Question 1

A company uses Amazon S3 as a data lake. The company sets up a data warehouse by using a multi-node

Amazon Redshift cluster. The company organizes the data files in the data lake based on the data source of

each data file.

The company loads all the data files into one table in the Redshift cluster by using a separate COPY command

for each data file location. This approach takes a long time to load all the data files into the table. The

company must increase the speed of the data ingestion. The company does not want to increase the cost of the

process.

Which solution will meet these requirements?

Options :

A :

Use a provisioned Amazon EMR cluster to copy all the data files into one folder. Use a COPY command to load the data into Amazon Redshift.

B :

Load all the data files in parallel into Amazon Aurora. Run an AWS Glue job to load the data into Amazon Redshift.

C :

Use an AWS Glue job to copy all the data files into one folder. Use a COPY command to load the data into Amazon Redshift.

D :

Create a manifest file that contains the data file locations. Use a COPY command to load the data into Amazon Redshift.

Answer: D

Question 2

A Data Engineering team is working on deploying a serverless data processing solution that includes AWS Lambda functions, AWS Step Functions for orchestrating workflows, and Amazon DynamoDB tables for storing processed data. The team needs a streamlined, manageable, and repeatable way to deploy and update this serverless infrastructure across multiple AWS accounts and regions. They also require the ability to roll back changes in case of errors and to manage different configurations for testing and production environments.

Which of the following deployment strategies would best fit these requirements?

Options :

A : Rely on Terraform as an infrastructure as code solution to define and deploy all components. Use Terraform workspaces to manage different configurations for testing and production environments.

B : Develop a unified AWS SAM template incorporating the Lambda functions, Step Functions, and DynamoDB tables. Utilize the AWS SAM CLI to deploy to different accounts and regions, managing different configurations with environment-specific SAM parameter files.

C : Use AWS CloudFormation with separate templates for each service (Lambda, Step Functions, DynamoDB). Handle different configurations for testing and production using parameter files. Manage deployments and updates manually through the AWS Management Console.

D : Implement an AWS Elastic Beanstalk application to manage the deployment and versioning of the serverless components. Configure separate environments within Elastic Beanstalk for testing and production.

Answer: B

Question 3

A Cloud Data Engineering Team is overseeing a data lake stored in Amazon S3, which is experiencing evolving characteristics such as changes in data volume, schema, and frequency of access. To ensure the system remains robust and cost-effective, the team needs to implement solutions that manage these changes without significant disruption.

Which of the following strategies would effectively allow the team to handle the changing characteristics of the S3-stored data? (Select TWO)

Options :

A : Implement Amazon Glue Schema Registry to manage schema evolution and enforce schema compatibility across different data streams.

B : Configure AWS Lake Formation to centrally manage data security, lifecycle, and metadata, accommodating changes in data types and usage patterns.

C : Apply Amazon Redshift-s elastic resize feature to scale computing resources on-demand, anticipating fluctuations in data query volumes.

D : Maintain a static database schema within AWS Glue Data Catalog, ensuring that access to data remains consistent despite changes.

E : Employ Amazon S3 lifecycle policies to automatically transition data to the most appropriate storage class as access patterns change over time.

Answer: A,E

Question 4

In a collaborative project involving several departments, a Cloud Data Engineering team has been tasked with setting up a data lake in AWS. The data lake stores sensitive customer data in an Amazon S3 bucket within the primary AWS account (Account A). However, a partner company needs access to certain datasets for analysis, which will be performed in their own AWS account (Account B). For compliance purposes, it's vital to ensure that data is encrypted in transit and at rest, and the encryption keys must not be shared or transferred out of the primary account.

What approach should the Cloud Data Engineering team adopt to securely configure encryption for access across these AWS account boundaries?

Options :

A : Implement server-side encryption on the S3 bucket using AWS Key Management Service (AWS KMS) with a customer-managed key (CMK) in Account A. Share the CMK with Account B and apply necessary IAM policies in Account B for decryption.

B : Utilize Amazon S3 server-side encryption with customer-provided keys (SSE-C), storing the encryption keys in AWS Secrets Manager within Account A, and grant Account B access to these keys.

C : Configure an Amazon S3 bucket policy granting cross-account access to Account B and enable AWS KMS cross-account encryption by creating a new KMS key in Account B and referencing it in the S3 bucket policy of Account A.

D : Enable Amazon S3 server-side encryption with an AWS-managed key (SSE-S3) and use a resource-based policy on the S3 bucket to grant access to Account B.

Answer: A

Question 5

A company specializing in geospatial data analysis needs to regularly import, store, and query large sets of geospatial data. Their current setup with a traditional SQL database struggles with the performance of complex spatial queries and efficient storage. Their Data Engineering Team is considering moving to a cloud-based solution that can optimize the storage and querying of geospatial data, particularly for location-based queries and spatial analysis.

Which AWS solution would best meet their needs?

Options :

A : Implement Amazon Aurora with MySQL compatibility, utilizing its spatial indexing feature for efficient geospatial data querying and Amazon ElastiCache to improve read performance.

B : Use Amazon DynamoDB with geospatial library for indexing geospatial data and querying, coupled with Amazon S3 for long-term storage.

C : Migrate to Amazon RDS with PostgreSQL and PostGIS extension for enhanced geospatial capabilities and improved query performance.

D : Leverage Amazon Redshift with geospatial data support to handle large-scale spatial queries and integrate with Amazon QuickSight for data visualization.

Answer: C