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An airline company is collecting metrics about flight activities for analytics. The company is conducting a proof of concept (POC) test to show how analytics can provide insights that the company can use to increase on-time departures. The POC test uses objects in Amazon S3 that contain the metrics in .csv format. The POC test uses Amazon Athena to query the data. The data is partitioned in the S3 bucket by date. As the amount of data increases, the company wants to optimize the storage solution to improve query performance. Which combination of solutions will meet these requirements? (Choose two.)
A company uses Amazon S3 to store semi-structured data in a transactional data lake. Some of the data files are small, but other data files are tens of terabytes. A data engineer must perform a change data capture (CDC) operation to identify changed data from the data source. The data source sends a full snapshot as a JSON file every day and ingests the changed data into the data lake. Which solution will capture the changed data MOST cost-effectively?
A data engineer needs to schedule a workflow that runs a set of AWS Glue jobs every day. The data engineer does not require the Glue jobs to run or finish at a specific time. Which solution will run the Glue jobs in the MOST cost-effective way?
A company currently stores all of its data in Amazon S3 by using the S3 Standard storage class. A data engineer examined data access patterns to identify trends. During the first 6 months, most data files are accessed several times each day. Between 6 months and 2 years, most data files are accessed once or twice each month. After 2 years, data files are accessed only once or twice each year. The data engineer needs to use an S3 Lifecycle policy to develop new data storage rules. The new storage solution must continue to provide high availability. Which solution will meet these requirements in the MOST cost-effective way?
A company receives a daily file that contains customer data in .xls format. The company stores the file in Amazon S3. The daily file is approximately 2 GB in size. A data engineer concatenates the column in the file that contains customer first names and the column that contains customer last names. The data engineer needs to determine the number of distinct customers in the file. Which solution will meet this requirement with the LEAST operational effort?
A company maintains a data warehouse in an on-premises Oracle database. The company wants to build a data lake on AWS. The company wants to load data warehouse tables into Amazon S3 and synchronize the tables with incremental data that arrives from the data warehouse every day. Each table has a column that contains monotonically increasing values. The size of each table is less than 50 GB. The data warehouse tables are refreshed every night between 1 AM and 2 AM. A business intelligence team queries the tables between 10 AM and 8 PM every day. Which solution will meet these requirements in the MOST operationally efficient way?
A healthcare company uses Amazon Kinesis Data Streams to stream real-time health data from wearable devices, hospital equipment, and patient records. A data engineer needs to find a solution to process the streaming data. The data engineer needs to store the data in an Amazon Redshift Serverless warehouse. The solution must support near real-time analytics of the streaming data and the previous day's data. Which solution will meet these requirements with the LEAST operational overhead?
A company needs to set up a data catalog and metadata management for data sources that run in the AWS Cloud. The company will use the data catalog to maintain the metadata of all the objects that are in a set of data stores. The data stores include structured sources such as Amazon RDS and Amazon Redshift. The data stores also include semistructured sources such as JSON files and .xml files that are stored in Amazon S3. The company needs a solution that will update the data catalog on a regular basis. The solution also must detect changes to the source metadata. Which solution will meet these requirements with the LEAST operational overhead?
A company has three subsidiaries. Each subsidiary uses a different data warehousing solution. The first subsidiary hosts its data warehouse in Amazon Redshift. The second subsidiary uses Teradata Vantage on AWS. The third subsidiary uses Google BigQuery. The company wants to aggregate all the data into a central Amazon S3 data lake. The company wants to use Apache Iceberg as the table format. A data engineer needs to build a new pipeline to connect to all the data sources, run transformations by using each source engine, join the data, and write the data to Iceberg. Which solution will meet these requirements with the LEAST operational effort?
A company has used an Amazon Redshift table that is named Orders for 6 months. The company performs weekly updates and deletes on the table. The table has an interleaved sort key on a column that contains AWS Regions. The company wants to reclaim disk space so that the company will not run out of storage space. The company also wants to analyze the sort key column. Which Amazon Redshift command will meet these requirements?

Question 4 - DEA-C01 discussion

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A retail company uses Amazon Aurora PostgreSQL to process and store live transactional data. The company uses an Amazon Redshift cluster for a data warehouse.

An extract, transform, and load (ETL) job runs every morning to update the Redshift cluster with new data from the PostgreSQL database. The company has grown rapidly and needs to cost optimize the Redshift cluster.

A data engineer needs to create a solution to archive historical data. The data engineer must be able to run analytics queries that effectively combine data from live transactional data in PostgreSQL, current data in Redshift, and archived historical data. The solution must keep only the most recent 15 months of data in Amazon Redshift to reduce costs.

Which combination of steps will meet these requirements? (Select TWO.)

A.

Configure the Amazon Redshift Federated Query feature to query live transactional data that is in the PostgreSQL database.

Answers
A.

Configure the Amazon Redshift Federated Query feature to query live transactional data that is in the PostgreSQL database.

B.

Configure Amazon Redshift Spectrum to query live transactional data that is in the PostgreSQL database.

Answers
B.

Configure Amazon Redshift Spectrum to query live transactional data that is in the PostgreSQL database.

C.

Schedule a monthly job to copy data that is older than 15 months to Amazon S3 by using the UNLOAD command. Delete the old data from the Redshift cluster. Configure Amazon Redshift Spectrum to access historical data in Amazon S3.

Answers
C.

Schedule a monthly job to copy data that is older than 15 months to Amazon S3 by using the UNLOAD command. Delete the old data from the Redshift cluster. Configure Amazon Redshift Spectrum to access historical data in Amazon S3.

D.

Schedule a monthly job to copy data that is older than 15 months to Amazon S3 Glacier Flexible Retrieval by using the UNLOAD command. Delete the old data from the Redshift duster. Configure Redshift Spectrum to access historical data from S3 Glacier Flexible Retrieval.

Answers
D.

Schedule a monthly job to copy data that is older than 15 months to Amazon S3 Glacier Flexible Retrieval by using the UNLOAD command. Delete the old data from the Redshift duster. Configure Redshift Spectrum to access historical data from S3 Glacier Flexible Retrieval.

E.

Create a materialized view in Amazon Redshift that combines live, current, and historical data from different sources.

Answers
E.

Create a materialized view in Amazon Redshift that combines live, current, and historical data from different sources.

Suggested answer: A, C

Explanation:

The goal is to archive historical data from an Amazon Redshift data warehouse while combining live transactional data from Amazon Aurora PostgreSQL with current and historical data in a cost-efficient manner. The company wants to keep only the last 15 months of data in Redshift to reduce costs.

Option A: 'Configure the Amazon Redshift Federated Query feature to query live transactional data that is in the PostgreSQL database.' Redshift Federated Query allows querying live transactional data directly from Aurora PostgreSQL without having to move it into Redshift, thereby enabling seamless integration of the current data in Redshift and live data in PostgreSQL. This is a cost-effective approach, as it avoids unnecessary data duplication.

Option C: 'Schedule a monthly job to copy data that is older than 15 months to Amazon S3 by using the UNLOAD command. Delete the old data from the Redshift cluster. Configure Amazon Redshift Spectrum to access historical data in Amazon S3.' This option uses Amazon Redshift Spectrum, which enables Redshift to query data directly in S3 without moving it into Redshift. By unloading older data (older than 15 months) to S3, and then using Spectrum to access it, this approach reduces storage costs significantly while still allowing the data to be queried when necessary.

Option B (Redshift Spectrum for live PostgreSQL data) is not applicable, as Redshift Spectrum is intended for querying data in Amazon S3, not live transactional data in Aurora.

Option D (S3 Glacier Flexible Retrieval) is not suitable because Glacier is designed for long-term archival storage with infrequent access, and querying data in Glacier for analytics purposes would incur higher retrieval times and costs.

Option E (materialized views) would not meet the need to archive data or combine it from multiple sources; it is best suited for combining frequently accessed data already in Redshift.

Amazon Redshift Federated Query

Amazon Redshift Spectrum Documentation

Amazon Redshift UNLOAD Command

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asked 29/10/2024
Pavel Tylich
37 questions
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