Amazon AWS Certified Developer — Associate DVA-C02 Exam Dumps and Practice Test Questions Set8 Q106-120
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Question 106:
Which feature of AWS CodeDeploy allows you to test a new deployment with a subset of instances before deploying to all instances?
A) In-place deployment
B) Blue/Green deployment
C) Rolling deployment
D) Canary deployment
Answer: D
Explanation:
Canary deployment is the correct feature that allows you to test a new deployment with a subset of instances before deploying to all instances in AWS CodeDeploy. In a canary deployment, you deploy the new version to a small percentage of instances initially, monitor those instances for issues, and then gradually deploy to the remaining instances if the canary deployment succeeds. This approach minimizes risk by exposing only a small portion of your infrastructure and user traffic to the new code initially. CodeDeploy provides predefined canary configurations such as deploying to 10 percent of instances and, if successful after a specified time, deploying to the remaining 90 percent. This staged approach helps detect issues early with minimal user impact.
In-place deployment is incorrect because it updates existing instances with the new application version without creating new infrastructure. During an in-place deployment, CodeDeploy stops the application on each instance, deploys the new version, and restarts the application. While you can configure the deployment to happen gradually across instances, the term «in-place» refers to the deployment strategy of updating existing resources rather than the canary testing pattern. In-place deployments can use rolling or all-at-once configurations, but the deployment type itself does not specifically describe testing with a subset before full deployment.
Blue/Green deployment is not the most accurate answer for this scenario. Blue/Green deployment involves creating a completely new set of instances with the new version while keeping the old version running, then shifting traffic to the new instances. While Blue/Green does allow testing before fully switching over, it involves duplicate infrastructure rather than deploying to a subset of existing instances. In a Blue/Green deployment, you can test the green environment before switching all traffic, but this is conceptually different from canary deployment where you gradually shift traffic to a portion of instances.
Rolling deployment describes the pattern of updating instances in batches but does not specifically emphasize the testing aspect with a small subset first. In a rolling deployment, you update a batch of instances, verify they are healthy, then proceed to the next batch until all instances are updated. While rolling deployments do provide some risk mitigation by updating in stages, they do not typically start with a small test subset like canary deployments. Canary specifically emphasizes starting with a small percentage for testing purposes before broader rollout, which is the scenario described in the question.
Question 107:
What is the primary purpose of AWS Systems Manager Parameter Store?
A) To store application logs
B) To store configuration data and secrets
C) To store database backups
D) To store static website files
Answer: B
Explanation:
AWS Systems Manager Parameter Store is designed to store configuration data and secrets, making this the correct answer. Parameter Store provides secure, hierarchical storage for configuration data management and secrets management. You can store data such as passwords, database connection strings, API keys, and license codes as parameter values. Parameter Store supports both plain text and encrypted parameters, with encryption provided by AWS KMS for sensitive data. Parameters can be organized hierarchically using forward slashes in parameter names, making it easy to group related configuration values. Parameter Store integrates with other AWS services and can be referenced in EC2 instances, Lambda functions, ECS tasks, and other compute resources, enabling centralized configuration management.
Storing application logs is incorrect because that is the purpose of services like Amazon CloudWatch Logs or Amazon S3, not Parameter Store. CloudWatch Logs provides centralized storage and analysis for log data from your applications and AWS services, with powerful query and monitoring capabilities. Parameter Store is designed for storing configuration values and secrets that your applications reference during execution, not for storing the continuous stream of log data that applications generate during operation. Logs are typically much larger in volume and require different storage and analysis tools than configuration parameters.
Storing database backups is not the purpose of Parameter Store. Database backups are typically stored in Amazon S3 or managed automatically by services like Amazon RDS, which stores automated backups and snapshots. Backups are large binary files that require different storage characteristics than the small text or encrypted values that Parameter Store manages. While you might store database connection information in Parameter Store, such as connection strings or credentials, the actual database backup files would be too large and inappropriate for Parameter Store. Use S3 or RDS automated backups for storing database backup data.
Storing static website files is incorrect because that is the purpose of Amazon S3, which provides object storage for files of any type including HTML, CSS, JavaScript, images, and other static assets. S3 can serve these files directly for static websites or through CloudFront for better performance. Parameter Store is designed for storing small pieces of configuration data and secrets, typically text values up to 4 KB for standard parameters or 8 KB for advanced parameters. Static website files are much larger and require object storage capabilities that S3 provides, not the key-value parameter storage that Parameter Store offers.
Question 108:
Which Amazon S3 storage class is designed for data that is accessed less frequently but requires rapid access when needed?
A) S3 Standard
B) S3 Intelligent-Tiering
C) S3 Standard-IA
D) S3 Glacier
Answer: C
Explanation:
S3 Standard-IA, which stands for Infrequent Access, is the correct storage class designed for data that is accessed less frequently but requires rapid access when needed. This storage class offers the same low latency and high throughput performance as S3 Standard but at a lower storage cost in exchange for a higher retrieval cost. Standard-IA is ideal for data that is accessed less than once per month but must be available immediately when requested, such as backups, disaster recovery files, or older data that occasionally needs to be accessed. Objects must be at least 128 KB to be cost-effective in Standard-IA, and there is a minimum storage duration of 30 days with charges applying even if objects are deleted before this period.
S3 Standard is incorrect because while it does provide rapid access, it is designed for frequently accessed data rather than infrequently accessed data. S3 Standard offers the highest durability, availability, and performance, with the lowest latency and highest throughput among S3 storage classes. However, it has higher storage costs compared to Infrequent Access storage classes because it is optimized for data that is accessed often. If your data is accessed less frequently, you can reduce costs by using Standard-IA instead of Standard without sacrificing access speed when retrieval is needed.
S3 Intelligent-Tiering is not the most specific answer for this scenario. While Intelligent-Tiering can automatically move objects between frequent and infrequent access tiers based on changing access patterns, it is designed for data with unknown or changing access patterns rather than specifically for infrequently accessed data. Intelligent-Tiering adds a small monthly monitoring and automation fee per object but can save money if access patterns vary. If you know your data is consistently accessed infrequently, Standard-IA is more cost-effective because you avoid the monitoring fees while getting the same performance characteristics.
S3 Glacier is incorrect because while it is designed for infrequently accessed data, it does not provide rapid access. Glacier is an archival storage class where retrieval can take from minutes to hours depending on the retrieval option you choose. Glacier is ideal for long-term archives where you rarely need access and can tolerate retrieval delays. If you need immediate access when you do retrieve data, even if infrequent, you should use Standard-IA instead. Glacier is significantly cheaper than Standard-IA but sacrifices immediate availability, making it suitable for compliance archives and long-term backups that are rarely accessed.
Question 109:
What is the purpose of AWS CloudFormation Outputs?
A) To display values from created resources
B) To import template files
C) To validate template syntax
D) To delete stack resources
Answer: A
Explanation:
CloudFormation Outputs are used to display values from resources created by the stack, making this the correct answer. Outputs allow you to return important information about resources after stack creation, such as URLs, IP addresses, resource IDs, or ARNs. These output values are displayed in the CloudFormation console, can be retrieved via the AWS CLI or API, and can be exported for use in other stacks through cross-stack references. Outputs are particularly useful for providing information needed to use or connect to resources created by the stack, such as the endpoint URL of an API Gateway, the DNS name of a load balancer, or the ARN of an SNS topic. You define outputs in the Outputs section of your CloudFormation template.
Importing template files is incorrect because CloudFormation does not use Outputs for importing templates. While CloudFormation does support nested stacks and cross-stack references, template importing is handled differently. You can include other templates as nested stacks by referencing them in the Resources section, and you can reference values from other stacks by importing exported outputs. However, the Outputs section itself is specifically for exporting values from the current stack, not for importing templates or other configuration files. Template organization and reuse is handled through nested stacks and stack sets.
Validating template syntax is not the purpose of Outputs. CloudFormation provides template validation through the validate-template CLI command or console validation, which checks for syntax errors, invalid property values, and other template issues before stack creation. This validation happens independently of the Outputs section. Outputs are used after successful stack creation to display resource information, not for validating whether the template is correctly formatted. Template validation ensures your JSON or YAML syntax is correct and that resource definitions are valid before attempting to create the stack.
Deleting stack resources is incorrect because CloudFormation handles resource deletion through stack deletion operations, not through Outputs. When you delete a CloudFormation stack, all resources defined in the template are automatically deleted unless you have enabled termination protection or specified a DeletionPolicy attribute for specific resources. Outputs simply display information about resources and have no role in resource lifecycle management. The Outputs section provides read-only information about your stack resources and does not control creation, modification, or deletion of those resources.
Question 110:
Which environment variable in AWS Lambda contains the name of the function being executed?
A) AWS_LAMBDA_NAME
B) LAMBDA_FUNCTION_NAME
C) AWS_LAMBDA_FUNCTION_NAME
D) FUNCTION_NAME
Answer: C
Explanation:
AWS_LAMBDA_FUNCTION_NAME is the correct environment variable that contains the name of the Lambda function being executed. AWS Lambda automatically sets this environment variable along with several others for every function invocation, providing runtime information that your code can access. The function name is useful for logging, monitoring, and conditional logic based on which function is running. Lambda follows a consistent naming convention for its environment variables, prefixing them with AWS_ or AWS_LAMBDA_ to distinguish them from user-defined variables. You can access this variable in your code to identify the function dynamically, which is particularly useful in shared code libraries or when generating logs and metrics.
AWS_LAMBDA_NAME is incorrect because it does not follow the complete naming convention that Lambda uses. Lambda environment variables that identify function characteristics include the word «FUNCTION» in the name to be explicit about what they represent. The correct variable includes «FUNCTION» as AWS_LAMBDA_FUNCTION_NAME. Understanding the exact names of these environment variables is important for writing Lambda functions that correctly access runtime information. Lambda documentation provides a complete list of all automatically set environment variables that are available in the execution environment.
LAMBDA_FUNCTION_NAME is not correct because it lacks the AWS prefix that all Lambda-provided environment variables include. AWS consistently uses the AWS_ prefix for environment variables it sets automatically to prevent naming conflicts with user-defined variables and to clearly indicate the source of the variable. Without this prefix, the variable name would not follow Lambda’s established naming pattern. All built-in Lambda environment variables begin with either AWS_ or AWS to maintain this clear separation between system and user variables.
FUNCTION_NAME is incorrect because it is too generic and does not include the AWS_LAMBDA prefix that Lambda uses for its environment variables. This simple name could easily conflict with environment variables that you or your libraries might define. Lambda’s naming convention is designed to be explicit and avoid conflicts by using descriptive, prefixed names. The proper variable AWS_LAMBDA_FUNCTION_NAME clearly indicates that it is a Lambda-provided environment variable containing the function name, eliminating any ambiguity about its source or meaning.
Question 111:
What is the maximum timeout you can configure for an API Gateway integration with a Lambda function?
A) 3 seconds
B) 15 seconds
C) 29 seconds
D) 5 minutes
Answer: C
Explanation:
The maximum timeout you can configure for an API Gateway integration with a Lambda function is 29 seconds, making this the correct answer. This timeout applies to all API Gateway integrations, including Lambda proxy integrations, Lambda custom integrations, and HTTP integrations. The 29-second limit is a hard constraint imposed by API Gateway to ensure responsive API behavior and prevent long-running requests from tying up connections. If your Lambda function or backend integration takes longer than 29 seconds to respond, API Gateway will return a 504 Gateway Timeout error to the client. This limit is important to consider when designing APIs, as any processing that might exceed this duration should be handled asynchronously using patterns like initiating the work and returning immediately, then notifying completion through another mechanism.
3 seconds is incorrect as it is far too short to be the maximum timeout for API Gateway integrations. While 3 seconds might be a reasonable timeout for many API calls to ensure good user experience, it is not the maximum that API Gateway allows. Some legitimate API operations might require more time to process, such as complex database queries, third-party API calls, or data processing tasks. API Gateway allows up to 29 seconds to accommodate these longer-running operations while still maintaining reasonable response times for synchronous API calls.
15 seconds is not correct, though it represents the maximum execution timeout for Lambda functions in earlier versions. Currently, Lambda functions can run for up to 15 minutes, but API Gateway imposes its own timeout constraint of 29 seconds for synchronous integrations. The 15-second value does not correspond to any current timeout limit for API Gateway or Lambda. Understanding the difference between Lambda’s maximum execution time and API Gateway’s integration timeout is important because Lambda functions invoked by API Gateway must complete within 29 seconds for synchronous requests.
5 minutes is incorrect and exceeds the API Gateway integration timeout significantly. While Lambda functions can run for up to 15 minutes, API Gateway’s hard limit of 29 seconds for integrations means you cannot use API Gateway for synchronous requests that take several minutes to process. If you need longer processing times, you should implement asynchronous patterns such as accepting the request, starting the work in the background, and providing a separate endpoint for checking status or returning results. Alternative architectures include using Step Functions for orchestrating long-running workflows or processing data asynchronously through SQS queues.
Question 112:
Which DynamoDB feature helps you model complex many-to-many relationships efficiently?
A) Local Secondary Index
B) Global Secondary Index
C) Single-table design pattern
D) DynamoDB Streams
Answer: C
Explanation:
The single-table design pattern is the correct approach for efficiently modeling complex many-to-many relationships in DynamoDB. This design pattern involves storing multiple entity types in one table using composite primary keys and overloaded attributes to represent different relationships. By carefully designing partition keys and sort keys, you can model complex relationships including one-to-many and many-to-many while minimizing the number of requests needed to retrieve related data. Single-table design takes advantage of DynamoDB’s ability to retrieve multiple related items in a single Query operation when they share the same partition key. This pattern is considered a best practice for DynamoDB because it reduces costs, improves performance, and simplifies application logic compared to using multiple tables with joins.
Local Secondary Index is incorrect because while LSIs provide alternative query patterns on the same partition key, they do not specifically address many-to-many relationship modeling. An LSI allows you to query items with the same partition key using a different sort key, which can be useful for accessing data in different orders or filtering by different attributes. However, LSIs are limited to the same partition key as the base table and do not help with the fundamental challenge of representing many-to-many relationships. LSIs are more about providing additional access patterns within a partition rather than modeling complex relationships between entities.
Global Secondary Index is not the primary answer for modeling many-to-many relationships, though GSIs are often used as part of implementing single-table design. GSIs allow you to query data using completely different keys than the base table, which supports creating multiple access patterns. While GSIs are valuable tools in DynamoDB design and are often used alongside single-table patterns, they are not specifically the pattern for modeling many-to-many relationships. The single-table design pattern is the overarching approach, and GSIs are one of the tools used within that pattern to enable different query paths through the data.
DynamoDB Streams is incorrect because it captures changes to table items over time, not a method for modeling relationships between entities. Streams provide a time-ordered sequence of item-level modifications, which is useful for triggering Lambda functions, replicating data, or maintaining materialized views. While Streams can help keep related data synchronized across tables, they do not help with the fundamental design of how to model many-to-many relationships within DynamoDB’s structure. Streams are about reacting to changes rather than organizing data for efficient access patterns.
Question 113:
What happens to in-flight messages when you delete an Amazon SQS queue?
A) Messages are automatically moved to a backup queue
B) Messages are permanently lost
C) Messages are saved to S3
D) Messages are returned to the sender
Answer: B
Explanation:
When you delete an Amazon SQS queue, all in-flight messages are permanently lost, making this the correct answer. SQS does not preserve messages when a queue is deleted; the deletion operation removes the queue and all messages it contains, whether those messages are visible in the queue or currently invisible due to being processed by consumers. This is an irreversible action, so AWS requires confirmation when deleting queues through the console and recommends caution when using deletion APIs. If you need to preserve messages before deleting a queue, you should ensure all messages are processed or manually move them to another queue. Understanding this behavior is critical for preventing data loss in production systems.
Messages are automatically moved to a backup queue is incorrect because SQS does not provide automatic backup or migration of messages during queue deletion. While you can configure a dead-letter queue to capture messages that fail processing repeatedly, this is separate from queue deletion. When you delete the main queue, any configuration including dead-letter queue associations are removed, but messages already in the deleted queue are not automatically transferred anywhere. If you want to preserve messages, you must implement your own backup process before deletion, such as consuming all messages and writing them to another queue or storage system.
Messages are saved to S3 is not correct because SQS does not automatically archive messages to S3 when a queue is deleted. While you could implement your own solution to consume messages from SQS and store them in S3 as part of a backup strategy, this is not automatic queue deletion behavior. SQS focuses on message queuing and delivery, not archival. If you need long-term message storage or backup, you must explicitly design and implement a process to transfer messages from SQS to S3 before deleting queues. Queue deletion simply removes the queue and all its contents without any automatic preservation.
Messages are returned to the sender is incorrect because SQS operates on a fire-and-forget messaging model where senders do not maintain connections or track individual messages after sending. Once a message is successfully sent to an SQS queue, the sender has no ongoing relationship with that message. SQS does not have a mechanism to return messages to senders, and when a queue is deleted, there is no way to notify senders about the deletion or return their messages. The sender receives acknowledgment when the message is initially sent, but after that point, the message belongs to the queue until it is consumed or the queue is deleted.
Question 114:
Which AWS CodeCommit feature allows you to review code changes before merging them into a branch?
A) Branches
B) Pull requests
C) Commits
D) Tags
Answer: B
Explanation:
Pull requests are the correct CodeCommit feature that allows you to review code changes before merging them into a branch. A pull request enables developers to notify team members about changes they have pushed to a branch and request review before merging those changes into another branch, typically the main or master branch. Pull requests provide a collaborative environment where team members can comment on specific lines of code, suggest improvements, and approve or request changes. CodeCommit pull requests integrate with approval rules, allowing you to enforce requirements such as a minimum number of approvals before merging. This feature supports code quality practices and team collaboration by ensuring code review happens before changes are integrated into important branches.
Branches are incorrect as an answer because while branches are essential for organizing development work, they do not provide review functionality by themselves. Branches allow developers to work on features, bug fixes, or experiments in isolation from the main codebase. You create a branch to develop changes separately, but the branch itself does not facilitate code review. Pull requests are what enable review of changes in one branch before merging to another branch. Branches are the structure that enables parallel development, while pull requests are the mechanism for reviewing and approving changes before integration.
Commits are not the right answer because commits represent individual snapshots of changes to the repository, not a review mechanism. A commit records changes to files along with metadata like author, timestamp, and commit message. While you can view commits to see what changed, commits themselves do not provide a collaborative review process. Pull requests aggregate multiple commits from a source branch and provide a structured way to review all those changes together before merging. Commits are the building blocks of version control, but pull requests are the feature that enables formal code review.
Tags are incorrect because they are used to mark specific points in repository history, typically for releases or important milestones. Tags create a named reference to a specific commit, making it easy to return to that exact state of the code later. Tags are useful for version management and creating release points, but they do not facilitate code review. You might create a tag after merging a pull request to mark a release, but tags themselves are not involved in the review process that happens before code is merged into a branch.
Question 115:
What is the primary benefit of using AWS Lambda layers?
A) To increase function memory
B) To share code and dependencies across multiple functions
C) To improve function execution speed
D) To enable cross-region replication
Answer: B
Explanation:
The primary benefit of using AWS Lambda layers is to share code and dependencies across multiple functions, making this the correct answer. Layers allow you to package libraries, custom runtimes, or other dependencies separately from your function code, and then reference those layers in multiple functions. This promotes code reuse, reduces deployment package sizes for individual functions, and makes it easier to manage dependencies centrally. When you update a layer, all functions using that layer can immediately benefit from the update. Layers can contain anything from third-party libraries to common utility functions, database connection logic, or configuration files that multiple functions need. This approach keeps your function deployment packages small and focused on business logic while sharing common code efficiently.
Increasing function memory is incorrect because Lambda layers do not affect the memory allocated to functions. Function memory is configured independently through the MemorySize parameter when creating or updating a function. While the content in layers contributes to the total size limits that Lambda enforces, layers do not provide additional memory for function execution. Memory allocation determines the CPU and network resources available to your function and affects execution cost, but this is completely separate from the layer feature which is purely about code and dependency sharing.
Improving function execution speed is not the primary benefit of layers, though layers can have minor performance effects. Layers do not inherently make functions execute faster; performance depends on your code efficiency, memory allocation, and external dependencies like network calls or database queries. However, using layers can slightly reduce cold start times if your function code becomes smaller by moving dependencies to layers, as there is less code to download and initialize. The primary purpose of layers remains code sharing and reusability, not performance optimization. Any performance benefits are secondary effects of better code organization.
Enabling cross-region replication is incorrect because layers do not provide replication functionality. While you can create layers in multiple regions and reference region-specific layer versions in your functions, this is manual configuration rather than automatic replication. If you want to use the same layer in multiple regions, you must create the layer separately in each region or copy it using the Lambda publish-layer-version API with appropriate permissions. Layers are region-specific resources, and cross-region usage requires deliberate duplication. The purpose of layers is sharing code within functions that use them, not distributing that code geographically.
Question 116:
Which HTTP method should be used for idempotent update operations in a RESTful API?
A) POST
B) PUT
C) PATCH
D) DELETE
Answer: B
Explanation:
PUT is the correct HTTP method for idempotent update operations in RESTful APIs. An idempotent operation is one that can be called multiple times with the same result as calling it once. PUT requests should replace the entire resource with the data provided in the request body, and making the same PUT request multiple times will result in the same resource state. For example, updating a user profile with PUT should send the complete user object, and calling it repeatedly with the same data produces the same result without side effects. This idempotency property makes PUT safe for retry logic and ensures consistent behavior even if duplicate requests occur due to network issues or client retries.
POST is incorrect because it is typically not idempotent in RESTful API design. POST is commonly used for creating new resources, and calling POST multiple times with the same data usually creates multiple new resources with different identifiers. For example, submitting an order through POST might create a new order each time, even if the data is identical. While POST can technically be used for updates in some API designs, it is not the standard RESTful approach for update operations. POST is appropriate for non-idempotent operations where multiple identical requests should have different effects, such as creating resources or performing actions.
PATCH is not the best answer for general idempotent update operations, though PATCH can be designed to be idempotent depending on implementation. PATCH is used for partial updates where you send only the fields that should change rather than the entire resource. Whether PATCH is idempotent depends on how the update is specified; some PATCH operations are idempotent while others may not be. For guaranteed idempotent updates in RESTful design, PUT is the standard choice because it replaces the entire resource, making the operation’s outcome clear and repeatable regardless of current resource state.
DELETE is not correct for update operations, though DELETE is idempotent. DELETE is used to remove resources, and calling DELETE multiple times on the same resource should have the same effect as calling it once: the resource is deleted, and subsequent DELETE requests on a non-existent resource typically return 404 or simply succeed without error. While DELETE demonstrates idempotency, it is for removal operations, not updates. The question specifically asks about update operations, for which PUT is the appropriate idempotent HTTP method in RESTful API design following standard REST principles.
Question 117:
What is the purpose of Amazon CloudWatch Alarms?
A) To delete unused resources
B) To monitor metrics and trigger actions based on thresholds
C) To store application logs
D) To deploy applications
Answer: B
Explanation:
Amazon CloudWatch Alarms are designed to monitor metrics and trigger actions based on threshold conditions, making this the correct answer. Alarms watch a single CloudWatch metric or the result of a metric math expression over a specified time period and perform one or more actions when the metric breaches a threshold you define. Actions can include sending notifications through SNS, executing Auto Scaling policies to add or remove instances, or triggering Systems Manager actions. Alarms have three possible states: OK when the metric is within the defined threshold, ALARM when the metric has breached the threshold, and INSUFFICIENT_DATA when not enough data is available to evaluate the metric. This feature is essential for proactive monitoring and automated responses to changing conditions in your AWS environment.
Deleting unused resources is incorrect because CloudWatch Alarms do not have built-in functionality to identify or delete resources. While you could create a complex workflow where an alarm triggers a Lambda function that deletes resources, this is not the primary purpose of alarms. AWS provides other services like AWS Config and Trusted Advisor to help identify unused or underutilized resources. CloudWatch Alarms focus on monitoring metric values and triggering configured actions, not on resource lifecycle management or cleanup operations. Resource deletion would require custom logic beyond the alarm’s core monitoring and notification capabilities.
Storing application logs is not the purpose of CloudWatch Alarms; that function belongs to CloudWatch Logs. CloudWatch Logs is a separate service that centralizes log storage, allows log querying and analysis, and provides log retention management. While CloudWatch Alarms can be set on metric filters created from CloudWatch Logs data, the alarms themselves do not store logs. The alarm feature monitors metrics, including custom metrics derived from logs, but the actual log storage and management happens in CloudWatch Logs. Understanding the distinction between CloudWatch Alarms and CloudWatch Logs is important for effectively using CloudWatch for monitoring and troubleshooting.
Deploying applications is incorrect because CloudWatch Alarms do not handle application deployment. Application deployment is managed by services like AWS CodeDeploy, AWS Elastic Beanstalk, or AWS CloudFormation. While CloudWatch Alarms might trigger Auto Scaling actions that launch new instances running your application, this is different from deploying new application versions or configurations. Alarms can be part of automated scaling workflows, but their purpose is monitoring and triggering actions based on metric thresholds, not managing the deployment lifecycle of applications or coordinating the rollout of new application versions.
Question 118:
Which AWS service provides a fully managed workflow orchestration service for coordinating distributed applications?
A) AWS Lambda
B) AWS Step Functions
C) Amazon SQS
D) Amazon SNS
Answer: B
Explanation:
AWS Step Functions is the correct service that provides fully managed workflow orchestration for coordinating distributed applications and microservices. Step Functions allows you to design and execute workflows called state machines that coordinate multiple AWS services into serverless workflows. You define your workflow using Amazon States Language, a JSON-based language that specifies states, transitions, error handling, and retry logic. Step Functions provides visual workflow representation, making it easy to understand and debug complex processes. The service handles state management, task coordination, error handling, and retries automatically, allowing you to build reliable distributed applications without managing infrastructure. Step Functions integrates with Lambda, ECS, Fargate, Batch, DynamoDB, SNS, SQS, and many other AWS services to orchestrate complex workflows.
AWS Lambda is incorrect because while Lambda executes individual functions in response to events, it does not provide workflow orchestration capabilities by itself. Lambda is a building block that Step Functions can coordinate, but Lambda functions run independently without built-in coordination between multiple functions. You would need to implement custom logic within Lambda functions to orchestrate multi-step workflows, which becomes complex and error-prone for sophisticated processes. Step Functions was specifically designed to orchestrate Lambda functions and other services into coordinated workflows with proper error handling, retries, and state management that Lambda alone does not provide.
Amazon SQS is not the right answer because while it can be used as part of distributed application architectures to decouple components, it does not provide workflow orchestration. SQS is a message queue that enables asynchronous communication between application components, but it does not define or manage multi-step workflows with conditional logic, error handling, and state transitions. You might use SQS within a Step Functions workflow to queue tasks, but SQS itself does not orchestrate the overall process or coordinate the execution of multiple steps in a defined sequence with branching and error handling capabilities.
Amazon SNS is incorrect because it is a pub/sub messaging service for distributing messages to multiple subscribers, not a workflow orchestration service. SNS enables you to send notifications to various endpoints like email, SMS, HTTP, SQS, or Lambda, but it does not manage stateful workflows or coordinate multi-step processes. SNS delivers messages and then its job is done; it does not track workflow progress, manage state transitions, or handle complex conditional logic. While SNS might be used within workflows to send notifications, it is not designed for the workflow orchestration capabilities that Step Functions provides for coordinating distributed applications.
Question 119:
What is the maximum number of tags you can assign to an AWS resource?
A) 10
B) 25
C) 50
D) 100
Answer: C
Explanation:
The maximum number of tags you can assign to most AWS resources is 50, making this the correct answer. Tags are key-value pairs that help you organize, track, and manage AWS resources. They are useful for cost allocation, access control, automation, and resource organization. Each tag consists of a key and an optional value, both of which are case-sensitive strings. You can use tags to categorize resources by purpose, owner, environment, or any other criteria relevant to your organization. While most AWS resources support up to 50 user-created tags, it is important to design a consistent tagging strategy that provides meaningful organization without reaching the limit unnecessarily. Some AWS services may automatically add their own system tags that do not count against this limit.
10 tags is incorrect as it is too few to be the maximum for AWS resources. While ten tags might be sufficient for simple use cases, AWS allows up to 50 tags per resource to accommodate complex organizational needs. Many organizations use tags for multiple purposes simultaneously, such as cost center identification, environment designation, data classification, compliance requirements, and automation triggers. Having only ten tags would be limiting for enterprises with sophisticated resource management requirements. The 50-tag limit provides ample flexibility for comprehensive resource organization and management strategies.
25 tags is not correct, though it represents half of the actual limit. While 25 tags would provide reasonable flexibility for many scenarios, AWS actually allows up to 50 tags per resource. This higher limit accommodates organizations with complex tagging strategies that might include multiple dimensions of categorization. For example, an organization might use tags for cost allocation, security classification, compliance tracking, automation, ownership, project identification, and environment designation, potentially requiring many tags to capture all necessary metadata about resources for proper management and governance.
100 tags is incorrect as it exceeds the actual limit AWS imposes on resource tags. While having 100 tags might seem useful for very complex scenarios, AWS limits most resources to 50 user-created tags. This limit helps prevent over-complication of tagging strategies and encourages thoughtful organization of resources. If you find yourself needing more than 50 tags, it might indicate that your tagging strategy could be simplified or that some information should be stored elsewhere, such as in a configuration database or resource metadata rather than as tags on the resources themselves.
Question 120:
Which AWS CLI command would you use to retrieve metadata about an EC2 instance from within the instance?
A) aws ec2 describe-instances
B) curl http://169.254.169.254/latest/meta-data/
C) aws ec2 get-instance-metadata
D) aws metadata describe
Answer: B
Explanation:
The command «curl http://169.254.169.254/latest/meta-data/» is the correct way to retrieve metadata about an EC2 instance from within the instance itself. This command accesses the instance metadata service, which provides information about the running instance without requiring AWS API credentials. The IP address 169.254.169.254 is a link-local address that is accessible only from within EC2 instances and provides access to metadata categories like instance ID, instance type, security groups, IAM role credentials, user data, and network information. You can navigate the metadata hierarchy by appending specific paths to the base URL. This metadata service is crucial for instances to discover information about themselves and retrieve temporary security credentials for IAM roles.
The command «aws ec2 describe-instances» is incorrect for retrieving metadata from within an instance, though it does retrieve instance information. This AWS CLI command queries the EC2 API to get information about instances in your account, but it requires AWS credentials and makes an API call rather than accessing the local metadata service. When running from within an instance, using the metadata service is faster, more efficient, and does not consume API request quota. The describe-instances command is used for managing instances from outside, such as from your development machine or automation scripts, not for instances to learn about themselves.
The command «aws ec2 get-instance-metadata» is incorrect because this is not an actual AWS CLI command. While the name sounds plausible, AWS CLI does not have a command called get-instance-metadata. The proper way to access instance metadata from within an instance is through the HTTP endpoint at 169.254.169.254, not through AWS CLI commands. This is a common confusion point, but understanding that metadata service access uses HTTP requests rather than AWS CLI or SDK calls is important for working effectively with EC2 instances and understanding how instances obtain information about themselves during runtime.
The command «aws metadata describe» is incorrect because no such AWS CLI command exists. AWS CLI commands follow the pattern «aws <service> <command>» where the service is a recognized AWS service like ec2, s3, or lambda. There is no «metadata» service in AWS CLI. The instance metadata service is accessed through HTTP requests to a special IP address, not through AWS CLI commands. This distinction is important because the metadata service is designed to be accessible without AWS credentials, making it perfect for bootstrapping instances and allowing them to discover their configuration and retrieve temporary credentials for IAM roles.