Amazon Data-Engineer-Associate Practice Exam Questions Free

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Total 485 Questions | Updated On: Jan 10, 2025

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Question 1

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 : Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the Aurora data catalog. Schedule the Lambda functions to run periodically.

B : Use the AWS Glue Data Catalog as the central metadata repository. Use AWS Glue crawlers to connect to multiple data stores and to update the Data Catalog with metadata changes. Schedule the crawlers to run periodically to update the metadata catalog.

C : Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the DynamoDB data catalog. Schedule the Lambda functions to run periodically.

D : Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for AmazonRDS and Amazon Redshift sources, and build the Data Catalog. Use AWS Glue crawlers for data that isin Amazon S3 to infer the schema and to automatically update the Data Catalog.

Answer: B

Question 2

A data engineer wants to orchestrate a set of extract, transform, and load (ETL) jobs that run on AWS. TheETL jobs contain tasks that must run Apache Spark jobs on Amazon EMR, make API calls to Salesforce, andload data into Amazon Redshift.The ETL jobs need to handle failures and retries automatically. The data engineer needs to use Python toorchestrate the jobs.Which service will meet these requirements?

A : Amazon Managed Workflows for Apache Airflow (Amazon MWAA)

B : AWS Step Functions

C : AWS Glue

D : Amazon EventBridge

Answer: A

Question 3

A Data Engineering team is planning to perform sampling on a large dataset stored in Amazon S3 for exploratory data analysis. Their goal is to understand the overall trends without processing the entire dataset. The dataset is structured and spread across multiple S3 objects.

Which AWS service and method should the team use to implement an efficient and cost-effective sampling strategy?

A : Implement AWS Glue with a custom Python script to sequentially scan through each S3 object and randomly pick samples from each.

B : Leverage AWS Data Pipeline to orchestrate and move subsets of the data into Amazon RDS, where sampling can be done using SQL queries.

C : Use Amazon S3 Select to efficiently sample individual records from each S3 object, thus reducing the amount of data loaded and processed.

D : Configure Amazon Redshift Spectrum to load the full dataset into a Redshift cluster, and then use SQL queries to perform random sampling.

Answer: C

Question 4

A company is migrating its database servers from Amazon EC2 instances that run Microsoft SQL Server to

Amazon RDS for Microsoft SQL Server DB instances. The company's analytics team must export large data

elements every day until the migration is complete. The data elements are the result of SQL joinsacross

multiple tables. The data must be in Apache Parquet format. The analytics team must store the data in Amazon

S3.

Which solution will meet these requirements in the MOST operationally efficient way?

A : Create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create an AWS Glue job that selects the data directly from the view and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.

B : Schedule SQL Server Agent to run a daily SQL query that selects the desired data elements from the EC2 instance-based SQL Server databases. Configure the query to direct the output .csv objects to an S3 bucket. Create an S3 event that invokes an AWS Lambda function to transform the output format from .csv to Parquet.

C : Use a SQL query to create a view in the EC2 instance-based SQL Server databases that contains the required data elements. Create and run an AWS Glue crawler to read the view. Create an AWS Glue job that retrieves the data and transfers the data in Parquet format to an S3 bucket. Schedule the AWS Glue job to run every day.

D : Create an AWS Lambda function that queries the EC2 instance-based databases by using Java Database Connectivity (JDBC). Configure the Lambda function to retrieve the required data, transform the data into Parquet format, and transfer the data into an S3 bucket. Use Amazon EventBridge to schedule the Lambda function to run every day.

Answer: A

Question 5

A company has multiple applications that use datasets that are stored in an Amazon S3 bucket. The company

has an ecommerce application that generates a dataset that contains personally identifiable information (PII).

The company has an internal analytics application that does not require access to the PII.

To comply with regulations, the company must not share PII unnecessarily. A data engineer needs to

implement a solution that with redact PII dynamically, based on the needs of each application that accesses the

dataset.

Which solution will meet the requirements with the LEAST operational overhead?

A : Create an S3 bucket policy to limit the access each application has. Create multiple copies of the dataset. Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.

B : Create an S3 Object Lambda endpoint. Use the S3 Object Lambda endpoint to read data from the S3 bucket. Implement redaction logic within an S3 Object Lambda function to dynamically redact PII based on the needs of each application that accesses the data.

C : Use AWS Glue to transform the data for each application. Create multiple copies of the dataset. Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.

D : Create an API Gateway endpoint that has custom authorizers. Use the API Gateway endpoint to read data from the S3 bucket. Initiate a REST API call to dynamically redact PII based on the needs of each application that accesses the data.

Answer: B

Page: 1 / 97

Total 485 Questions | Updated On: Jan 10, 2025

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