Amazon AWS Certified Developer — Associate DVA-C02 Exam Dumps and Practice Test Questions Set7 Q91-105
Visit here for our full Amazon AWS Certified Developer — Associate DVA-C02 exam dumps and practice test questions.
Question 91:
What is the default visibility timeout for messages in an Amazon SQS queue?
A) 15 seconds
B) 30 seconds
C) 60 seconds
D) 120 seconds
Answer: B
Explanation:
The default visibility timeout for messages in an Amazon SQS queue is 30 seconds, making this the correct answer. The visibility timeout is the period during which Amazon SQS prevents other consumers from receiving and processing a message that has already been retrieved by one consumer. When a consumer retrieves a message from the queue, the message remains in the queue but becomes invisible to other consumers for the duration of the visibility timeout. If the consumer successfully processes the message and deletes it from the queue before the visibility timeout expires, the message is removed. If processing is not completed within the timeout period, the message becomes visible again for other consumers to process.
15 seconds is incorrect as it is too short to be the default visibility timeout. While you can configure the visibility timeout to be as low as 0 seconds or as high as 12 hours, the default value set by SQS is 30 seconds. A 15-second timeout would be appropriate for very quick processing tasks, but it is not the default setting. If your processing requires more or less time than the default, you can adjust the visibility timeout at the queue level or dynamically change it for individual messages using the ChangeMessageVisibility API call during processing.
60 seconds is not correct, though it might seem like a reasonable default value. While one minute could be a practical timeout for many processing scenarios, Amazon SQS uses 30 seconds as the default. You can certainly configure your queue to use a 60-second visibility timeout if your message processing typically takes around a minute, and this would help prevent messages from becoming visible again too quickly. Understanding the actual default is important for designing reliable message processing systems and avoiding duplicate processing scenarios.
120 seconds is incorrect as it is longer than the default visibility timeout. Two minutes would be suitable for messages that require more complex processing, but it is not the default value that SQS assigns. The default 30-second timeout represents a balance between giving consumers enough time to process simple messages and preventing messages from being invisible for too long if a consumer fails. If your application needs longer processing time, you should explicitly set a longer visibility timeout or extend it programmatically while processing using the ChangeMessageVisibility API to prevent the message from becoming visible to other consumers prematurely.
Question 92:
Which AWS service allows you to trace requests as they travel through your distributed application?
A) Amazon CloudWatch
B) AWS X-Ray
C) AWS CloudTrail
D) Amazon Inspector
Answer: B
Explanation:
AWS X-Ray is the correct service for tracing requests as they travel through your distributed application. X-Ray helps you analyze and debug production and distributed applications by providing end-to-end visibility into requests. It creates a service map that visualizes your application’s architecture and shows how different services are connected and performing. X-Ray traces requests from beginning to end, showing you exactly where time is being spent in your application, including calls to AWS services, HTTP APIs, databases, and other resources. This makes it invaluable for identifying performance bottlenecks, understanding dependencies, and troubleshooting errors in complex microservices architectures.
Amazon CloudWatch is incorrect because while it is a comprehensive monitoring service, it focuses on metrics, logs, and alarms rather than distributed tracing. CloudWatch collects monitoring data from AWS resources and applications, allowing you to visualize metrics, set alarms, and analyze logs. However, it does not provide the end-to-end request tracing capabilities that X-Ray offers. CloudWatch is excellent for monitoring resource utilization, application performance metrics, and log aggregation, but it does not create service maps or trace individual requests through multiple services like X-Ray does for distributed applications.
AWS CloudTrail is not the right answer because it records AWS API calls made in your account for governance and compliance purposes. CloudTrail provides event history of account activity, including actions taken through the AWS Management Console, SDKs, and CLI. While CloudTrail is essential for security analysis and compliance auditing, it does not trace application requests or provide performance insights into how requests flow through your distributed services. CloudTrail focuses on who did what and when in your AWS account, not on application-level request tracing and performance analysis.
Amazon Inspector is incorrect as it is an automated security assessment service that helps improve the security and compliance of applications. Inspector analyzes your applications for vulnerabilities and deviations from security best practices, providing detailed findings and recommendations. It performs network and host assessments on EC2 instances and container images but does not trace application requests or provide distributed tracing capabilities. Inspector focuses on security vulnerabilities and compliance issues rather than application performance monitoring and request flow visualization that X-Ray provides for distributed systems.
Question 93:
What is the purpose of Amazon API Gateway resource policies?
A) To control IAM permissions for API developers
B) To define who can invoke your API and under what conditions
C) To configure API throttling limits
D) To manage API documentation
Answer: B
Explanation:
Resource policies in Amazon API Gateway define who can invoke your API and under what conditions, making this the correct answer. Resource policies are JSON policy documents that you attach to your API to control access at the API level. They allow you to specify which AWS accounts, IAM users, roles, or even IP addresses can access your API. Resource policies are particularly useful for cross-account access scenarios, allowing specific AWS accounts to invoke your API, or for implementing IP whitelist ing to restrict access to specific network ranges. These policies work independently of IAM policies and Lambda authorizers, providing an additional layer of access control for your APIs.
Controlling IAM permissions for API developers is incorrect because that is handled through IAM policies attached to IAM users or roles, not through API Gateway resource policies. IAM policies determine what actions developers can perform on API Gateway resources such as creating, updating, or deleting APIs. Resource policies, in contrast, focus on who can invoke and access the deployed API itself. While both involve access control, they serve different purposes: IAM policies manage administrative access to API Gateway as a service, while resource policies manage access to the API endpoints themselves for client invocations.
Configuring API throttling limits is not the purpose of resource policies. Throttling in API Gateway is configured through usage plans and API stage settings, where you define rate limits and burst limits to protect your backend services from being overwhelmed by too many requests. Throttling controls the rate at which requests are accepted, helping prevent abuse and manage costs. Resource policies, on the other hand, determine whether a request is authorized to invoke the API at all, based on the identity of the caller or other conditions, not the rate or frequency of requests.
Managing API documentation is incorrect because API documentation in API Gateway is handled separately through the documentation parts feature or by importing OpenAPI specifications. Documentation allows you to provide information about your API resources, methods, parameters, and responses to help developers understand how to use your API. Resource policies have no relationship to documentation; they are purely about access control and authorization. You can generate, publish, and version API documentation independently of any resource policies you have configured for access control purposes.
Question 94:
Which deployment type in AWS Lambda allows you to gradually shift traffic to the new function version?
A) All-at-once
B) Canary
C) Blue/Green
D) Rolling
Answer: B
Explanation:
Canary deployment is the correct answer for gradually shifting traffic to a new Lambda function version. In a canary deployment, you route a small percentage of traffic to the new version while the majority continues to use the old version. This allows you to test the new version with real production traffic while minimizing risk. If the new version performs well, you gradually increase the percentage of traffic until all requests go to the new version. If issues are detected, you can quickly roll back by redirecting all traffic to the previous version. AWS Lambda integrates with CodeDeploy to support canary deployments, offering predefined configurations like shifting 10 percent of traffic every 10 minutes until complete.
All-at-once deployment is incorrect because this approach immediately shifts 100 percent of traffic to the new function version without gradual rollout. While all-at-once is the simplest and fastest deployment method, it carries higher risk because any issues with the new version immediately affect all users. There is no gradual testing period with real traffic, and if problems occur, they impact the entire user base until you can roll back. All-at-once deployments are suitable for low-risk changes or non-production environments but not for gradually shifting traffic as the question describes.
Blue/Green deployment is not the most accurate answer in the Lambda context, though it is related to canary deployments. Blue/Green traditionally refers to having two identical production environments where you switch traffic from one to the other. In Lambda, this concept is implemented through aliases pointing to different versions, but the traffic shifting mechanism that allows gradual rollout is specifically called canary deployment. While Lambda does support Blue/Green principles through aliases and versions, the specific feature for gradually shifting traffic percentages is the canary deployment pattern configured through CodeDeploy integration.
Rolling deployment is incorrect because this term typically applies to scenarios where you update instances in batches, such as EC2 instances in an Auto Scaling group or containers in an ECS service. With rolling deployments, you update a subset of instances, verify they are healthy, then proceed to the next batch until all instances run the new version. This concept does not directly apply to Lambda functions, which are stateless and do not have instances in the traditional sense. Lambda uses canary deployments for gradual traffic shifting rather than rolling updates of compute instances.
Question 95:
What is the maximum size of an item in Amazon DynamoDB?
A) 100 KB
B) 256 KB
C) 400 KB
D) 1 MB
Answer: C
Explanation:
The maximum size of an item in Amazon DynamoDB is 400 KB, making this the correct answer. This size limit includes both the attribute names and their values for all attributes in the item. When calculating item size, DynamoDB counts the binary length of the attribute name and value, including all data types such as numbers, strings, binary data, lists, maps, and sets. This 400 KB limit applies to individual items, not to query results or entire tables. If your application requires storing larger objects, you should consider storing the large data in Amazon S3 and keeping a reference to the S3 object in DynamoDB. This hybrid approach is common for applications dealing with documents, images, or other large binary data.
100 KB is incorrect as it is too small to be the actual item size limit. While 100 KB might seem like a reasonable limit for individual database items, DynamoDB actually allows items up to 400 KB. However, even with the 400 KB limit, it is important to design your data model efficiently. Storing unnecessarily large items can impact performance and increase costs since DynamoDB charges based on the amount of data read or written. If you frequently find yourself approaching the size limit, it may indicate that you should reconsider your data model or use S3 for large attribute values.
256 KB is not correct, though it might seem like a logical limit given that many systems use powers of two for size limits. DynamoDB specifically allows items up to 400 KB, not 256 KB. This difference is significant because it gives you more flexibility in storing composite items with multiple attributes. However, remember that larger items consume more read and write capacity units, so you should still aim to keep items as small as practical for your use case to optimize performance and cost.
1 MB is incorrect and exceeds the actual DynamoDB item size limit by more than double. If you attempt to store an item larger than 400 KB, DynamoDB will reject the request with a validation error. For applications that need to store objects larger than 400 KB, the recommended pattern is to store the large data in Amazon S3 and store only metadata and an S3 object reference in DynamoDB. This approach leverages the strengths of both services: DynamoDB for fast, indexed access to metadata and S3 for cost-effective storage of large objects.
Question 96:
Which environment variable is automatically set by AWS Lambda to indicate the memory allocated to the function?
A) AWS_LAMBDA_MEMORY
B) AWS_LAMBDA_FUNCTION_MEMORY_SIZE
C) LAMBDA_MEMORY_SIZE
D) MEMORY_SIZE
Answer: B
Explanation:
AWS_LAMBDA_FUNCTION_MEMORY_SIZE is the correct environment variable that AWS Lambda automatically sets to indicate the memory allocated to the function. This environment variable contains the amount of memory configured for the function in megabytes. Lambda sets several environment variables automatically for every function execution, providing runtime information that your code can access. The memory size is important because it directly affects the function’s performance characteristics, including CPU power and network bandwidth allocated proportionally to memory. Your function code can read this variable to make decisions based on available resources or for logging and monitoring purposes.
AWS_LAMBDA_MEMORY is incorrect because this is not the actual name of the environment variable that Lambda sets. While the name might seem logical and descriptive, AWS uses a more specific naming convention for its environment variables. The correct variable name includes «FUNCTION» in it to be more explicit: AWS_LAMBDA_FUNCTION_MEMORY_SIZE. Understanding the exact names of Lambda environment variables is important for writing code that correctly accesses runtime information. Lambda provides comprehensive documentation of all automatically set environment variables that are available to your functions during execution.
LAMBDA_MEMORY_SIZE is not correct as it does not include the AWS prefix that all Lambda-provided environment variables use. AWS consistently prefixes its environment variables with «AWS_» or «AWS_LAMBDA_» to clearly distinguish them from environment variables you might set yourself. This naming convention helps prevent conflicts and makes it immediately clear which variables are provided by the runtime versus which are user-defined. Without the AWS prefix, this variable name would not follow Lambda’s established pattern for built-in environment variables.
MEMORY_SIZE is incorrect because it is too generic and does not follow the naming convention that AWS Lambda uses for its environment variables. Lambda uses descriptive, prefixed names that clearly indicate their source and purpose. A simple name like MEMORY_SIZE could easily conflict with user-defined variables or variables from other sources. The proper variable name AWS_LAMBDA_FUNCTION_MEMORY_SIZE is much more specific and eliminates any ambiguity about where the value comes from and what it represents. Using the correct variable name is essential for reliable Lambda function execution.
Question 97:
What AWS service provides a managed Kubernetes service for running containerized applications?
A) Amazon ECS
B) Amazon EKS
C) AWS Fargate
D) AWS App Runner
Answer: B
Explanation:
Amazon EKS, which stands for Elastic Kubernetes Service, is the correct answer as it provides a fully managed Kubernetes service for running containerized applications on AWS. EKS runs the Kubernetes control plane across multiple Availability Zones to ensure high availability and automatically handles control plane scaling and updates. You can run Kubernetes workloads on AWS using EC2 instances or on Fargate for serverless container execution. EKS is compatible with standard Kubernetes, allowing you to use existing Kubernetes tooling and plugins. It integrates with many AWS services for networking, security, monitoring, and logging, making it easier to run production-grade Kubernetes clusters without managing the control plane infrastructure yourself.
Amazon ECS, or Elastic Container Service, is incorrect because while it is a container orchestration service, it uses AWS’s proprietary orchestration rather than Kubernetes. ECS is designed specifically for AWS and provides a simpler alternative to Kubernetes for many use cases. ECS works with both EC2 and Fargate launch types, making it flexible for various deployment scenarios. While ECS is excellent for running containers on AWS, it does not provide Kubernetes compatibility or support the Kubernetes API and ecosystem. If you specifically need Kubernetes, you must use EKS rather than ECS for your container workloads.
AWS Fargate is not the right answer because it is a serverless compute engine for containers rather than a container orchestration service. Fargate works with both ECS and EKS, providing the underlying compute capacity without requiring you to manage servers. With Fargate, you define your application requirements, and AWS automatically provisions, scales, and manages the infrastructure. Fargate is a launch type or compute option for running containers, not an orchestration platform. You use Fargate in conjunction with EKS or ECS, which provide the orchestration layer on top of Fargate’s serverless compute capabilities.
AWS App Runner is incorrect because it is a fully managed service that makes it easy to deploy containerized web applications and APIs without requiring infrastructure knowledge. App Runner abstracts both the container orchestration and infrastructure management, providing an even simpler deployment model than EKS or ECS. While App Runner runs containers, it is designed for developers who want to deploy applications quickly without dealing with Kubernetes or other orchestration platforms. App Runner is ideal for simple web services but does not provide the Kubernetes management capabilities that EKS offers for complex, production-grade container orchestration needs.
Question 98:
Which DynamoDB operation allows you to retrieve multiple items from one or more tables in a single request?
A) Query
B) Scan
C) GetItem
D) BatchGetItem
Answer: D
Explanation:
BatchGetItem is the correct operation for retrieving multiple items from one or more tables in a single request. This operation allows you to retrieve up to 100 items or 16 MB of data from one or more DynamoDB tables using their primary keys. BatchGetItem is more efficient than making multiple individual GetItem calls because it reduces the number of network round trips and can retrieve items from multiple tables simultaneously. The operation returns the items in no particular order, and if any requested items do not exist, they simply do not appear in the result. BatchGetItem is particularly useful when you need to fetch multiple related items efficiently, such as retrieving user profiles and their associated preference settings in a single call.
Query is incorrect because while it can retrieve multiple items, it only works on a single table and requires that you specify a partition key value. Query efficiently retrieves all items with a specific partition key or a subset of items using a partition key and sort key condition. Query is optimized for retrieving groups of related items that share the same partition key, but it cannot retrieve arbitrary items across multiple tables like BatchGetItem can. Query is ideal for accessing items with known keys that share a partition value, whereas BatchGetItem is designed for retrieving specific items by their complete primary keys.
Scan is not the right answer because it reads every item in a table and returns all data attributes by default, which is very different from retrieving specific items. Scan examines every item in the table and can filter results, but it processes the entire table regardless of how many items you actually need. This makes Scan inefficient and expensive for retrieving specific items when you know their primary keys. Scan is useful for analytics or when you need to process all items in a table, but for retrieving specific known items, especially from multiple tables, BatchGetItem is the appropriate operation.
GetItem is incorrect because it retrieves only a single item from one table per request. While GetItem is efficient for fetching individual items when you know the complete primary key, it does not support batch operations or cross-table retrieval. If you need to retrieve multiple items, making many individual GetItem calls would result in numerous network requests and higher latency. BatchGetItem was specifically designed to address this limitation by allowing you to retrieve multiple items in a single request, reducing both latency and the complexity of your application code when fetching multiple related items.
Question 99:
What is the purpose of AWS SAM, the Serverless Application Model?
A) To monitor serverless applications
B) To simplify building and deploying serverless applications
C) To test serverless applications locally
D) To secure serverless applications
Answer: B
Explanation:
AWS SAM, or Serverless Application Model, is designed to simplify building and deploying serverless applications, making this the correct answer. SAM is an open-source framework that extends AWS CloudFormation with a simplified syntax specifically tailored for serverless resources. It provides shorthand syntax to express functions, APIs, databases, and event source mappings, reducing the amount of code needed compared to plain CloudFormation. SAM includes the SAM CLI, which provides a Lambda-like execution environment for testing functions locally, building serverless applications, and deploying them to AWS. SAM helps developers iterate quickly during development and ensures consistent deployment processes through infrastructure as code practices optimized for serverless architectures.
Monitoring serverless applications is incorrect because that is the primary purpose of services like Amazon CloudWatch and AWS X-Ray, not SAM. While SAM applications integrate with CloudWatch for logging and monitoring once deployed, SAM itself focuses on the development and deployment lifecycle rather than runtime monitoring. CloudWatch collects metrics and logs from your Lambda functions, while X-Ray provides distributed tracing capabilities. SAM templates can configure monitoring resources, but the core purpose of SAM is to simplify application definition and deployment, not to provide monitoring capabilities during execution.
Testing serverless applications locally is partially correct but not the primary purpose of SAM. The SAM CLI does include features for local testing, allowing you to invoke Lambda functions locally and run API Gateway locally for development purposes. However, this is just one feature of the broader SAM framework. The main purpose of SAM is to simplify the entire development and deployment workflow through its template specification and CLI tools. Local testing is an important capability that SAM provides, but it is a supporting feature rather than the primary purpose of the framework as a whole.
Securing serverless applications is incorrect because security is handled through various AWS services and best practices rather than being the primary focus of SAM. While SAM templates can define IAM roles, resource policies, and other security configurations, SAM itself does not provide security features. Security for serverless applications comes from properly configuring IAM permissions, using services like AWS WAF, implementing encryption, and following the principle of least privilege. SAM helps you define these security configurations in your infrastructure as code, but its main purpose is simplifying development and deployment, not providing security functionality.
Question 100:
Which HTTP status code should an API return when a resource is successfully created?
A) 200 OK
B) 201 Created
C) 202 Accepted
D) 204 No Content
Answer: B
Explanation:
201 Created is the correct HTTP status code to return when a resource is successfully created. This status code specifically indicates that the request succeeded and led to the creation of a new resource. When returning 201, the response should typically include a Location header containing the URI of the newly created resource, and the response body often includes a representation of the new resource. Using the appropriate status code helps API consumers understand exactly what happened with their request and enables them to write more robust client code. The 201 status code is part of the HTTP standard and is widely recognized as the proper response for successful resource creation operations.
200 OK is incorrect for resource creation even though it indicates a successful request. While 200 is appropriate for successful GET requests, updates, or other operations, it is not semantically correct for creation operations. Using 200 for creation is technically functional since it indicates success, but it does not provide the specific information that 201 conveys about a new resource being created. Following REST API best practices and using the correct status codes improves API clarity and helps developers using your API understand the specific outcome of their requests without examining the response body.
202 Accepted is not the right answer for immediate resource creation. The 202 status code indicates that the request has been accepted for processing, but the processing has not been completed yet. This status is appropriate for asynchronous operations where the server accepts the request and will process it later, such as queuing a long-running job. If your API creates resources synchronously and the resource exists immediately after the request completes, you should use 201 Created instead. Use 202 only when the creation is asynchronous and the resource might not exist immediately.
204 No Content is incorrect because it indicates that the request succeeded but there is no content to return in the response body. This status code is commonly used for successful DELETE operations or PUT updates where the client does not need to receive data back. For creation operations, you typically want to return information about the newly created resource, such as its location and current state, so 204 would not be appropriate. The absence of a response body with 204 provides no information about the created resource, which is usually valuable to the client making the creation request.
Question 101:
What is the purpose of Amazon CloudFront signed URLs?
A) To make content publicly accessible
B) To restrict access to private content for specific users
C) To improve content delivery speed
D) To compress content during delivery
Answer: B
Explanation:
Amazon CloudFront signed URLs are designed to restrict access to private content for specific users or time periods, making this the correct answer. Signed URLs contain special encoded information including authentication data and an expiration time, allowing you to control who can access your content and for how long. This feature is particularly useful for distributing premium content, sharing private documents, or implementing pay-per-view functionality. When you create a signed URL, you use your private key to sign the URL, and CloudFront verifies the signature before serving the content. This ensures that only users with valid signed URLs can access your private content, providing both security and temporary access control for distributed content.
Making content publicly accessible is incorrect because that is the opposite of what signed URLs achieve. Public content in CloudFront does not require signed URLs; anyone with the regular CloudFront URL can access public content. Signed URLs are specifically designed for controlling access to private content that should not be publicly available. If you want content to be public, you simply configure your CloudFront distribution and origin to allow public access without implementing signed URLs. Signed URLs add complexity and should only be used when you need to restrict and control access to your content based on authentication or time constraints.
Improving content delivery speed is not the purpose of signed URLs. CloudFront improves delivery speed for all content, whether public or private, by caching content at edge locations close to users worldwide. The speed benefit comes from CloudFront’s global network of edge locations and caching behavior, not from using signed URLs. Signed URLs are purely about access control and do not affect performance or delivery speed. In fact, generating and validating signed URLs adds a small amount of overhead compared to serving public content, though this impact is negligible for most applications.
Compressing content during delivery is incorrect because compression in CloudFront is handled through automatic content compression settings, not signed URLs. CloudFront can automatically compress certain file types using Gzip or Brotli compression when the viewer supports it, reducing the size of transferred data and improving load times. This compression feature is configured separately from access control mechanisms like signed URLs. Signed URLs control who can access content and when, while compression settings control how content is delivered to optimize bandwidth and performance for all users, regardless of how access is controlled.
Question 102:
Which AWS service can automatically scale the number of Amazon EC2 instances based on demand?
A) Amazon EC2 Auto Scaling
B) Elastic Load Balancing
C) AWS Lambda
D) Amazon CloudWatch
Answer: A
Explanation:
Amazon EC2 Auto Scaling is the correct service that automatically scales the number of EC2 instances based on demand or other metrics. Auto Scaling helps ensure that you have the right number of instances available to handle the load for your application. You define scaling policies that automatically add or remove instances based on conditions you specify, such as CPU utilization, network traffic, or custom CloudWatch metrics. Auto Scaling also performs health checks on instances and replaces unhealthy ones to maintain capacity. This service improves application availability and reduces costs by scaling capacity up during demand spikes and scaling down when demand decreases, ensuring you only pay for the resources you actually need.
Elastic Load Balancing is incorrect because while it distributes traffic across instances, it does not create or terminate instances to match demand. ELB works well with Auto Scaling by automatically distributing incoming traffic across all healthy instances registered with the load balancer, including instances that Auto Scaling adds or removes. However, the actual scaling of instance count is handled by Auto Scaling, not by the load balancer itself. ELB focuses on traffic distribution and high availability, while Auto Scaling handles capacity management and instance count adjustments based on demand metrics.
AWS Lambda is not the right answer because it is a serverless compute service that automatically scales function executions, not EC2 instances. Lambda scaling works fundamentally differently from EC2 Auto Scaling; Lambda automatically runs your code in response to events and scales the number of function executions based on incoming requests. Lambda does not involve managing EC2 instances at all. While Lambda provides automatic scaling, it is for serverless functions rather than virtual server instances. For scaling EC2 instances specifically, you must use EC2 Auto Scaling.
Amazon CloudWatch is incorrect because it is a monitoring service that collects metrics and logs, not a scaling service. However, CloudWatch plays an important role in Auto Scaling by providing the metrics that trigger scaling actions. Auto Scaling uses CloudWatch alarms to determine when to add or remove instances based on metric thresholds you define. While CloudWatch is essential for the scaling process by providing the monitoring data, it does not perform the actual scaling operations. CloudWatch monitors and alerts, while Auto Scaling acts on those metrics to adjust capacity.
Question 103:
What is the default behavior when a message in an Amazon SQS FIFO queue fails processing?
A) The message is immediately deleted
B) The message becomes invisible and returns to the queue after visibility timeout
C) The message is sent to a dead-letter queue
D) The message is duplicated for retry
Answer: B
Explanation:
The default behavior when a message in an Amazon SQS FIFO queue fails processing is that the message becomes invisible during processing and returns to the queue after the visibility timeout expires, making this the correct answer. This behavior is consistent across both standard and FIFO queues. When a consumer receives a message, it becomes invisible to other consumers for the duration of the visibility timeout. If the consumer does not explicitly delete the message before the visibility timeout expires, the message becomes visible again in the queue for other consumers to process. This ensures that messages are not lost if processing fails or if the consumer crashes before completing processing.
The message is immediately deleted is incorrect because SQS does not automatically delete messages when processing fails. Messages must be explicitly deleted by the consumer using the DeleteMessage API call after successful processing. This design ensures message durability and prevents data loss. If SQS automatically deleted messages upon retrieval or when processing failed, you would lose messages whenever consumer applications encountered errors or crashed. The explicit deletion requirement gives consumers control over when messages are removed, ensuring successful processing before deletion and allowing for retry logic when processing fails.
The message is sent to a dead-letter queue is not the default behavior; this only happens when you explicitly configure a dead-letter queue and the message exceeds the maximum receive count you specify. A dead-letter queue is a useful feature for handling messages that repeatedly fail processing, but it requires configuration. You must set up a dead-letter queue for your source queue and define a maxReceiveCount value that determines how many times a message can be received before moving to the dead-letter queue. Without this configuration, messages simply return to the queue for reprocessing after the visibility timeout with no limit on receive count.
The message is duplicated for retry is incorrect because SQS does not create duplicate messages for retry purposes. In FIFO queues, messages are delivered exactly once and in order, so duplication would violate the FIFO guarantee. When a message fails processing and the visibility timeout expires, the same message becomes visible again in the queue for another processing attempt. Standard queues may occasionally deliver duplicate messages due to their distributed architecture, but this is not a retry mechanism triggered by processing failure. For both queue types, retry happens by the same message becoming visible again, not by creating duplicates.
Question 104:
Which AWS SDK method should you use to read items from a DynamoDB table one page at a time?
A) GetItem
B) Query with pagination
C) Scan without limits
D) BatchGetItem with offsets
Answer: B
Explanation:
Query with pagination is the correct method for reading items from a DynamoDB table one page at a time. When you perform a Query operation, DynamoDB returns results in pages, with each page containing up to 1 MB of data or the number of items specified in the Limit parameter, whichever is smaller. If there are more results available, DynamoDB includes a LastEvaluatedKey in the response, which you can use as the ExclusiveStartKey in the next Query request to retrieve the next page of results. This pagination mechanism allows you to efficiently process large result sets without loading all data into memory at once. Pagination is essential for handling queries that might return many items while maintaining good performance and memory efficiency in your application.
GetItem is incorrect because it retrieves a single item based on its primary key and does not support pagination. GetItem is designed for reading one specific item when you know its complete primary key. There is no concept of pages with GetItem since it always returns zero or one item. If you need to retrieve multiple items and process them in pages, you must use Query or Scan operations instead. GetItem is the most efficient operation for single-item retrieval but is not applicable when you need to process multiple items with pagination support for memory efficiency.
Scan without limits is not the right answer because while Scan does support pagination similar to Query, running it without proper limit controls can cause performance and memory issues. Scan reads every item in a table and can return large result sets, so it does support the same pagination mechanism as Query using LastEvaluatedKey and ExclusiveStartKey. However, the question asks specifically about reading items one page at a time, which implies controlled pagination. Both Query and Scan support pagination, but Query is generally preferred because it reads only items matching your key condition rather than scanning the entire table.
BatchGetItem with offsets is incorrect because BatchGetItem does not use offset-based pagination. BatchGetItem retrieves multiple specific items using their primary keys in a single request, but it does not support sequential page-by-page reading of table data. Additionally, DynamoDB does not use offset-based pagination anywhere; instead, it uses cursor-based pagination with LastEvaluatedKey and ExclusiveStartKey. BatchGetItem is designed for retrieving known items efficiently, not for reading through a table’s contents in pages. For paginated reading of table data, you must use Query or Scan operations.
Question 105:
What AWS CLI parameter formats the output in a human-readable table?
A) —output json
B) —output text
C) —output table
D) —output yaml
Answer: C
Explanation:
The —output table parameter formats AWS CLI results in a human-readable table format, making this the correct answer. This output format presents data in a structured table with columns and rows, making it easy to read and understand at a glance. The table format includes borders and headers, organizing the information visually for quick comprehension. This format is particularly useful when you are running commands interactively and want to quickly understand the results without parsing JSON or text. However, the table format is not ideal for programmatic processing or scripting, where JSON or text formats are more appropriate for parsing and automation purposes.
—output json is incorrect for producing human-readable table output, although JSON is the default output format for the AWS CLI. JSON format presents data in a structured, machine-readable format that is excellent for programmatic processing and scripting. While JSON is readable by humans with some practice, it is not formatted as a table and can be difficult to scan quickly, especially for results with many fields or nested structures. JSON is the best choice when you need to pipe CLI output to other tools, save results for later processing, or extract specific values programmatically using tools like jq.
—output text is not the right answer for table-formatted output. The text format produces tab-delimited output where each value is separated by tabs and newlines. This format is useful for scripting and can be easily processed with standard Unix text processing tools like awk, grep, and cut. However, text format does not present data in a visual table structure with borders and headers like the table format does. Text output is optimized for parsing by scripts rather than for human readability and quick visual scanning of results.
—output yaml is incorrect because while YAML is a human-readable data serialization format, it does not present results in a table structure. YAML format presents data in a hierarchical, indented structure that can be easier to read than JSON for some people, especially for complex nested structures. However, it still does not provide the columnar table layout that the table output format offers. YAML is useful when you want a more readable serialization format than JSON but still need structured data for further processing. For quick visual scanning in a table format, the table output option is the best choice.