Data-Engineer-Associate Exam Dumps - Amazon Web Services AWS Certified Associate Questions and Answers

Question # 24

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?

Options:

Select * from Sales where city_name - '$(San|EI)";

Select * from Sales where city_name -, ^(San|EI) *';

Select * from Sales where city_name - '$(San&EI)";

Select * from Sales where city_name -, ^(San&EI)";

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Question # 25

A mobile gaming company wants to capture data from its gaming app. The company wants to make the data available to three internal consumers of the data. The data records are approximately 20 KB in size.

The company wants to achieve optimal throughput from each device that runs the gaming app. Additionally, the company wants to develop an application to process data streams. The stream-processing application must have dedicated throughput for each internal consumer.

Which solution will meet these requirements?

Options:

Configure the mobile app to call the PutRecords API operation to send data to Amazon Kinesis Data Streams. Use the enhanced fan-out feature with a stream for each internal consumer.

Configure the mobile app to call the PutRecordBatch API operation to send data to Amazon Data Firehose. Submit an AWS Support case to turn on dedicated throughput for the company's AWS account. Allow each internal consumer to access the stream.

Configure the mobile app to use the Amazon Kinesis Producer Library (KPL) to send data to Amazon Data Firehose. Use the enhanced fan-out feature with a stream for each internal consumer.

Configure the mobile app to call the PutRecords API operation to send data to Amazon Kinesis Data Streams. Host the stream-processing application for each internal consumer on Amazon EC2 instances. Configure auto scaling for the EC2 instances.

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Question # 26

A gaming company uses Amazon Kinesis Data Streams to collect clickstream data. The company uses Amazon Kinesis Data Firehose delivery streams to store the data in JSON format in Amazon S3. Data scientists at the company use Amazon Athena to query the most recent data to obtain business insights.

The company wants to reduce Athena costs but does not want to recreate the data pipeline.

Which solution will meet these requirements with the LEAST management effort?

Options:

Change the Firehose output format to Apache Parquet. Provide a custom S3 object YYYYMMDD prefix expression and specify a large buffer size. For the existing data, create an AWS Glue extract, transform, and load (ETL) job. Configure the ETL job to combine small JSON files, convert the JSON files to large Parquet files, and add the YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena

B.
Create an Apache Spark job that combines JSON files and converts the JSON files to Apache Parquet files. Launch an Amazon EMR ephemeral cluster every day to run the Spark job to create new Parquet files in a different S3 location. Use the ALTER TABLE SET LOCATION statement to reflect the new S3 location on the existing Athena table.

C.
Create a Kinesis data stream as a delivery destination for Firehose. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to run Apache Flink on the Kinesis data stream. Use Flink to aggregate the data and save the data to Amazon S3 in Apache Parquet format with a custom S3 object YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena table.<

D.
Integrate an AWS Lambda function with Firehose to convert source records to Apache Parquet and write them to Amazon S3. In parallel, run an AWS Glue extract, transform, and load (ETL) job to combine the JSON files and convert the JSON files to large Parquet files. Create a custom S3 object YYYYMMDD prefix. Use the ALTER TABLE ADD PARTITION statement to reflect the partition on the existing Athena table.

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Answer:
A
Explanation:

Step 1: Understanding the Problem
The company collectsclickstream datavia Amazon Kinesis Data Streams and stores it inJSON formatin Amazon S3 using Kinesis Data Firehose. They useAmazon Athenato query the data, but they want toreduce Athena costswhile maintaining the same data pipeline.
Since Athena charges based on the amount of data scanned during queries, reducing the data size (by converting JSON to a more efficient format likeApache Parquet) is a key solution to lowering costs.
Step 2: Why Option A is Correct
Option Aprovides a straightforward way to reduce costs withminimal management overhead:
Changing the Firehose output format to Parquet: Parquet is a columnar data format, which is more compact and efficient than JSON for Athena queries. It significantly reduces the amount of data scanned, which in turn reduces Athena query costs.
Custom S3 Object Prefix (YYYYMMDD): Adding a date-based prefix helps in partitioning the data, which further improves query efficiency in Athena by limiting the data scanned to only relevant partitions.
AWS Glue ETL Job for Existing Data: To handle existing data stored in JSON format, a one-time AWS Glue ETL job can combine small JSON files, convert them to Parquet, and apply the YYYYMMDD prefix. This ensures consistency in the S3 bucket structure and allows Athena to efficiently query historical data.
ALTER TABLE ADD PARTITION: This command updates Athena's table metadata to reflect the new partitions, ensuring that future queries target only the required data.
Step 3: Why Other Options Are Not Ideal
Option B (Apache Spark on EMR)introduces higher management effort by requiring the setup ofApache Spark jobsand anAmazon EMR cluster. While it achieves the goal of converting JSON to Parquet, it involves running and maintaining an EMR cluster, which adds operational complexity.
Option C (Kinesis and Apache Flink)is a more complex solution involvingApache Flink, which adds a real-time streaming layer to aggregate data. Although Flink is a powerful tool for stream processing, it adds unnecessary overhead in this scenario since the company already uses Kinesis Data Firehose for batch delivery to S3.
Option D (AWS Lambda with Firehose)suggests usingAWS Lambdato convert records in real time. While Lambda can work in some cases, it's generally not the best tool for handling large-scale data transformations like JSON-to-Parquet conversion due to potential scaling and invocation limitations. Additionally, running parallel Glue jobs further complicates the setup.
Step 4: How Option A Minimizes Costs
By usingApache Parquet, Athena queries become more efficient, as Athena will scan significantly less data, directly reducing query costs.
Firehosenatively supports Parquet as an output format, so enabling this conversion in Firehose requires minimal effort. Once set, new data will automatically be stored in Parquet format in S3, without requiring any custom coding or ongoing management.
TheAWS Glue ETL jobfor historical data ensures that existing JSON files are also converted to Parquet format, ensuring consistency across the data stored in S3.
Conclusion:
Option A meets the requirement toreduce Athena costswithout recreating the data pipeline, using Firehose’s native support forApache Parquetand a simple one-timeAWS Glue ETL jobfor existing data. This approach involvesminimal management effortcompared to the other solutions.

Question # 27

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?

Options:

A.
Create and run an Apache Spark job in an AWS Glue notebook. Configure the job to read the S3 file and calculate the number of distinct customers.

B.
Create an AWS Glue crawler to create an AWS Glue Data Catalog of the S3 file. Run SQL queries from Amazon Athena to calculate the number of distinct customers.

C.
Create and run an Apache Spark job in Amazon EMR Serverless to calculate the number of distinct customers.

D.
Use AWS Glue DataBrew to create a recipe that uses the COUNT_DISTINCT aggregate function to calculate the number of distinct customers.

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Answer:
D
Explanation:

AWS Glue DataBrew is a visual data preparation tool that allows you to clean, normalize, and transform data without writing code. You can use DataBrew to create recipes that define the steps to apply to your data, such as filtering, renaming, splitting, or aggregating columns. You can also use DataBrew to run jobs that execute the recipes on your data sources, such as Amazon S3, Amazon Redshift, or Amazon Aurora. DataBrew integrates with AWS Glue Data Catalog, which is a centralized metadata repository for your data assets1.
The solution that meets the requirement with the least operational effort is to use AWS Glue DataBrew to create a recipe that uses the COUNT_DISTINCT aggregate function to calculate the number of distinct customers. This solution has the following advantages:
It does not require you to write any code, as DataBrew provides a graphical user interface that lets you explore, transform, and visualize your data. You can use DataBrew to concatenate the columns that contain customer first names and last names, and then use the COUNT_DISTINCT aggregate function to count the number of unique values in the resulting column2.
It does not require you to provision, manage, or scale any servers, clusters, or notebooks, as DataBrew is a fully managed service that handles all the infrastructure for you. DataBrew can automatically scale up or down the compute resources based on the size and complexity of your data and recipes1.
It does not require you to create or update any AWS Glue Data Catalog entries, as DataBrew can automatically create and register the data sources and targets in the Data Catalog. DataBrew can also use the existing Data Catalog entries to access the data in S3 or other sources3.
Option A is incorrect because it suggests creating and running an Apache Spark job in an AWS Glue notebook. This solution has the following disadvantages:
It requires you to write code, as AWS Glue notebooks are interactive development environments that allow you to write, test, and debug Apache Spark code using Python or Scala. You need to use the Spark SQL or the Spark DataFrame API to read the S3 file and calculate the number of distinct customers.
It requires you to provision and manage a development endpoint, which is a serverless Apache Spark environment that you can connect to your notebook. You need to specify the type and number of workers for your development endpoint, and monitor its status and metrics.
It requires you to create or update the AWS Glue Data Catalog entries for the S3 file, either manually or using a crawler. You need to use the Data Catalog as a metadata store for your Spark job, and specify the database and table names in your code.
Option B is incorrect because it suggests creating an AWS Glue crawler to create an AWS Glue Data Catalog of the S3 file, and running SQL queries from Amazon Athena to calculate the number of distinct customers. This solution has the following disadvantages:
It requires you to create and run a crawler, which is a program that connects to your data store, progresses through a prioritized list of classifiers to determine the schema for your data, and then creates metadata tables in the Data Catalog. You need to specify the data store, the IAM role, the schedule, and the output database for your crawler.
It requires you to write SQL queries, as Amazon Athena is a serverless interactive query service that allows you to analyze data in S3 using standard SQL. You need to use Athena to concatenate the columns that contain customer first names and last names, and then use the COUNT(DISTINCT) aggregate function to count the number of unique values in the resulting column.
Option C is incorrect because it suggests creating and running an Apache Spark job in Amazon EMR Serverless to calculate the number of distinct customers. This solution has the following disadvantages:
It requires you to write code, as Amazon EMR Serverless is a service that allows you to run Apache Spark jobs on AWS without provisioning or managing any infrastructure. You need to use the Spark SQL or the Spark DataFrame API to read the S3 file and calculate the number of distinct customers.
It requires you to create and manage an Amazon EMR Serverless cluster, which is a fully managed and scalable Spark environment that runs on AWS Fargate. You need to specify the cluster name, the IAM role, the VPC, and the subnet for your cluster, and monitor its status and metrics.
It requires you to create or update the AWS Glue Data Catalog entries for the S3 file, either manually or using a crawler. You need to use the Data Catalog as a metadata store for your Spark job, and specify the database and table names in your code.
[:, 1: AWS Glue DataBrew - Features, 2: Working with recipes - AWS Glue DataBrew, 3: Working with data sources and data targets - AWS Glue DataBrew, [4]: AWS Glue notebooks - AWS Glue, [5]: Development endpoints - AWS Glue, [6]: Populating the AWS Glue Data Catalog - AWS Glue, [7]: Crawlers - AWS Glue, [8]: Amazon Athena - Features, [9]: Amazon EMR Serverless - Features, [10]: Creating an Amazon EMR Serverless cluster - Amazon EMR, [11]: Using the AWS Glue Data Catalog with Amazon EMR Serverless - Amazon EMR, , ]

Question # 28

A company created an extract, transform, and load (ETL) data pipeline in AWS Glue. A data engineer must crawl a table that is in Microsoft SQL Server. The data engineer needs to extract, transform, and load the output of the crawl to an Amazon S3 bucket. The data engineer also must orchestrate the data pipeline.
Which AWS service or feature will meet these requirements MOST cost-effectively?

Options:

A.
AWS Step Functions

B.
AWS Glue workflows

C.
AWS Glue Studio

D.
Amazon Managed Workflows for Apache Airflow (Amazon MWAA)

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Answer:
B
Explanation:

AWS Glue workflows are a cost-effective way to orchestrate complex ETL jobs that involve multiple crawlers, jobs, and triggers. AWS Glue workflows allow you to visually monitor the progress and dependencies of your ETL tasks, and automatically handle errors and retries. AWS Glue workflows also integrate with other AWS services, such as Amazon S3, Amazon Redshift, and AWS Lambda, among others, enabling you to leverage these services for your data processing workflows. AWS Glue workflows are serverless, meaning you only pay for the resources you use, and you don’t have to manage any infrastructure.
AWS Step Functions, AWS Glue Studio, and Amazon MWAA are also possible options for orchestrating ETL pipelines, but they have some drawbacks compared to AWS Glue workflows. AWS Step Functions is a serverless function orchestrator that can handle different types of data processing, such as real-time, batch, and stream processing. However, AWS Step Functions requires you to write code to define your state machines, which can be complex and error-prone. AWS Step Functions also charges you for every state transition, which can add up quickly for large-scale ETL pipelines.
AWS Glue Studio is a graphical interface that allows you to create and run AWS Glue ETL jobs without writing code. AWS Glue Studio simplifies the process of building, debugging, and monitoring your ETL jobs, and provides a range of pre-built transformations and connectors. However, AWS Glue Studio does not support workflows, meaning you cannot orchestrate multiple ETL jobs or crawlers with dependencies and triggers. AWS Glue Studio also does not support streaming data sources or targets, which limits its use cases for real-time data processing.
Amazon MWAA is a fully managed service that makes it easy to run open-source versions of Apache Airflow on AWS and build workflows to run your ETL jobs and data pipelines. Amazon MWAA provides a familiar and flexible environment for data engineers who are familiar with Apache Airflow, and integrates with a range of AWS services such as Amazon EMR, AWS Glue, and AWS Step Functions. However, Amazon MWAA is not serverless, meaning you have to provision and pay for the resources you need, regardless of your usage. Amazon MWAA also requires you to write code to define your DAGs, which can be challenging and time-consuming for complex ETL pipelines. References:
AWS Glue Workflows
AWS Step Functions
AWS Glue Studio
Amazon MWAA
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

Question # 29

A company has a data warehouse in Amazon Redshift. To comply with security regulations, the company needs to log and store all user activities and connection activities for the data warehouse.
Which solution will meet these requirements?

Options:

A.
Create an Amazon S3 bucket. Enable logging for the Amazon Redshift cluster. Specify the S3 bucket in the logging configuration to store the logs.

B.
Create an Amazon Elastic File System (Amazon EFS) file system. Enable logging for the Amazon Redshift cluster. Write logs to the EFS file system.

C.
Create an Amazon Aurora MySQL database. Enable logging for the Amazon Redshift cluster. Write the logs to a table in the Aurora MySQL database.

D.
Create an Amazon Elastic Block Store (Amazon EBS) volume. Enable logging for the Amazon Redshift cluster. Write the logs to the EBS volume.

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Answer:
A
Explanation:

Problem Analysis:
The company must log alluser activitiesandconnection activitiesin Amazon Redshift for security compliance.
Key Considerations:
Redshift supportsaudit logging, which can be configured to write logs to an S3 bucket.
S3 provides durable, scalable, and cost-effective storage for logs.
Solution Analysis:
Option A: S3 for Logging
Standard approach for storing Redshift logs.
Easy to set up and manage with minimal cost.
Option B: Amazon EFS
EFS is unnecessary for this use case and less cost-efficient than S3.
Option C: Aurora MySQL
Using a database to store logs increases complexity and cost.
Option D: EBS Volume
EBS is not a scalable option for log storage compared to S3.
Final Recommendation:
EnableRedshift audit loggingand specify anS3 bucketas the destination.
[:, Amazon Redshift Audit Logging, Storing Logs in Amazon S3, , ]

Question # 30

A company is setting up a data pipeline in AWS. The pipeline extracts client data from Amazon S3 buckets, performs quality checks, and transforms the data. The pipeline stores the processed data in a relational database. The company will use the processed data for future queries.
Which solution will meet these requirements MOST cost-effectively?

Options:

A.
Use AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use AWS Glue Data Quality to enforce suggested quality rules. Load the data and the quality check results into an Amazon RDS for MySQL instance.

B.
Use AWS Glue Studio to extract the data from the S3 buckets. Use AWS Glue DataBrew to perform the transformations and quality checks. Load the processed data into an Amazon RDS for MySQL instance. Load the quality check results into a new S3 bucket.

C.
Use AWS Glue ETL to extract the data from the S3 buckets and perform the transformations. Use AWS Glue DataBrew to perform quality checks. Load the processed data and the quality check results into a new S3 bucket.

D.
Use AWS Glue Studio to extract the data from the S3 buckets. Use AWS Glue DataBrew to perform the transformations and quality checks. Load the processed data and quality check results into an Amazon RDS for MySQL instance.

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Answer:
A
Explanation:

AWS Glue ETL is designed for scalable and serverless data processing, and it supports integrated quality enforcement usingAWS Glue Data Quality, which makes it the most cost-effective and integrated option when combined withAmazon RDS for MySQLas the relational database.
“AWS Glue can perform data validation as part of the ETL process, ensuring data quality before storingthe data in the target data store.”
–Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf
Using AWS Glue Data Quality directly in the ETL workflow is simpler and more cost-effective than separating transformation (Glue) and validation (DataBrew) into different services.

Question # 31

A data engineer uses Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to run data pipelines in an AWS account. A workflow recently failed to run. The data engineer needs to use Apache Airflow logs to diagnose the failure of the workflow. Which log type should the data engineer use to diagnose the cause of the failure?

Options:

A.
YourEnvironmentName-WebServer

B.
YourEnvironmentName-Scheduler

C.
YourEnvironmentName-DAGProcessing

D.
YourEnvironmentName-Task

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Answer:
D
Explanation:

In Amazon Managed Workflows for Apache Airflow (MWAA), the type of log that is most useful for diagnosing workflow (DAG) failures is theTask logs. These logs provide detailed information on the execution of each task within the DAG, including error messages, exceptions, and other critical details necessary for diagnosing failures.
Option D: YourEnvironmentName-TaskTask logs capture the output from the execution of each task within a workflow (DAG), which is crucial for understanding what went wrong when a DAG fails. These logs contain detailed execution information, including errors and stack traces, making them the best source for debugging.
Other options(WebServer, Scheduler, and DAGProcessing logs) provide general environment-level logs or logs related to scheduling and DAG parsing, but they do not provide the granular task-level execution details needed for diagnosing workflow failures.
[References:, Amazon MWAA Logging and Monitoring, Apache Airflow Task Logs, ]

Question # 32

A company uses Amazon Redshift as a data warehouse solution. One of the datasets that the company stores in Amazon Redshift contains data for a vendor.
Recently, the vendor asked the company to transfer the vendor's data into the vendor's Amazon S3 bucket once each week.
Which solution will meet this requirement?

Options:

A.
Create an AWS Lambda function to connect to the Redshift data warehouse. Configure the Lambda function to use the Redshift COPY command to copy the required data to the vendor's S3 bucket on a schedule.

B.
Create an AWS Glue job to connect to the Redshift data warehouse. Configure the AWS Glue job to use the Redshift UNLOAD command to load the required data to the vendor's S3 bucket on a schedule.

C.
Use the Amazon Redshift data sharing feature. Set the vendor's S3 bucket as the destination. Configure the source to be as a custom SQL query that selects the required data.

D.
Configure Amazon Redshift Spectrum to use the vendor's S3 bucket as destination. Enable dataquerying in both directions.

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Answer:
B
Explanation:

TheRedshift UNLOADcommand is specifically designed to export query results toAmazon S3, andAWS Gluecan orchestrate this as part of a scheduled job. This is the cleanest and most appropriate approach for recurring weekly data transfers:
“Use the Redshift UNLOAD command with AWS Glue to export data to Amazon S3. This pattern enables routine exports of selected data to external locations.”
–Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf
This avoids complexities of Redshift Spectrum or unsupported use of COPY commands in Lambda.

Question # 33

A data engineer maintains a materialized view that is based on an Amazon Redshift database. The view has a column named load_date that stores the date when each row was loaded.
The data engineer needs to reclaim database storage space by deleting all the rows from the materialized view.
Which command will reclaim the MOST database storage space?

Options:

A.
Option A

B.
Option B

C.
Option C

D.
Option D

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Answer:
A
Explanation:

To reclaim the most storage space from a materialized view in Amazon Redshift, you should use aDELETEoperation that removes all rows from the view. The most efficient way to remove all rows is to use a condition that always evaluates to true, such as1=1.This will delete all rows without needing to evaluate each row individually based on specific column values like load_date.
Option A: DELETE FROM materialized_view_name WHERE 1=1;This statement will delete all rows in the materialized view and free up the space. Since materialized views in Redshift store precomputed data, performing a DELETE operation will remove all stored rows.
Other options either involve inappropriate SQL statements (e.g., VACUUM in option C is used for reclaiming storage space in tables, not materialized views), or they don’t remove data effectively in the context of a materialized view (e.g., TRUNCATE cannot be used directly on a materialized view).
[References:, Amazon Redshift Materialized Views Documentation, Deleting Data from Redshift, ]

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Exam Code: Data-Engineer-Associate

Exam Name: AWS Certified Data Engineer - Associate (DEA-C01)

Last Update: Jun 20, 2025

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