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Online Test
1). What are the key factors to consider when choosing between Lambda, ECS, and EC2 for a specific workload??
A) Workload type, scaling requirements, cost, and management overhead.
B) Security, performance, and integration with other AWS services.
C) Deployment complexity, monitoring, and troubleshooting.
D) All of the above.
2). How can you optimize Lambda performance for computationally intensive workloads??
A) Increasing memory allocation, using provisioned concurrency, and code optimization.
B) Leveraging AWS Step Functions for orchestration.
C) Using Lambda with Amazon EFS for persistent storage.
D) All of the above.
3). Explain the concept of Lambda power tuning and how it impacts performance and cost.?
A) Adjusting memory allocation for optimal performance and cost-efficiency.
B) Tuning CPU and network resources for specific workloads.
C) Optimizing function code for faster execution.
D) None of the above.
4). How can you implement a serverless architecture for real-time data processing using Lambda and Kinesis??
A) Using Lambda as a consumer of Kinesis data streams.
B) Leveraging Lambda with Kinesis Data Analytics.
C) Implementing a fan-out pattern with multiple Lambda functions.
D) All of the above.
5). What are the best practices for handling errors and retries in complex Lambda-based workflows??
A) Using exponential backoff, dead-letter queues, and error handling mechanisms.
B) Implementing circuit breakers and retry policies.
C) Leveraging AWS Step Functions for orchestration and error handling.
D) All of the above.
6). How can you optimize Lambda costs for large-scale, variable workloads??
A) Using provisioned concurrency, Lambda layers, and function reuse.
B) Implementing cost allocation tags, analyzing CloudWatch metrics, and rightsizing resources.
C) Leveraging reserved concurrency and spot instances.
D) All of the above.
7). What are the challenges of using Lambda for long-running tasks and how to address them??
A) Function timeouts, state management, and cost optimization.
B) Using Lambda with Step Functions for orchestration.
C) Breaking down tasks into smaller functions.
D) All of the above.
8). How can you implement a serverless architecture for machine learning inference using Lambda??
A) Using Lambda with Amazon SageMaker.
B) Packaging machine learning models as Lambda layers.
C) Optimizing Lambda function memory and CPU for inference workloads.
D) All of the above.
9). What are the key factors to consider when choosing between Lambda and container-based services like ECS or Fargate for a specific workload??
A) Workload characteristics, scaling requirements, cost, and management overhead.
B) Security, performance, and integration with other AWS services.
C) Deployment complexity, monitoring, and troubleshooting.
D) All of the above.
10). How can you optimize Lambda performance for I/O intensive workloads??
A) Using EFS for persistent storage.
B) Leveraging AWS Lambda power tuning.
C) Optimizing code for efficient I/O operations.
D) All of the above.
11). What are the best practices for securing Lambda functions and data in a production environment??
A) Implementing IAM roles with least privilege principles.
B) Using VPC configuration with security groups and network ACLs.
C) Encrypting data at rest and in transit.
D) All of the above.
12). How can you implement a blue/green deployment strategy for Lambda functions??
A) Using AWS Lambda aliases.
B) Leveraging AWS CodeDeploy.
C) Using AWS Step Functions for orchestration.
D) All of the above.
13). How can you optimize Lambda costs for large-scale, batch processing workloads??
A) Using spot instances for Lambda functions.
B) Leveraging AWS Batch for batch processing.
C) Optimizing Lambda function memory and timeout.
D) All of the above.
14). What are the challenges of using Lambda for stateful applications and how to address them??
A) Cold starts, function timeouts, and data consistency.
B) Using AWS Step Functions for state management.
C) Leveraging AWS DynamoDB or EFS for persistent storage.
D) All of the above.
15). How can you implement serverless monitoring and alerting for Lambda functions??
A) Using CloudWatch metrics and alarms.
B) Leveraging AWS X-Ray for distributed tracing.
C) Integrating with third-party monitoring tools.
D) All of the above.
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