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Question 11

A company is reading data from various customer databases that run on Amazon RDS. The databases contain many inconsistent fields For example, a customer record field that is place_id in one database is location_id in another database. The company wants to link customer records across different databases, even when many customer record fields do not match exactly Which solution will meet these requirements with the LEAST operational overhead?

A.Create an Amazon EMR cluster to process and analyze data in the databases. Connect to the Apache Zeppelin notebook, and use Apache Spark ML to find duplicate records in the data. Evaluate and tune the model by evaluating performance and results of finding duplicates
B.Create an AWS Glue crawler to crawl the databases. Use the FindMatches transform to find duplicate records in the data Evaluate and tune the transform by evaluating performance and results of finding matches
C.Create an Amazon EMR cluster to process and analyze data in the databases Connect to the Apache Zeppelin notebook, and use the FindMatches transform to find duplicate records in the data.
D.Create an AWS Glue crawler to crawl the data in the databases Use Amazon SageMaker to construct Apache Spark ML pipelines to find duplicate records in the data

Question 12

A streaming application is reading data from Amazon Kinesis Data Streams and immediately writing the data to an Amazon S3 bucket every 10 seconds. The application is reading data from hundreds of shards. The batch interval cannot be changed due to a separate requirement. The data is being accessed by Amazon Athena.
Users are seeing degradation in query performance as time progresses.
Which action can help improve query performance?

A.Increase the number of shards in Kinesis Data Streams.
B.Merge the files in Amazon S3 to form larger files.
C.Write the files to multiple S3 buckets.
D.Add more memory and CPU capacity to the streaming application.

Question 13

An ecommerce company stores customer purchase data in Amazon RDS. The company wants a solution to store and analyze historical dat a. 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.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.
B.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.
C.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.
D.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.

Question 14

A company is streaming its high-volume billing data (100 MBps) to Amazon Kinesis Data Streams. A data analyst partitioned the data on account_id to ensure that all records belonging to an account go to the same Kinesis shard and order is maintained. While building a custom consumer using the Kinesis Java SDK, the data analyst notices that, sometimes, the messages arrive out of order for account_id. Upon further investigation, the data analyst discovers the messages that are out of order seem to be arriving from different shards for the same account_id and are seen when a stream resize runs.
What is an explanation for this behavior and what is the solution?

A.There are multiple shards in a stream and order needs to be maintained in the shard. The data analyst needs to make sure there is only a single shard in the stream and no stream resize runs.
B.The hash key generation process for the records is not working correctly. The data analyst should generate an explicit hash key on the producer side so the records are directed to the appropriate shard accurately.
C.The records are not being received by Kinesis Data Streams in order. The producer should use the PutRecords API call instead of the PutRecord API call with the SequenceNumberForOrdering parameter.
D.The consumer is not processing the parent shard completely before processing the child shards after a stream resize. The data analyst should process the parent shard completely first before processing the child shards.

Question 15

An ecommerce company uses Amazon Aurora PostgreSQL to process and store live transactional data and uses Amazon Redshift for its data warehouse solution. A nightly ET L job has been implemented to update the Redshift cluster with new data from the PostgreSQL database. The business has grown rapidly and so has the size and cost of the Redshift cluster. The company's data analytics team needs to create a solution to archive historical data and only keep the most recent 12 months of data in Amazon Redshift to reduce costs. Data analysts should also be able to run analytics queries that effectively combine data from live transactional data in PostgreSQL, current data in Redshift, and archived historical data.
Which combination of tasks will meet these requirements? (Select THREE.)

A.Configure Amazon Redshift Spectrum to query live transactional data in the PostgreSQL database.
B.Create a materialized view in Amazon Redshift that combines live, current, and historical data from different sources.
C.Schedule a monthly job to copy data older than 12 months to Amazon S3 by using the UNLOAD command, and then delete that data from the Redshift cluster. Configure Amazon Redshift Spectrum to access historical data in Amazon S3.
D.Configure the Amazon Redshift Federated Query feature to query live transactional data in the PostgreSQL database.
E.Create a late-binding view in Amazon Redshift that combines live, current, and historical data from different sources.
F.Schedule a monthly job to copy data older than 12 months to Amazon S3 Glacier Flexible Retrieval by using the UNLOAD command, and then delete that data from the Redshift cluster. Configure Redshift Spectrum to access historical data with S3 Glacier Flexible Retrieval.