Browse DBS Questions
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Question 71 of 100
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: Hundreds of user image uploads per second Maximum image upload size of 10 MB Maximum image metadata size of 1 KB 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?
AWrite image and metadata to 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.
BWrite 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.
CWrite 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.
DUpload 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.
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Question 72 of 100
A company is using Amazon Machine Learning as part of a medical software application. The application will predict the most likely blood type for a patient based on a variety of other clinical tests that are available when blood type knowledge is unavailable. What is the appropriate model choice and target attribute combination for this problem?
AMulti-class classification model with a categorical target attribute.
BRegression model with a numeric target attribute.
CBinary Classification with a categorical target attribute.
DK-Nearest Neighbors model with a multi-class target attribute.
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Question 73 of 100
A company is developing a video application that will emit a log stream. Each record in the stream may contain up to 400 KB of data. To improve the video-streaming experience, it is necessary to collect a subset of metrics from the stream to be analyzed for trends over time using complex SQL queries. A Solutions Architect will create a solution that allows the application to scale without customer interaction. Which solution should be implemented to meet these requirements?
ASend the log data to an Amazon Kinesis Data Firehose delivery stream. Use an AWS Lambda function to transform the data. Deliver the data to Amazon Redshift. Query the data in Amazon Redshift.
BSend the log data to an Amazon SQS standard queue. Make the queue an event source for an AWS Lambda function that transforms the data and stores it in Amazon Redshift. Query the data in Amazon Redshift.
CSend the log data to an Amazon CloudWatch Logs log group. Make the log group an event source for an AWS Lambda function that transforms the data and stores it in an Amazon S3 bucket. Query the data with Amazon Athena.
DSend the log data to an Amazon Kinesis data stream. Subscribe an AWS Lambda function to the stream that transforms the data and sends it to a second data stream. Use Amazon Kinesis Data Analytics to query the data in the second stream.
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Question 74 of 100
A data engineer in a manufacturing company is designing a data processing platform that receives a large volume of unstructured data. The data engineer must populate a well-structured star schema in Amazon Redshift. What is the most efficient architecture strategy for this purpose?
ATransform the unstructured data using Amazon EMR and generate CSV data. COPY the CSV data into the analysis schema within Redshift.
BLoad the unstructured data into Redshift, and use string parsing functions to extract structured data for inserting into the analysis schema.
CWhen the data is saved to Amazon S3, use S3 Event Notifications and AWS Lambda to transform the file contents. Insert the data into the analysis schema on Redshift.
DNormalize the data using an AWS Marketplace ETL tool, persist the results to Amazon S3, and use AWS Lambda to INSERT the data into Redshift.
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Question 75 of 100
You are deploying an application to track GPS coordinates of delivery trucks in the United States. Coordinates are transmitted from each delivery truck once every three seconds. You need to design an architecture that will enable real-time processing of these coordinates from multiple consumers. Which service should you use to implement data ingestion?
AAmazon Kinesis
BAWS Data Pipeline
CAmazon AppStream
DAmazon Simple Queue Service
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Question 76 of 100
You have a customer-facing application running on multiple M3 instances in two AZs. These instances are in an auto-scaling group configured to scale up when load increases. After taking a look at your CloudWatch metrics, you realize that during specific times every single day, the auto-scaling group has a lot more instances than it normally does. Despite this, one of your customers is complaining that the application is very slow to respond during those time periods every day. The application is reading and writing to a DynamoDB table which has 400 Write Capacity Units and 400 Read Capacity Units. The primary key is the company ID, and the table is storing roughly 20 TB of data. Which solution would solve the issue in a scalable and cost-effective manner?
AUse data pipelines to migrate your DynamoDB table to a new DynamoDB table with a different primary key that evenly distributes the dataset across the table.
BAdd a caching layer in front of the web application with ElastiCache Memcached, or Redis.
CDynamoDB is not a good solution for this use case. Instead, create a data pipeline to move data from DynamoDB to Amazon RDS, which is more suitable for this.
DDouble the number of Read and Write Capacity Units. The DynamoDB table is being throttled when customers from the same company all use the table at the same time.
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Question 77 of 100
Your enterprise application requires key-value storage as the database. The data is expected to be about 10 GB the first month and grow to 2 PB over the next two years. There are no other query requirements at this time. What solution would you recommend?
AHive on HDFS
BRDS MySQL
CHBase on HDFS
DHadoop with Spark
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Question 78 of 100
A system needs to collect on-premises application spool files into a persistent storage layer in AWS. Each spool file is 2 KB. The application generates 1 M files per hour. Each source file is automatically deleted from the local server after an hour. What is the most cost-efficient option to meet these requirements?
AWrite file contents to an Amazon DynamoDB table.
BCopy files to Amazon S3 Standard Storage.
CWrite file contents to Amazon ElastiCache.
DCopy files to Amazon S3 infrequent Access Storage.
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Question 79 of 100
A data engineer needs to architect a data warehouse for an online retail company to store historic purchases. The data engineer needs to use Amazon Redshift. To comply with PCI:DSS and meet corporate data protection standards, the data engineer must ensure that data is encrypted at rest and that the keys are managed by a corporate on-premises HSM. Which approach meets these requirements in the most cost-effective manner?
ACreate a VPC, and then establish a VPN connection between the VPC and the on-premises network. Launch the Amazon Redshift cluster in the VPC, and configure it to use your corporate HSM.
BUse the AWS CloudHSM service to establish a trust relationship between the CloudHSM and the corporate HSM over a Direct Connect connection. Configure Amazon Redshift to use the CloudHSM device.
CConfigure the AWS Key Management Service to point to the corporate HSM device, and then launch the Amazon Redshift cluster with the KMS managing the encryption keys.
DUse AWS Import/Export to import the corporate HSM device into the AWS Region where the Amazon Redshift cluster will launch, and configure Redshift to use the imported HSM.
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Question 80 of 100
Your application generates a 1 KB JSON payload that needs to be queued and delivered to EC2 instances for applications. At the end of the day, the application needs to replay the data for the past 24 hours. In the near future, you also need the ability for other multiple EC2 applications to consume the same stream concurrently. What is the best solution for this?
AKinesis Data Streams
BKinesis Firehose
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