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An online photo album app has a key design feature to support multiple screens (e.g, desktop, mobile phone, and tablet) with high-quality displays. Multiple versions of the image must be saved in different resolutions and layouts.

The image-processing Java program takes an average of five seconds per upload, depending on the image size and format. Each image upload captures the following image metadata: user, album, photo label, upload timestamp.

The app should support the following requirements:

1.

Hundreds of user image uploads per second

2.

Maximum image upload size of 10 MB

3.

Maximum image metadata size of 1 KB

4.

Image displayed in optimized resolution in all supported screens no later than one minute after image upload

Which strategy should be used to meet these requirements?

A. Write images and metadata to Amazon Kinesis. Use a Kinesis Client Library (KCL) application to run the image processing and save the image output to Amazon S3 and metadata to the app repository DB.

B. Write image and metadata RDS with BLOB data type. Use AWS Data Pipeline to run the image processing and save the image output to Amazon S3 and metadata to the app repository DB.

C. Upload image with metadata to Amazon S3, use Lambda function to run the image processing and save the images output to Amazon S3 and metadata to the app repository DB.

D. Write image and metadata to Amazon Kinesis. Use Amazon Elastic MapReduce (EMR) with Spark Streaming to run image processing and save the images output to Amazon S3 and metadata to app repository DB.

A company with a support organization needs support engineers to be able to search historic cases to provide fast responses on new issues raised. The company has forwarded all support messages into an Amazon Kinesis Stream. This meets a company objective of using only managed services to reduce operational overhead.

The company needs an appropriate architecture that allows support engineers to search on historic cases and find similar issues and their associated responses.

Which AWS Lambda action is most appropriate?

A. Ingest and index the content into an Amazon Elasticsearch domain.

B. Stem and tokenize the input and store the results into Amazon ElastiCache.

C. Write data as JSON into Amazon DynamoDB with primary and secondary indexes.

D. Aggregate feedback in Amazon S3 using a columnar format with partitioning.

An organization is designing an Amazon DynamoDB table for an application that must meet the following requirements:

1.

Item size is 40 KB

2.

Read/write ratio 2000/500 sustained, respectively

3.

Heavily read-oriented and requires low latencies in the order of milliseconds

4.

The application runs on an Amazon EC2 instance

5.

Access to the DynamoDB table must be secure within the VPC

6.

Minimal changes to application code to improve performance using write-through cache

Which design options will BEST meet these requirements?

A. Size the DynamoDB table with 10000 RCUs/20000 WCUs, implement the DynamoDB Accelerator (DAX) for read performance, use VPC endpoints for DynamoDB, and implement an IAM role on the EC2 instance to secure DynamoDB access.

B. Size the DynamoDB table with 20000 RCUs/20000 WCUs, implement the DynamoDB Accelerator (DAX) for read performance, leverage VPC endpoints for DynamoDB, and implement an IAM user on the EC2 instance to secure DynamoDB access.

C. Size the DynamoDB table with 10000 RCUs/20000 WCUs, implement Amazon ElastiCache for read performance, set up a NAT gateway on VPC for the EC2 instance to access DynamoDB, and implement an IAM role on the EC2 instance to secure DynamoDB access.

D. Size the DynamoDB table with 20000 RCUs/20000 WCUs, implement Amazon ElastiCache for read performance, leverage VPC endpoints for DynamoDB, and implement an IAM user on the EC2 instance to secure DynamoDB access.