DATA-ENGINEER-ASSOCIATE RELIABLE EXAM SIMULATIONS & PRACTICE DATA-ENGINEER-ASSOCIATE MOCK

Data-Engineer-Associate Reliable Exam Simulations & Practice Data-Engineer-Associate Mock

Data-Engineer-Associate Reliable Exam Simulations & Practice Data-Engineer-Associate Mock

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q72-Q77):

NEW QUESTION # 72
A financial company recently added more features to its mobile app. The new features required the company to create a new topic in an existing Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster.
A few days after the company added the new topic, Amazon CloudWatch raised an alarm on the RootDiskUsed metric for the MSK cluster.
How should the company address the CloudWatch alarm?

  • A. Expand the storage of the Apache ZooKeeper nodes.
  • B. Specify the Target-Volume-in-GiB parameter for the existing topic.
  • C. Expand the storage of the MSK broker. Configure the MSK cluster storage to expand automatically.
  • D. Update the MSK broker instance to a larger instance type. Restart the MSK cluster.

Answer: C

Explanation:
The RootDiskUsed metric for the MSK cluster indicates that the storage on the broker is reaching its capacity. The best solution is to expand the storage of the MSK broker and enable automatic storage expansion to prevent future alarms.
* Expand MSK Broker Storage:
* AWS Managed Streaming for Apache Kafka (MSK) allows you to expand the broker storage to accommodate growing data volumes. Additionally, auto-expansion of storage can be configured to ensure that storage grows automatically as the data increases.


NEW QUESTION # 73
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_dat a. 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. Change WHERE year = 2023 to WHERE extractlyear FROM sales data) = 2023.
  • B. Remove the GROUP BY clause
  • C. Add HAVING sumfsales amount) > 0 after the GROUP BY clause.
  • D. Replace sum(sales amount) with count(*J for the aggregation.

Answer: A

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)


NEW QUESTION # 74
A company has a data warehouse that contains a table that is named Sales. The company stores the table in Amazon Redshift The table includes a column that is named city_name. The company wants to query the table to find all rows that have a city_name that starts with "San" or "El." Which SQL query will meet this requirement?