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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q110-Q115):

NEW QUESTION # 110
A data engineer needs to build an extract, transform, and load (ETL) job. The ETL job will process daily incoming .csv files that users upload to an Amazon S3 bucket. The size of each S3 object is less than 100 MB.
Which solution will meet these requirements MOST cost-effectively?

Answer: C

Explanation:
AWS Glue is a fully managed serverless ETL service that can handle various data sources and formats, including .csv files in Amazon S3. AWS Glue provides two types of jobs: PySpark and Python shell. PySpark jobs use Apache Spark to process large-scale data in parallel, while Python shell jobs use Python scripts to process small-scale data in a single execution environment. For this requirement, a Python shell job is more suitable and cost-effective, as the size of each S3 object is less than 100 MB, which does not require distributed processing. A Python shell job can use pandas, a popular Python library for data analysis, to transform the .csv data as needed. The other solutions are not optimal or relevant for this requirement. Writing a custom Python application and hosting it on an Amazon EKS cluster would require more effort and resources to set up and manage the Kubernetes environment, as well as to handle the data ingestion and transformation logic. Writing a PySpark ETL script and hosting it on an Amazon EMR cluster would also incur more costs and complexity to provision and configure the EMR cluster, as well as to use Apache Spark for processing small data files. Writing an AWS Glue PySpark job would also be less efficient and economical than a Python shell job, as it would involve unnecessary overhead and charges for using Apache Spark for small data files. Reference:
AWS Glue
Working with Python Shell Jobs
pandas
[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]


NEW QUESTION # 111
A company stores employee data in Amazon Redshift A table named Employee uses columns named Region ID, Department ID, and Role ID as a compound sort key. Which queries will MOST increase the speed of a query by using a compound sort key of the table? (Select TWO.)

Answer: B,E

Explanation:
In Amazon Redshift, a compound sort key is designed to optimize the performance of queries that use filtering and join conditions on the columns in the sort key. A compound sort key orders the data based on the first column, followed by the second, and so on. In the scenario given, the compound sort key consists of Region ID, Department ID, and Role ID. Therefore, queries that filter on the leading columns of the sort key are more likely to benefit from this order.
* Option B: "Select * from Employee where Region ID='North America' and Department ID=20;" This query will perform well because it uses both the Region ID and Department ID, which are the first two columns of the compound sort key. The order of the columns in the WHERE clause matches the order in the sort key, thus allowing the query to scan fewer rows and improve performance.
* Option C: "Select * from Employee where Department ID=20 and Region ID='North America';" This query also benefits from the compound sort key because it includes both Region ID and Department ID, which are the first two columns in the sort key. Although the order in the WHERE clause does not match exactly, Amazon Redshift will still leverage the sort key to reduce the amount of data scanned, improving query speed.
* Options A, D, and E are less optimal because they do not utilize the sort key as effectively:
* Option A only filters by the Region ID, which may still use the sort key but does not take full advantage of the compound nature.
* Option D uses only Role ID, the last column in the compound sort key, which will not benefit much from sorting since it is the third key in the sort order.
* Option E filters on Region ID and Role ID but skips the Department ID column, making it less efficient for the compound sort key.
References:
* Amazon Redshift Documentation - Sorting Data
* AWS Certified Data Analytics Study Guide
* AWS Certification - Data Engineer Associate Exam Guide


NEW QUESTION # 112
A company uses Amazon S3 buckets, AWS Glue tables, and Amazon Athena as components of a data lake.
Recently, the company expanded its sales range to multiple new states. The company wants to introduce state names as a new partition to the existing S3 bucket, which is currently partitioned by date.
The company needs to ensure that additional partitions will not disrupt daily synchronization between the AWS Glue Data Catalog and the S3 buckets.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: C

Explanation:
Scheduling an AWS Glue crawler to periodically update the Data Catalog automates the process of detecting new partitions and updating the catalog, which minimizes manual maintenance and operational overhead.


NEW QUESTION # 113
A company needs to generate a one-time performance report by joining data that is stored in Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3. The company wants to avoid unnecessary data movement and to minimize query execution time.
Which solution will meet these requirements?

Answer: D

Explanation:
Comprehensive and Detailed Explanation (150-250 words)
Amazon Athena Federated Query enables SQL queries across multiple data sources without moving data.
Athena can directly query Amazon DynamoDB, Amazon RDS, Amazon Redshift, and Amazon S3 using data source connectors. This capability is ideal for one-time or ad hoc analytical queries, where setting up ingestion pipelines would introduce unnecessary cost and delay.
Because the requirement is for a one-time performance report, the solution should minimize operational overhead and data duplication. Athena Federated Query executes queries in place, eliminating ETL processes, data exports, and staging layers. This significantly reduces query preparation time and avoids storage costs associated with copying data into a centralized location.
The other options require extensive data movement and setup. DynamoDB Streams, AWS DMS, Glue ETL pipelines, and Amazon EMR clusters are appropriate for ongoing analytics pipelines, not for a single analytical report. These approaches increase operational complexity and execution time.
Athena Federated Query is purpose-built for this exact scenario and provides the fastest path to insight with minimal overhead.


NEW QUESTION # 114
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?

Answer: C

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 querywill 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. References:
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 # 115
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