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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?
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Question 60 - DEA-C01 discussion

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A data engineer is using Amazon Athena to analyze sales data that is in Amazon S3. The data engineer writes a query to retrieve sales amounts for 2023 for several products from a table named sales_data. However, the query does not return results for all of the products that are in the sales_data table. The data engineer needs to troubleshoot the query to resolve the issue.

The data engineer's original query is as follows:

SELECT product_name, sum(sales_amount)

FROM sales_data

WHERE year = 2023

GROUP BY product_name

How should the data engineer modify the Athena query to meet these requirements?

A.

Replace sum(sales amount) with count(*J for the aggregation.

Answers
A.

Replace sum(sales amount) with count(*J for the aggregation.

B.

Change WHERE year = 2023 to WHERE extractlyear FROM sales data) = 2023.

Answers
B.

Change WHERE year = 2023 to WHERE extractlyear FROM sales data) = 2023.

C.

Add HAVING sumfsales amount) > 0 after the GROUP BY clause.

Answers
C.

Add HAVING sumfsales amount) > 0 after the GROUP BY clause.

D.

Remove the GROUP BY clause

Answers
D.

Remove the GROUP BY clause

Suggested answer: B

Explanation:

The original query does not return results for all of the products because the year column in the sales_data table is not an integer, but a timestamp. Therefore, the WHERE clause does not filter the data correctly, and only returns the products that have a null value for the year column. To fix this, the data engineer should use the extract function to extract the year from the timestamp and compare it with 2023. This way, the query will return the correct results for all of the products in the sales_data table. The other options are either incorrect or irrelevant, as they do not address the root cause of the issue. Replacing sum with count does not change the filtering condition, adding HAVING clause does not affect the grouping logic, and removing the GROUP BY clause does not solve the problem of missing products.Reference:

Troubleshooting JSON queries - Amazon Athena(Section: JSON related errors)

When I query a table in Amazon Athena, the TIMESTAMP result is empty(Section: Resolution)

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 7, page 197)

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asked 29/10/2024
Nagarajapandian T
27 questions
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