
B) Strongly consistent reads
C) Transactional reads
D) Cached reads
Correct Answer: B
Explanation:
DynamoDB strongly consistent reads guarantee that the returned data reflects all writes that received a successful response prior to the read request. When you perform a strongly consistent read, DynamoDB contacts all replicas storing the item and returns the most recent data, ensuring you always receive the latest committed version. This consistency level is essential for applications where reading immediately after writing must return the updated value.
DynamoDB stores data across multiple Availability Zones for durability and availability. After a successful write, data is stored on multiple replicas, but replication happens asynchronously. Eventually consistent reads might return data from a replica that hasn’t yet received the latest update, potentially serving stale data. This usually occurs within one second, but applications requiring immediate consistency must use strongly consistent reads.
Strongly consistent reads consume twice the read capacity units compared to eventually consistent reads because DynamoDB must coordinate across replicas to ensure data freshness. This increased cost and slightly higher latency are tradeoffs for guaranteed consistency. Developers should evaluate whether their application truly requires strong consistency or if eventual consistency suffices, considering most replicas reflect updates within milliseconds.
Eventually consistent reads are the default behavior and provide better performance and cost efficiency when immediate consistency isn’t critical. Transactional reads ensure atomicity across multiple items in single or multiple tables but don’t relate to consistency levels. Cached reads aren’t a DynamoDB consistency option; caching is implemented separately using DynamoDB Accelerator or application-level caching.
Use cases for strongly consistent reads include financial transactions, inventory management, and scenarios where reading immediately after writing must reflect the update. For analytics, reporting, or applications tolerant of slight delays, eventually consistent reads offer better performance. The GetItem, Query, and Scan operations support the ConsistentRead parameter, allowing per-request consistency control. Understanding these options helps design efficient, cost-effective DynamoDB applications that meet specific consistency requirements.
Question 12:
Which environment variable contains the name of the Lambda function being executed?
A) AWS_LAMBDA_NAME
B) AWS_FUNCTION_NAME
C) LAMBDA_FUNCTION_NAME
D) AWS_LAMBDA_FUNCTION_NAME
Correct Answer: D
Explanation:
AWS Lambda automatically sets several environment variables for all functions, and AWS_LAMBDA_FUNCTION_NAME contains the name of the currently executing Lambda function. This runtime environment variable is consistently available across all supported Lambda runtimes including Node.js, Python, Java, Go, Ruby, and .NET Core. It allows functions to reference their own name programmatically without hardcoding values or relying on deployment parameters, improving maintainability and portability across environments.
Lambda exposes a wide range of built-in environment variables to provide visibility into the execution context. AWS_LAMBDA_FUNCTION_VERSION indicates the published version or alias currently being executed, while AWS_LAMBDA_FUNCTION_MEMORY_SIZE reports the memory allocated to the function in megabytes. AWS_REGION identifies the region where the function is running, which is useful when constructing service clients or resource ARNs dynamically. Similarly, AWS_LAMBDA_LOG_GROUP_NAME and AWS_LAMBDA_LOG_STREAM_NAME point to the CloudWatch Logs group and log stream where the function output is being written. AWS_EXECUTION_ENV identifies the runtime environment, helping developers tailor logic based on language version or application tooling.
These variables enable dynamic, context-aware behavior in Lambda functions. For example, developers can adjust verbosity or log formatting based on function version, generate resource names using function details and region, or enable feature flags based on environment settings. Because these built-in variables are injected by the Lambda runtime itself, no additional IAM permissions are required to access them from within the function code.
In addition to built-in variables, Lambda supports custom environment variables that can be configured through the console, AWS CLI, SDKs, CloudFormation, SAM, or Terraform templates. Custom variables allow developers to store configuration parameters, endpoints, credentials, feature toggles, or deployment metadata. Sensitive values can be encrypted with AWS KMS, but AWS Systems Manager Parameter Store and AWS Secrets Manager are generally recommended for storing highly confidential information due to secure retrieval, rotation, and fine-grained access control.
Using environment variables appropriately helps ensure Lambda functions remain flexible, environment-agnostic, and easier to manage across different stages such as development, testing, and production. Avoiding hardcoded values reduces deployment overhead and promotes best practices for serverless application design. Accessing AWS_LAMBDA_FUNCTION_NAME within code enables enhanced observability, standardized logging formats, distributed tracing correlation, and environment-specific logic that automatically adapts across multiple deployed functions.
Question 13:
What is the maximum size for a Lambda deployment package when uploaded directly?
A) 10 MB
B) 50 MB
C) 100 MB
D) 250 MB
Correct Answer: B
Explanation:
AWS Lambda allows direct upload of deployment packages up to 50 MB (compressed as a .zip file) through the console, CLI, or API. This limit applies when uploading function code directly without using Amazon S3. Understanding deployment package size limits is essential for developers managing Lambda functions, especially those with large dependencies or libraries.
For larger deployment packages, Lambda supports uploading to S3 first, then referencing the S3 object when creating or updating the function. When using S3, the uncompressed deployment package can be up to 250 MB. This approach is necessary for functions with extensive dependencies, such as machine learning libraries, image processing tools, or multiple SDK dependencies. The deployment package includes your function code and all dependencies bundled together.
Lambda Layers provide an alternative approach for managing large dependencies. Layers are .zip archives containing libraries, custom runtimes, or other dependencies that multiple functions can reference. Each function can reference up to five layers, and the total uncompressed size of the function and all layers cannot exceed 250 MB. Layers reduce deployment package sizes, enable code sharing across functions, and simplify dependency management.
Container image support in Lambda allows deployment packages up to 10 GB, significantly larger than .zip-based deployments. Container images enable using familiar Docker tooling and packaging large applications or dependencies that exceed .zip limits. Images are stored in Amazon ECR and pulled when Lambda creates execution environments.
Optimizing deployment package size improves function performance by reducing cold start times. Strategies include removing unused dependencies, using smaller runtime-specific packages, excluding development dependencies, and leveraging layers for shared code. Tools like webpack for Node.js or tools for Python can bundle and minimize code. Monitoring deployment package sizes during CI/CD pipelines prevents exceeding limits. Understanding these constraints helps design efficient Lambda functions that deploy quickly and start faster.
Question 14:
Which API Gateway integration type passes the client request directly to the backend without modification?
A) Lambda Proxy Integration
B) HTTP Proxy Integration
C) AWS Service Integration
D) Mock Integration
Correct Answer: B
Explanation:
HTTP Proxy Integration in API Gateway forwards the entire client request to the HTTP backend without applying any transformation. This includes all headers, query strings, path parameters, cookies, and the full request body. The backend server receives the request exactly in the same structure and format that API Gateway received from the client. Likewise, the backend’s response is passed back to the client without modification, allowing the backend to fully manage response formatting, status codes, and headers. Because of this passthrough behavior, HTTP Proxy Integration significantly reduces configuration overhead and is one of the simplest ways to expose an existing HTTP endpoint through API Gateway.
This approach is particularly effective when the backend is already designed to handle requests in the expected format. In this setup, API Gateway functions primarily as an entry point, offering benefits such as usage plans, throttling, authentication using API keys, CloudWatch metrics, request filtering, and optional caching. It also provides a layer of protection by integrating with AWS WAF for security filtering. However, since API Gateway does not transform the payload, the backend must perform all validation, data sanitization, error generation, and response shaping.
In contrast, HTTP Custom Integration is far more configurable. It allows developers to use mapping templates based on the Velocity Template Language to reshape requests, inject parameters, modify headers, or restructure responses. This option is useful when the backend requires a specific data format or when an organization wants standardized API responses across different backend systems. But this flexibility increases the complexity of setup and requires more maintenance.
Other integration types extend API Gateway’s capabilities for different backend architectures. Lambda Proxy Integration forwards the entire HTTP request to a Lambda function, which then returns a structured JSON response. AWS Service Integrations allow APIs to directly call AWS services such as DynamoDB, S3, or SNS, eliminating the need for a dedicated backend. Mock Integrations let API Gateway return predefined responses without invoking any external service at all, making them useful for testing, prototyping, or implementing temporary endpoints.
HTTP Proxy Integration is best suited for migrating traditional HTTP APIs, wrapping third-party services, or exposing microservices already capable of handling full client requests. It ensures the backend receives complete client context such as IP address, user-agent, headers, and authentication data, enabling comprehensive request processing. Understanding each integration type allows developers to choose the best pattern for balancing simplicity, flexibility, and operational efficiency.
Question 15:
Which command deploys a CloudFormation stack?
A) aws cloudformation create-stack
B) aws cloudformation deploy
C) aws cloudformation update-stack
D) Both A and C
Correct Answer: D
Explanation:
AWS CloudFormation provides multiple commands for deploying stacks, and both create-stack and update-stack are valid deployment commands depending on whether the stack already exists. The create-stack command initializes a brand-new stack using a CloudFormation template, while update-stack applies modifications to an existing stack. Understanding when and how to use each command is essential for infrastructure automation, DevOps practices, and reliable deployment workflows.
The create-stack command requires a unique stack name within a region and a template file or URL. If a stack with the specified name already exists, the command fails immediately, preventing accidental overwriting of infrastructure. Additional options include template parameters, IAM capability flags such as CAPABILITY_NAMED_IAM, tags, resource policies, and rollback settings. When invoked, CloudFormation begins provisioning the resources defined in the template—such as EC2 instances, IAM roles, Lambda functions, ECS clusters, or VPC components—and logs all events during the process. Administrators can monitor progress through the AWS Management Console or by using commands such as describe-stacks, describe-stack-events, or CloudWatch logs for certain resource types.
The update-stack command, on the other hand, is designed for iterative changes. When you supply an updated template, CloudFormation evaluates what resources need to be added, replaced, or modified. Before applying changes, CloudFormation supports generating a change set, which offers a preview of modifications and allows teams to review potentially destructive operations like resource replacement or deletion before committing. This promotes safer deployments, especially in environments where uptime and resource integrity are critical. Update failures initiate a rollback to the previous stable state unless rollback is explicitly disabled.
The deploy command (aws cloudformation deploy) offers a higher-level, simplified workflow. It automatically determines whether a stack should be created or updated, removing the need for conditional logic in scripts or pipelines. This command is particularly valuable when working with AWS SAM because it can automatically package, upload artifacts to S3, and deploy serverless applications seamlessly. It also integrates smoothly with CI/CD systems like CodePipeline, GitHub Actions, and Jenkins, where minimizing manual logic reduces operational complexity and the likelihood of errors.
In automated pipelines, using deploy improves reliability and consistency since it abstracts the decision-making process. However, teams that require fine-grained control, such as explicit rollbacks, change-set reviews, or custom stack policies, may still prefer create-stack and update-stack. By understanding the full range of CloudFormation deployment commands, engineers can design highly resilient, repeatable, and controlled infrastructure-as-code workflows, ensuring that cloud environments remain stable, traceable, and fully automated.