Question 1

A sales company uses AWS Glue ETL to collect, process, and ingest data into an Amazon S3 bucket. The AWS Glue pipeline creates a new file in the S3 bucket every hour. File sizes vary from 200 KB to 300 KB. The company wants to build a sales prediction model by using data from the previous 5 years. The historic data includes 44,000 files. The company builds a second AWS Glue ETL pipeline by using the smallest worker type. The second pipeline retrieves the historic files from the S3 bucket and processes the files for downstream analysis. The company notices significant performance issues with the second ETL pipeline. The company needs to improve the performance of the second pipeline. Which solution will meet this requirement MOST cost-effectively?

Options :

A : Use a larger worker type.

B : Increase the number of workers in the AWS Glue ETL jobs.

C : Use the AWS Glue DynamicFrame grouping option.

D : Enable AWS Glue auto scaling.

Answer: D

Question 2

A Data Engineering Team is tasked with enhancing the CI/CD workflow of their complex ETL pipeline on AWS. The pipeline involves frequent but varying updates—from minor feature additions to major architectural overhauls. The team uses GitHub for version control and manages a diverse array of AWS resources in their projects.

They require a CI/CD solution that not only automates build and test processes upon each commit but also allows for customizable, condition-based deployment strategies (e.g., deploying only specific branches or tags, or based on commit messages).

Given these requirements, which AWS service or combination of services would be the most effective?

Options :

A : AWS Lambda and AWS CodePipeline, where a Lambda function is triggered by GitHub webhooks to determine if the deployment should proceed, followed by CodePipeline managing the build, test, and deployment phases.

B : AWS CodeCommit and AWS CodePipeline, with CodeCommit to replace GitHub for enhanced integration within the AWS ecosystem, and CodePipeline to orchestrate the entire process including condition-based deployments.

C : AWS CodePipeline, integrating both GitHub and AWS CodeBuild. CodePipeline to manage the overall workflow and CodeBuild for the build and testing phases, leveraging pipeline actions for deployment conditions.

D : AWS CodeBuild and AWS CodeDeploy, with CodeBuild handling the build and test processes and CodeDeploy managing advanced deployment strategies based on GitHub webhook events.

Answer: C

Question 3

A financial services firm is experiencing rising costs associated with monthly data migrations from their on-premises Oracle database to AWS. They need a more economical method to transfer their extensive transaction records to an Amazon RDS for Oracle instance with minimal disruption to their ongoing operations.

What AWS service should the firm utilize to achieve a cost-efficient and low-downtime migration?

Options :

A : Configure an AWS Snowball job to handle the end-of-month data transfers.

B : Establish AWS Direct Connect for a dedicated network connection to AWS.

C : Use AWS DataSync to schedule and automate the data transfer process.

D : Implement AWS Database Migration Service (AWS DMS) to manage ongoing data migrations.

Answer: D

Question 4

A data engineer is building a data pipeline on AWS by using AWS Glue extract, transform, and load (ETL) jobs. The data engineer needs to process data from Amazon RDS and MongoDB, perform transformations, and load the transformed data into Amazon Redshift for analytics. The data updates must occur every hour. Which combination of tasks will meet these requirements with the LEAST operational overhead? (Choose two.)

Options :

A : Configure AWS Glue triggers to run the ETL jobs even/ hour.

B : Use AWS Glue DataBrewto clean and prepare the data for analytics.

C : Use AWS Lambda functions to schedule and run the ETL jobs even/ hour.

D : Use AWS Glue connections to establish connectivity between the data sources and Amazon Redshift.

E : Use the Redshift Data API to load transformed data into Amazon Redshift.

Answer: A,D

Question 5

The Data Engineering Team at an online retail company is using Amazon SageMaker Data Wrangler to clean and transform sales data before loading it into their analytics platform. The dataset includes transaction dates, product categories, sales amounts, and customer demographics. The team noted inconsistencies in date formats and several categorical fields with unstandardized text entries.

Which steps should they take in Data Wrangler to resolve these issues effectively?

Options :

A : In Data Wrangler, first perform statistical analysis on all numerical fields to identify outliers, then address the date format inconsistencies and unstandardized categorical fields using a combination of built-in string functions and manual adjustments.

B : Use Data Wrangler to transform date fields into a uniform format by selecting the appropriate date functions and employ the built-in string functions to standardize and clean categorical fields, then proceed to export the data for further processing.

C : Apply transformations in Data Wrangler to convert date fields into a uniform format using its built-in date functions. For the categorical fields, leverage the 'Find and Replace' tool to standardize text entries before exporting the dataset.

D : Utilize Data Wrangler to standardize date formats using custom Python scripts. For categorical fields, manually create new columns to capture standardized text entries, then export the data for analysis.

Answer: B

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