Amazon AWS Certified Developer — Associate DVA-C02 Exam Dumps and Practice Test Questions Set13 Q181-195
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Question 181:
What is the purpose of the AppSpec file in AWS CodeDeploy deployments?
A) To define build commands and artifacts
B) To specify deployment actions and lifecycle event hooks
C) To configure source code repository connections
D) To manage deployment group membership
Answer: B
Explanation:
The AppSpec (Application Specification) file in AWS CodeDeploy serves as the deployment blueprint that specifies exactly how CodeDeploy should deploy your application to target instances or resources. This YAML or JSON-formatted file defines which files should be deployed, where they should be placed, and what lifecycle event hooks should execute during deployment, providing complete control over the deployment process from start to finish.
For EC2 and on-premises deployments, the AppSpec file contains a «files» section mapping source files in your deployment package to destination locations on target instances, and a «hooks» section specifying scripts to run at various points during deployment. The hooks section is particularly powerful, allowing you to execute custom scripts for tasks like stopping application services before deployment, validating successful deployment, or starting services after deployment completes.
CodeDeploy deployment lifecycle includes several event phases: ApplicationStop, DownloadBundle, BeforeInstall, Install, AfterInstall, ApplicationStart, and ValidateService. You can attach custom scripts to any of these hooks, and CodeDeploy executes them in sequence. For example, your ApplicationStop hook might gracefully shut down your web server, AfterInstall might set file permissions, and ApplicationStart might restart the web server with new code.
For Lambda deployments, the AppSpec file specifies the Lambda function version to deploy and traffic routing configuration. You define whether traffic should shift all at once, linearly over time (like 10% every minute), or using canary patterns (like 10% immediately, then 90% after 5 minutes). The file also specifies Lambda hooks functions that run before traffic shifting (BeforeAllowTraffic) and after traffic shifting (AfterAllowTraffic) for validation.
For ECS deployments, the AppSpec file defines the ECS task definition for the new version, container and port information for routing traffic, and optional Lambda functions to run as hooks during deployment. This enables sophisticated ECS blue/green deployments where CodeDeploy creates new task sets, shifts load balancer traffic, and validates the deployment before finalizing.
The AppSpec file must be named appspec.yml (or appspec.json) and must be placed at the root of your deployment package archive. CodeDeploy reads this file to understand deployment instructions, making it central to the deployment process. While buildspec files define build processes in CodeBuild, and deployment groups define target resources in CodeDeploy, the AppSpec file specifically defines how the deployment executes, including all application-specific deployment logic through hooks. Understanding AppSpec file structure and capabilities is essential for implementing sophisticated deployment strategies with proper validation and rollback capabilities.
Question 182:
Which DynamoDB read consistency option provides the most up-to-date data?
A) Eventually consistent reads
B) Strongly consistent reads
C) Transactional reads
D) Sequential reads
Answer: B
Explanation:
Strongly consistent reads in DynamoDB provide the most up-to-date data by returning responses that reflect all writes that received a successful response prior to the read. When you request a strongly consistent read, DynamoDB queries all relevant storage replicas and returns the most current data, ensuring you never read stale data even immediately after write operations.
DynamoDB replicates all data across multiple storage nodes within an AWS Region for durability and availability. When you write data, the write must be acknowledged by a quorum of these replicas before DynamoDB returns a successful response to your application. However, replication to all nodes might not complete instantaneously. This is where read consistency becomes important — it determines which replica data your reads retrieve.
Strongly consistent reads guarantee that any successful write completed before your read will be reflected in the read result. This is critical for applications requiring immediate consistency, such as financial systems where you need to read an account balance immediately after updating it, inventory systems where stock counts must be accurate, or any scenario where reading stale data could cause incorrect business decisions or user experiences.
The tradeoff for strong consistency is higher cost and potentially higher latency compared to eventually consistent reads. Strongly consistent reads consume twice as many read capacity units as eventually consistent reads for the same data. They also require coordination across replicas to find the most current data, which can add latency, particularly during network partitions or replica failures. Additionally, strongly consistent reads are not supported for global secondary indexes, only for base tables and local secondary indexes.
Eventually consistent reads, in contrast, might return data from a replica that hasn’t yet received the most recent write. For many applications, this staleness (typically less than one second) is acceptable, and the cost savings and potentially lower latency make eventually consistent reads preferable. DynamoDB defaults to eventually consistent reads for this reason.
Transactional reads in DynamoDB provide atomicity across multiple items but don’t specifically guarantee the most current data for a single item read — they could use either strong or eventual consistency. Sequential reads are not a DynamoDB concept. When your application absolutely requires reading the most current data and you need to ensure writes are immediately visible to subsequent reads, strongly consistent reads are the correct choice. Understanding when to use strong versus eventual consistency helps optimize both cost and application correctness in DynamoDB applications.
Question 183:
What mechanism does AWS Lambda use to handle concurrent function executions?
A) Thread pooling within a single execution environment
B) Creating separate execution environments for each invocation
C) Queuing requests until previous invocations complete
D) Using operating system process forking
Answer: B
Explanation:
AWS Lambda handles concurrent function executions by creating separate, isolated execution environments for each concurrent invocation. This architecture ensures complete isolation between invocations, preventing one invocation’s activities, state, or errors from affecting others. Each execution environment includes the runtime, your function code, dependencies, and a dedicated portion of memory and compute resources configured for your function.
When Lambda receives multiple invocation requests simultaneously or while previous invocations are still executing, it provisions additional execution environments to handle the concurrent load. Each environment runs on separate infrastructure with dedicated resources, providing true concurrency rather than simulated concurrency through threading or process multiplexing. This design eliminates concerns about thread safety, shared state, or resource contention between concurrent invocations.
Lambda’s scaling behavior aims to reuse execution environments when possible to improve performance. After an invocation completes, Lambda keeps the execution environment alive for a period (typically several minutes) and reuses it for subsequent invocations of the same function. This reuse enables initialization optimization where expensive operations like establishing database connections or loading large libraries can be performed once during cold start and reused across multiple invocations within the same environment.
The temporary nature of execution environments means you cannot reliably share state between invocations through execution environment reuse, even though such reuse does occur. Each invocation must treat the execution environment as potentially new, initializing any required state. For state that should persist across invocations, you must use external storage services like DynamoDB, ElastiCache, or S3.
Lambda enforces concurrency limits to control how many execution environments can run simultaneously. By default, functions in an AWS region share a pool of 1,000 concurrent executions, though you can request increases. You can also configure reserved concurrency to guarantee specific functions always have capacity available, or provisioned concurrency to pre-initialize execution environments for consistently low latency.
This execution model differs fundamentally from traditional application servers that might handle concurrent requests through thread pools or process forking within shared infrastructure. Lambda’s approach of creating isolated environments ensures security, reliability, and predictable performance characteristics. Understanding this execution model is crucial for designing Lambda functions that scale correctly, handle state appropriately, and optimize for both cold and warm start scenarios.
Question 184:
Which CloudFormation feature allows using values from other stacks?
A) Stack parameters
B) Stack outputs and exports
C) Stack imports
D) Stack references
Answer: B
Explanation:
CloudFormation stack outputs and exports enable sharing values between stacks through a publish-subscribe pattern where one stack exports values that other stacks can import and reference. This cross-stack referencing capability is fundamental to building modular, reusable CloudFormation templates that promote separation of concerns and enable independent stack lifecycle management while maintaining proper resource relationships.
The mechanism works through two steps: first, a stack declares outputs with an Export property, publishing values to a region-wide namespace; second, other stacks use the Fn::ImportValue intrinsic function to retrieve these exported values. For example, a networking stack might export VPC ID and subnet IDs, which application stacks then import to launch resources in the correct network configuration without hard-coding values or duplicating network resource definitions.
Exports must have unique names within an AWS region and account. When you export a value, CloudFormation prevents you from deleting or modifying the exporting stack if any other stack currently imports that value. This dependency tracking prevents accidental deletion of resources that other stacks rely on, ensuring system integrity. If you need to delete an exporting stack, you must first update or delete all importing stacks to remove the dependency.
This cross-stack reference pattern promotes several best practices. It enables separating infrastructure concerns into focused stacks: a network stack managing VPCs and subnets, a security stack managing security groups and IAM roles, and application stacks managing application resources. Each stack can be managed independently by appropriate teams while maintaining proper relationships through exports and imports.
The feature also supports organizational standardization. Platform teams can create foundational stacks exporting standard networking, security, or infrastructure configurations, and application teams import these values ensuring their resources integrate correctly with organizational standards. This prevents configuration drift and ensures consistency across applications.
One limitation is that exported values are static within a stack — you can’t change an exported value without updating the stack, which fails if other stacks import the value. For more dynamic cross-stack communication, you might use Systems Manager Parameter Store or other configuration services. Additionally, exports only work within a single region; for cross-region value sharing, you need alternative approaches like custom resources or parameter stores.
Stack parameters pass values into a stack at creation or update time, Stack imports as a standalone concept don’t exist in CloudFormation, and Stack references are not a CloudFormation feature. Stack outputs with exports specifically enable cross-stack value sharing, making them essential for building modular CloudFormation-based infrastructure as code.
Question 185:
What is the purpose of AWS CodeCommit repository triggers?
A) To automatically create branches for new commits
B) To send notifications or invoke Lambda functions on repository events
C) To enforce code review requirements
D) To automatically merge pull requests
Answer: B
Explanation:
AWS CodeCommit repository triggers enable automated responses to repository events by sending notifications to Amazon SNS topics or invoking AWS Lambda functions when specific repository activities occur. This event-driven capability allows you to implement custom workflows, notifications, and integrations that respond automatically to code changes, branch creation, or other repository events without requiring manual intervention or polling.
CodeCommit triggers can be configured to activate on two types of repository events: pushes to branches and pull request events. For push events, you specify which branches should activate the trigger — all branches, specific branches, or branches matching naming patterns. When code is pushed to a watched branch, CodeCommit immediately sends event data to your configured SNS topic or Lambda function, enabling real-time responses to code changes.
When integrating with SNS, triggers publish messages containing event details including repository name, branch name, commit ID, and information about what changed. Subscribers to the SNS topic receive these notifications and can take various actions. Common use cases include sending email notifications to development teams about code changes, posting messages to Slack or other collaboration tools, or triggering additional automated processes through SNS subscriptions.
Lambda function integration provides even more powerful automation capabilities. When a trigger invokes a Lambda function, the function receives detailed event data and can execute arbitrary logic in response. This enables sophisticated custom workflows like running security scans on new code, automatically creating JIRA tickets for commits to specific branches, updating external systems when code changes, or implementing custom compliance checks that analyze commit contents.
CodeCommit triggers complement but differ from CodePipeline. Triggers provide immediate, event-driven responses to repository activities and can implement lightweight automation or notifications. CodePipeline orchestrates complete CI/CD workflows including building, testing, and deploying code. Many organizations use both: triggers for immediate notifications and lightweight automation, and CodePipeline for comprehensive build and deployment workflows.
An important consideration is that triggers fire for every matching event, which can generate substantial notifications or Lambda invocations in active repositories. Designing trigger logic to handle high event volumes efficiently and implementing appropriate filtering ensures triggers enhance rather than complicate your development workflows. While triggers don’t enforce code reviews or merge pull requests automatically (these are approval features), they enable implementing custom logic that can facilitate these processes through integrations with external tools or custom workflows.
Question 186:
Which parameter controls the batch size of records Lambda receives from a Kinesis stream?
A) MaxRecordAge
B) BatchSize
C) ParallelizationFactor
D) MaximumBatchingWindowInSeconds
Answer: B
Explanation:
The BatchSize parameter in Lambda’s event source mapping configuration controls how many records Lambda retrieves from a Kinesis stream in a single batch and passes to your function in one invocation. This setting directly affects function invocation frequency, processing efficiency, and how quickly records are consumed from the stream, making it a critical configuration parameter for stream-processing Lambda functions.
For Kinesis streams, you can configure BatchSize between 1 and 10,000 records. Lambda’s stream polling mechanism retrieves up to the configured BatchSize number of records from the stream and invokes your function once with all retrieved records in a single event. Your function receives the records as an array and typically processes them in a loop, handling each record individually or in aggregate depending on your use case.
Choosing an appropriate batch size involves balancing several factors. Larger batch sizes reduce invocation frequency and can improve throughput by amortizing function initialization overhead across more records, potentially reducing costs since you pay per invocation. However, larger batches mean longer processing times, which can increase the risk that your function times out before completing all records. They also reduce processing parallelism since fewer concurrent invocations occur.
Smaller batch sizes enable faster processing of individual records and higher parallelism with more concurrent function invocations processing different batches simultaneously. This can be valuable for time-sensitive data requiring low latency. However, smaller batches increase invocation frequency and associated costs, and might not fully utilize your function’s processing capacity if individual records process very quickly.
The BatchSize interacts with other event source mapping parameters to determine overall stream processing behavior. ParallelizationFactor (between 1 and 10) controls how many concurrent batches Lambda processes per shard, multiplying effective parallelism. MaximumBatchingWindowInSeconds determines how long Lambda waits to accumulate records before invoking your function, useful when record arrival is sporadic. MaxRecordAge controls how long records can remain in the stream before Lambda skips them, helping prevent processing extremely old records during error recovery.
Lambda automatically adjusts batch size downward if your function returns errors repeatedly, a feature called adaptive batching. This reduces the number of records your function receives in each invocation to help isolate problematic records and improve error recovery. Understanding how BatchSize affects processing characteristics helps you optimize Lambda stream processing for your specific latency, throughput, and cost requirements.
Question 187:
What is the correct method to handle sensitive data in CloudFormation templates?
A) Hardcode secrets directly in template files
B) Use dynamic references to Secrets Manager or Parameter Store
C) Store secrets in template metadata sections
D) Pass secrets through template parameters as plain text
Answer: B
Explanation:
Using dynamic references to AWS Secrets Manager or Systems Manager Parameter Store is the recommended and secure method for handling sensitive data in CloudFormation templates. Dynamic references enable templates to retrieve secret values at stack creation or update time without embedding the actual secret values in templates, stack policies, or stack events, maintaining security while providing templates with the secrets they need to create resources.
CloudFormation dynamic references use special syntax in template property values to indicate that the value should be retrieved from Secrets Manager or Parameter Store when the stack is processed. The syntax format is {{resolve:service:reference}} where service is either secretsmanager for Secrets Manager secrets or ssm for Parameter Store parameters. For example, {{resolve:secretsmanager:MyDatabasePassword}} would retrieve the value from a Secrets Manager secret named MyDatabasePassword.
This approach provides significant security advantages. The actual secret values never appear in CloudFormation templates, which might be stored in version control, shared among team members, or visible in CloudFormation console. Stack events and CloudFormation logs show the dynamic reference syntax, not the resolved secret values, preventing secret leakage through logs. IAM permissions control who can access the referenced secrets independently from CloudFormation permissions.
Dynamic references work seamlessly with CloudFormation’s infrastructure-as-code model. When creating resources like RDS databases that require passwords, you reference a Secrets Manager secret storing the password. CloudFormation retrieves the current secret value when creating the database, ensuring the database is configured with the correct password. If you later rotate the secret, updating the stack retrieves the new value automatically.
The feature supports Secrets Manager’s automatic rotation capability particularly well. You can configure Secrets Manager to automatically rotate database credentials, API keys, or other secrets, and CloudFormation stacks using dynamic references will automatically use the current secret value whenever you update the stack. This enables implementing security best practices like regular credential rotation without manual template updates.
For Parameter Store, dynamic references support both standard parameters and SecureString parameters encrypted with KMS. You can reference parameters by name or by path, and specify version numbers if you need specific parameter versions. Secrets Manager references can retrieve entire secret values or extract specific keys from JSON-structured secrets, providing flexibility in how you structure and reference secrets.
Hard-coding secrets in templates creates serious security vulnerabilities with secrets visible to anyone accessing templates. Storing secrets in metadata sections doesn’t encrypt or protect them. Passing secrets through parameters as plain text exposes them in stack operations and CloudWatch events. Dynamic references specifically address secret management, making them the secure, recommended approach for CloudFormation templates handling sensitive data.
Question 188:
Which AWS service provides managed Kafka clusters for streaming data applications?
A) Amazon Kinesis
B) Amazon MSK
C) Amazon EventBridge
D) Amazon MQ
Answer: B
Explanation:
Amazon Managed Streaming for Apache Kafka (Amazon MSK) is AWS’s fully managed service for Apache Kafka, providing native Kafka clusters without the operational overhead of deploying, managing, and scaling Kafka infrastructure yourself. MSK is specifically designed for applications built on Apache Kafka that require Kafka-specific features, protocols, and ecosystem tools while benefiting from managed service convenience.
MSK provisions and manages Apache Kafka clusters, handling infrastructure tasks including server provisioning, configuration, patching, failure recovery, and software upgrades. The service configures Kafka brokers for high availability by deploying them across multiple Availability Zones within your VPC, providing resilience against infrastructure failures. MSK also manages Apache ZooKeeper nodes, which Kafka requires for coordination, eliminating another operational burden.
The service provides full compatibility with Apache Kafka APIs, tools, and ecosystem components. Applications using Kafka client libraries can connect to MSK clusters using standard Kafka protocols without modification. Popular Kafka ecosystem tools like Kafka Connect for data integration, Kafka Streams for stream processing, and schema registries work seamlessly with MSK. This compatibility enables migrating existing Kafka-based applications to AWS without application changes.
MSK offers integration with AWS services for security and monitoring. You can configure encryption in transit using TLS and encryption at rest using AWS KMS. Authentication supports both TLS mutual authentication and SASL/SCRAM through AWS Secrets Manager for credential storage. For authorization, MSK integrates with Apache Kafka Access Control Lists (ACLs). Monitoring integrates with CloudWatch for metrics and with tools like Prometheus for more detailed Kafka metrics.
The service provides flexibility in cluster configuration, allowing you to choose broker instance types based on throughput and storage requirements, configure storage volumes, and specify the number of brokers per Availability Zone. MSK handles cluster scaling operations, allowing you to increase storage capacity or add brokers to existing clusters as your data volumes grow.
While Amazon Kinesis provides managed streaming data services, it uses Amazon’s proprietary streaming technology rather than Apache Kafka and has different APIs, capabilities, and ecosystem. Amazon EventBridge is an event bus service for application integration through events, not a streaming platform. Amazon MQ provides managed message brokers for ActiveMQ and RabbitMQ protocols, not Kafka. When you need Apache Kafka specifically — for its particular features, existing Kafka applications, or Kafka ecosystem tools — Amazon MSK is the appropriate managed service choice.
Question 189:
What is the recommended approach for passing large data payloads to Lambda functions?
A) Include data directly in invocation payload
B) Store data in S3 and pass S3 object reference
C) Encode data in base64 in environment variables
D) Store data in Lambda layers
Answer: B
Explanation:
Storing large data payloads in Amazon S3 and passing only the S3 object reference (bucket name and key) to Lambda functions is the recommended approach for handling large datasets that exceed Lambda’s invocation payload size limits or where including data directly in the payload would be inefficient. This pattern leverages S3’s virtually unlimited storage capacity and high-throughput data transfer while keeping Lambda invocations lightweight and fast.
Lambda has strict payload size limits: 256 KB for synchronous invocations through direct invoke API calls, and 6 MB for asynchronous invocations through events from services like S3, SNS, or EventBridge. Many real-world scenarios involve processing data that exceeds these limits — processing uploaded files, transforming large datasets, analyzing log files, or handling media content. Attempting to include such data directly in invocation payloads fails or is impractical.
The S3 reference pattern works by having the data producer store the payload in an S3 bucket and either invoke Lambda with an event containing the S3 location details or configure S3 event notifications to automatically trigger Lambda when objects are created. The Lambda function receives the bucket name and object key, uses the AWS SDK to retrieve the object from S3, processes the data, and optionally writes results back to S3 or another destination.
This approach provides several advantages beyond overcoming payload size limits. S3 provides durable storage ensuring data isn’t lost if Lambda invocations fail and need to retry. Multiple Lambda functions or invocations can access the same S3 object, enabling parallel processing or retry scenarios without re-uploading data. S3’s high-performance transfer capabilities enable functions to retrieve large objects quickly, often faster than passing equivalent data through invocation payloads.
For very large files, Lambda functions can use S3’s byte-range fetch capability to retrieve only specific portions of objects, enabling processing data larger than Lambda’s available memory by streaming and processing data in chunks. This technique enables Lambda to process multi-gigabyte files that could never fit in function memory if loaded entirely.
Including data directly in invocation payloads is practical only for small datasets under the size limits. Environment variables have a combined maximum size of 4 KB total for all variables, making them unsuitable for data payloads, and they’re intended for configuration, not data. Lambda layers distribute code and dependencies, not runtime data payloads. When dealing with anything beyond small data volumes, the S3 reference pattern is the scalable, performant, and recommended approach for getting data to Lambda functions.
Question 190:
Which DynamoDB feature provides automatic data replication across multiple AWS regions?
A) DynamoDB Streams
B) DynamoDB Global Tables
C) DynamoDB Accelerator
D) Cross-region backup
Answer: B
Explanation:
DynamoDB Global Tables provide fully managed, multi-region, multi-active database replication that automatically replicates your DynamoDB tables across two or more AWS regions. This capability enables building globally distributed applications with local read and write access in multiple geographic locations while DynamoDB handles all replication complexity behind the scenes, ensuring data consistency across regions.
Global Tables use a multi-active replication architecture where you can read from and write to the table in any replicated region. Writes in any region are automatically replicated to all other regions, typically within one second under normal conditions. This provides local read and write access with low latency for users and applications worldwide, significantly improving user experience for globally distributed applications compared to accessing a single-region database.
The replication is eventually consistent across regions. When you write data to the table in one region, the write is immediately consistent within that region using DynamoDB’s strong consistency model, but it takes a short time (typically under one second) to replicate to other regions. Applications reading from remote regions might see slightly stale data during this replication window. For most global applications, this brief eventual consistency is acceptable and enables the performance benefits of local access.
Global Tables handle conflict resolution automatically using a last-writer-wins reconciliation strategy based on timestamps. If applications make conflicting writes to the same item in different regions simultaneously, DynamoDB uses timestamps to determine which write should be retained, ensuring tables converge to identical states across regions. This automatic conflict resolution eliminates the need for application-level conflict management in most scenarios.
Setting up Global Tables is straightforward. You create a DynamoDB table in your primary region with streams enabled, then add replica regions through the console or API. DynamoDB automatically creates replica tables in the specified regions and establishes replication streams between them. You can add or remove regions from an existing global table as your application’s geographic footprint changes.
Global Tables integrate with other DynamoDB features. You pay standard DynamoDB pricing in each region plus data transfer costs for replication. Global secondary indexes are replicated alongside the base table. Encryption at rest can be configured per region. IAM permissions control access in each region independently. While DynamoDB Streams enable change data capture for integration use cases, DynamoDB Accelerator (DAX) provides in-memory caching for performance, and cross-region backup enables disaster recovery, Global Tables specifically provide active-active multi-region replication for globally distributed applications requiring local access with automatic synchronization.
Question 191:
What is the correct method to invoke a Lambda function synchronously from application code?
A) Use RequestResponse invocation type
B) Use Event invocation type
C) Use DryRun invocation type
D) Use Scheduled invocation type
Answer: A
Explanation:
Using the RequestResponse invocation type when calling the Lambda Invoke API is the correct method for synchronously invoking a Lambda function from application code. This invocation mode tells Lambda to process the function immediately and wait for it to complete before returning the response, including any return value or error information, making it behave like a traditional synchronous function call.
When you invoke a Lambda function with RequestResponse invocation type, the AWS SDK or client making the call waits for the function to complete execution. Lambda runs the function, collects the return value or error information, and sends it back in the API response. This synchronous behavior is appropriate for scenarios where the calling application needs the function’s result immediately to proceed with further processing or to return a response to an end user, such as API backends or data transformation workflows.
The invoke request includes your function payload as the request body and specifies RequestResponse as the InvocationType parameter. After Lambda executes the function, the response includes the function’s return value in the Payload field, execution metadata like log information, and any errors that occurred. If the function throws an exception, the response includes error details allowing your calling code to handle errors appropriately.
This invocation type is commonly used for several scenarios: REST API implementations where API Gateway or Application Load Balancer invokes Lambda synchronously and immediately returns the function response to clients, synchronous workflows where one function must complete and return data before subsequent steps can proceed, and interactive applications where users wait for operation results before continuing.
It’s important to understand timeout considerations with synchronous invocations. The calling client must wait for the entire function execution duration, up to the function’s configured timeout (maximum 15 minutes). For long-running operations, this can create poor user experiences or timeout issues in calling applications. In such cases, asynchronous patterns using the Event invocation type might be more appropriate.
Event invocation type triggers asynchronous execution where Lambda immediately accepts the request, returns an acknowledgment, and processes the function in the background without the caller waiting for results. DryRun invocation type validates invocation parameters and permissions without actually executing the function. Scheduled invocation type doesn’t exist; scheduled function execution uses EventBridge rules invoking functions asynchronously. When your application needs immediate function results and can wait for execution to complete, RequestResponse provides the synchronous invocation semantics required for request-response interaction patterns.
Question 192:
Which CloudWatch metric indicates Lambda function throttling is occurring?
A) Duration
B) Errors
C) Throttles
D) ConcurrentExecutions
Answer: C
Explanation:
The Throttles metric in Amazon CloudWatch specifically indicates Lambda function throttling occurrences, measuring how many function invocation requests were throttled due to exceeding concurrency limits. This metric is essential for monitoring whether your Lambda functions are being rate-limited, helping you identify when you need to request concurrency limit increases or optimize your function’s concurrency usage patterns.
Lambda throttling occurs when invocation requests exceed available concurrency capacity. Each AWS account has a regional concurrent execution limit (default 1,000 concurrent executions), and individual functions can have reserved concurrency allocations limiting their maximum concurrent executions. When an invocation request arrives but no concurrency capacity is available, Lambda throttles the request rather than executing it immediately.
The behavior when throttling occurs depends on the invocation type. For synchronous invocations (like those from API Gateway), Lambda returns a 429 error (Too Many Requests) to the caller, and the calling service or application must handle the error, typically by retrying the request. For asynchronous invocations (like those from S3 events or SNS), Lambda automatically retries throttled requests twice with delays between attempts. If retries also encounter throttling, Lambda can send the event to a configured dead-letter queue or destination.
Monitoring the Throttles metric helps you understand if your concurrency configuration is appropriate for your workload. Consistent non-zero throttle values indicate your functions regularly exceed available concurrency, potentially impacting user experience or system reliability. You can visualize this metric in CloudWatch dashboards, create alarms to notify you when throttling occurs, and analyze throttle patterns to understand peak concurrency requirements.
If you observe function throttling, you have several mitigation options. You can request a concurrency limit increase from AWS Support to raise your account’s regional concurrent execution quota. You can reduce reserved concurrency allocations on low-priority functions to free up capacity for critical functions. You can also optimize function execution time to reduce how long each execution holds concurrency capacity, enabling higher throughput within the same concurrency limits.
The Duration metric measures how long functions execute, Errors tracks invocations that result in function errors or exceptions, and ConcurrentExecutions shows how many function instances are running simultaneously. While all these metrics are valuable for Lambda monitoring, Throttles specifically indicates rate-limiting due to concurrency constraints, making it the definitive metric for identifying and diagnosing throttling issues that impact function availability and reliability.
Question 193:
What is the purpose of AWS SAM template Globals section?
A) To define stack-level outputs
B) To specify common properties inherited by resources
C) To configure deployment preferences
D) To manage template parameters
Answer: B
Explanation:
The Globals section in AWS SAM templates provides a mechanism to define common properties that are automatically inherited by multiple resources of the same type, reducing template duplication and ensuring consistency across similar resources. This section serves as a template-level defaults configuration where you specify properties once, and SAM applies them to all applicable resources unless overridden at the resource level.
Globals sections can define common properties for three resource types: functions, APIs, and simple tables. For functions, you might specify common timeout values, memory sizes, runtime versions, environment variables, or VPC configurations that apply to all Lambda functions in the template. For APIs, you can define CORS settings, stage names, or other API Gateway configurations. For simple tables, you can specify DynamoDB provisioned throughput defaults.
The inheritance model allows resource-specific overrides. If a resource defines a property that also exists in the Globals section, the resource-specific value takes precedence. This enables you to set sensible defaults for most resources while customizing specific resources that need different configurations. For example, you might set a default 128 MB memory size in Globals but override it to 1024 MB for a specific memory-intensive function.
Using Globals promotes several best practices. It reduces template verbosity by eliminating repetitive property definitions across resources, making templates easier to read and maintain. It ensures consistency across resources by defining common configurations once, reducing the risk of configuration drift where similar resources inadvertently have different settings. It also simplifies template-wide configuration changes since updating a Globals property automatically affects all inheriting resources.
A common pattern is defining consistent environment variables, VPC configurations, or security settings in Globals that all functions should share. For example, you might configure all functions to use the same VPC subnets and security groups for database access, or provide all functions with common environment variables pointing to shared resources like DynamoDB table names or API endpoints.
The Globals section is purely a SAM template convenience feature. When SAM transforms your template to CloudFormation, it expands Globals properties into each applicable resource, generating a standard CloudFormation template with explicitly defined properties. The CloudFormation template doesn’t contain Globals sections; it’s a SAM abstraction that simplifies authoring.
While stack outputs export values from stacks, deployment preferences configure CodeDeploy deployments, and parameters provide stack creation inputs, the Globals section specifically addresses reducing resource property duplication through inheritance. Understanding Globals enables writing more maintainable SAM templates with consistent resource configurations and less repetitive code.
Question 194:
Which feature enables Lambda functions to process SQS messages with partial batch success reporting?
A) Message visibility timeout
B) Dead-letter queue configuration
C) ReportBatchItemFailures
D) Long polling
Answer: C
Explanation:
The ReportBatchItemFailures feature enables Lambda functions processing SQS messages to report partial batch success by returning information about which specific messages failed processing while successfully processed messages are automatically deleted from the queue. This capability eliminates the binary choice between treating entire batches as succeeded or failed, preventing unnecessary reprocessing of successful messages when some messages in a batch encounter errors.
Before ReportBatchItemFailures, Lambda’s SQS integration had limited failure handling options. If any message in a batch failed processing and your function threw an exception, Lambda made all messages in the batch visible in the queue again for reprocessing, including messages that were actually processed successfully. Alternatively, if your function caught errors and returned success, failed messages were deleted from the queue and lost. Neither option was ideal for robust production systems.
To use ReportBatchItemFailures, you must enable it in your Lambda event source mapping by setting FunctionResponseTypes to include ReportBatchItemFailures. Your function code must then return a specially formatted response object containing a batchItemFailures array listing the messageId of each message that failed processing. Lambda interprets messages not in this array as successfully processed and deletes them from the queue automatically.
The function response structure looks like: {«batchItemFailures»: [{«itemIdentifier»: «message-id-1»}, {«itemIdentifier»: «message-id-2»}]}. This explicitly tells Lambda which messages failed, enabling precise failure handling. If your function encounters an error processing message-id-1 but successfully processes the other four messages in a five-message batch, you return only message-id-1 in batch ItemFailures, and Lambda handles the rest appropriately.
This feature requires careful implementation. Your function must process messages individually rather than in aggregate, tracking which specific messages succeed or fail. You should catch exceptions during individual message processing rather than allowing them to propagate and terminate the function, since uncaught exceptions cause Lambda to treat the entire batch as failed, bypassing your batch item failure reporting.
The feature works with both standard and FIFO queues, though behavior differs. For standard queues, failed messages become visible again after visibility timeout and may be processed out of order. For FIFO queues, ordering is maintained, and subsequent messages in the same message group are blocked until failed messages are successfully processed.
Message visibility timeout determines how long messages are invisible after retrieval before becoming visible again, Dead-letter queues capture messages that fail repeatedly, and long polling reduces empty receives by waiting for messages. While all these features support robust SQS processing, ReportBatchItemFailures specifically enables granular batch failure reporting, making it essential for efficient error handling in Lambda SQS integrations.
Question 195:
What is the purpose of AWS X-Ray sampling in distributed tracing?
A) To reduce costs by tracing only a portion of requests
B) To improve application performance by disabling tracing
C) To filter traces based on error status
D) To aggregate traces from multiple services
Answer: A
Explanation:
AWS X-Ray sampling serves to control costs and reduce performance overhead by tracing only a statistically significant portion of requests rather than every single request flowing through your application. This sampling approach provides sufficient data to understand application behavior, identify performance issues, and troubleshoot errors while minimizing the volume of trace data generated, stored, and analyzed, directly reducing X-Ray costs and minimizing tracing overhead.
X-Ray uses a reservoir and rate-based sampling algorithm. The reservoir defines a minimum number of requests per second that are always traced regardless of overall traffic volume, ensuring you always capture some traces even during low-traffic periods. The rate defines the percentage of additional requests beyond the reservoir that should be traced. For example, a sampling rule might specify a reservoir of 1 request per second and a rate of 5%, meaning X-Ray traces the first request each second plus 5% of additional requests.
The default X-Ray sampling rule traces the first request each second (reservoir of 1) and 5% of additional requests (rate of 0.05). This default balances cost and visibility appropriately for many applications, capturing enough traces to identify issues without incurring excessive costs. For high-traffic applications processing thousands of requests per second, tracing every request would generate enormous data volumes and costs, while tracing 5% still provides thousands of traces offering comprehensive visibility.
X-Ray supports custom sampling rules providing fine-grained control over what gets traced. You can create rules targeting specific services, HTTP methods, URLs, or other attributes, applying different sampling rates to different traffic patterns. For example, you might trace 100% of requests to administrative endpoints for complete audit trails while sampling only 1% of high-volume public API requests. Rules are evaluated in priority order, with the first matching rule determining sampling behavior.
Sampling decisions are made at the entry point where requests enter your application (like API Gateway or Application Load Balancer) and propagate through the request chain. Once a request is sampled for tracing, all downstream services involved in processing that request are also traced, ensuring you capture complete trace data for sampled requests. This provides end-to-end visibility for sampled requests while avoiding incomplete traces.
The sampling approach is based on statistical sampling principles. Even tracing a small percentage of requests provides statistically significant insights into application behavior, performance characteristics, and error patterns, especially for high-volume applications where small percentages still represent thousands of requests. While sampling reduces costs compared to full tracing, it doesn’t improve performance by disabling tracing, filter based on errors (though you can query traces by error status after collection), or aggregate traces across services (which X-Ray does automatically regardless of sampling). Sampling specifically controls trace data volume to balance visibility and cost effectively.