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Question 71

A hospital uses an electronic health records (EHR) system to collect two types of data
* Patient information, which includes a patient's name and address
* Diagnostic tests conducted and the results of these tests
Patient information is expected to change periodically Existing diagnostic test data never changes and only new records are added The hospital runs an Amazon Redshift cluster with four dc2.large nodes and wants to automate the ingestion of the patient information and diagnostic test data into respective Amazon Redshift tables for analysis The EHR system exports data as CSV files to an Amazon S3 bucket on a daily basis Two sets of CSV files are generated One set of files is for patient information with updates, deletes, and inserts The other set of files is for new diagnostic test data only What is the MOST cost-effective solution to meet these requirements?

A.Use an AWS Lambda function to run a COPY command that appends new diagnostic test data to the diagnostic tests table Run another COPY command to load the patient information data into the staging tables Use a stored procedure to handle create update, and delete operations for the patient information table
B.Use AWS Database Migration Service (AWS DMS) to collect and process change data capture (CDC) records Use the COPY command to load patient information data into the staging tables. Use a stored procedure to handle create, update and delete operations for the patient information table
C.Use an AWS Glue crawler to catalog the data in Amazon S3 Use Amazon Redshift Spectrum to perform scheduled queries of the data in Amazon S3 and ingest the data into the patient information table and the diagnostic tests table.
D.Use Amazon EMR with Apache Hudi. Run daily ETL jobs using Apache Spark and the Amazon Redshift JDBC driver

Question 72

An ecommerce company stores customer purchase data in Amazon RDS. The company wants a solution to store and analyze historical data. The most recent 6 months of data will be queried frequently for analytics workloads. This data is several terabytes large. Once a month, historical data for the last 5 years must be accessible and will be joined with the more recent data. The company wants to optimize performance and cost.
Which storage solution will meet these requirements?

A.Use an ETL tool to incrementally load the most recent 6 months of data into an Amazon Redshift cluster.
Run more frequent queries against this cluster. Create a read replica of the RDS database to run queries on the historical data.
B.Create a read replica of the RDS database to store the most recent 6 months of data. Copy the historical data into Amazon S3. Create an AWS Glue Data Catalog of the data in Amazon S3 and Amazon RDS.
Run historical queries using Amazon Athena.
C.Incrementally copy data from Amazon RDS to Amazon S3. Load and store the most recent 6 months of data in Amazon Redshift. Configure an Amazon Redshift Spectrum table to connect to all historical data.
D.Incrementally copy data from Amazon RDS to Amazon S3. Create an AWS Glue Data Catalog of the data in Amazon S3. Use Amazon Athena to query the data.

Question 73

A manufacturing company has many loT devices in different facilities across the world The company is using Amazon Kinesis Data Streams to collect the data from the devices The company's operations team has started to observe many WnteThroughputExceeded exceptions The operations team determines that the reason is the number of records that are being written to certain shards The data contains device ID capture date measurement type, measurement value and facility ID The facility ID is used as the partition key Which action will resolve this issue?

A.Increase the number of shards
B.Change the partition key from facility ID to a randomly generated key
C.Change the partition key from facility ID to capture date
D.Archive the data on the producers' side

Question 74

A company wants to enrich application logs in near-real-time and use the enriched dataset for further analysis.
The application is running on Amazon EC2 instances across multiple Availability Zones and storing its logs using Amazon CloudWatch Logs. The enrichment source is stored in an Amazon DynamoDB table.
Which solution meets the requirements for the event collection and enrichment?

A.Use a CloudWatch Logs subscription to send the data to Amazon Kinesis Data Firehose. Use AWS Lambda to transform the data in the Kinesis Data Firehose delivery stream and enrich it with the data in the DynamoDB table. Configure Amazon S3 as the Kinesis Data Firehose delivery destination.
B.Export the raw logs to Amazon S3 on an hourly basis using the AWS CLI. Use AWS Glue crawlers to catalog the logs. Set up an AWS Glue connection for the DynamoDB table and set up an AWS Glue ETL job to enrich the data. Store the enriched data in Amazon S3.
C.Configure the application to write the logs locally and use Amazon Kinesis Agent to send the data to Amazon Kinesis Data Streams. Configure a Kinesis Data Analytics SQL application with the Kinesis data stream as the source. Join the SQL application input stream with DynamoDB records, and then store the enriched output stream in Amazon S3 using Amazon Kinesis Data Firehose.
D.Export the raw logs to Amazon S3 on an hourly basis using the AWS CLI. Use Apache Spark SQL on Amazon EMR to read the logs from Amazon S3 and enrich the records with the data from DynamoDB.
Store the enriched data in Amazon S3.

Question 75

A healthcare company uses AWS data and analytics tools to collect, ingest, and store electronic health record (EHR) data about its patients. The raw EHR data is stored in Amazon S3 in JSON format partitioned by hour, day, and year and is updated every hour. The company wants to maintain the data catalog and metadata in an AWS Glue Data Catalog to be able to access the data using Amazon Athena or Amazon Redshift Spectrum for analytics.
When defining tables in the Data Catalog, the company has the following requirements:
Choose the catalog table name and do not rely on the catalog table naming algorithm. Keep the table updated with new partitions loaded in the respective S3 bucket prefixes.
Which solution meets these requirements with minimal effort?

A.Run an AWS Glue crawler that connects to one or more data stores, determines the data structures, and writes tables in the Data Catalog.
B.Use the AWS Glue console to manually create a table in the Data Catalog and schedule an AWS Lambda function to update the table partitions hourly.
C.Use the AWS Glue API CreateTable operation to create a table in the Data Catalog. Create an AWS Glue crawler and specify the table as the source.
D.Create an Apache Hive catalog in Amazon EMR with the table schema definition in Amazon S3, and update the table partition with a scheduled job. Migrate the Hive catalog to the Data Catalog.