Question 1

A manufacturing company wants to collect data from sensors. A data engineer needs to implement a solution

that ingests sensor data in near real time.

The solution must store the data to a persistent data store. The solution must store the data in nested JSON

format. The company must have the ability to query from the data store with a latency of less than 10

milliseconds.

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

Options :

A :

Use a self-hosted Apache Kafka cluster to capture the sensor data. Store the data in Amazon S3 for querying.

B : Use AWS Lambda to process the sensor data. Store the data in Amazon S3 for querying.

C :

Use Amazon Kinesis Data Streams to capture the sensor data. Store the data in Amazon DynamoDB for querying.

D :

Use Amazon Simple Queue Service(Amazon SQS) to buffer incomingsensor data. Use AWS Glue to store thedata in Amazon RDS for querying.

Answer: C

Question 2

A Cloud Data Engineering Consultant is advising a healthcare company on setting up a centralized auditing mechanism for their AWS infrastructure. The company has stringent audit requirements, including the need to review and analyze all API calls related to their Amazon EC2 and Amazon RDS instances over the last year. They have enabled AWS CloudTrail and are exploring options for centralized log management and analysis.

Given the need for a simplified, cost-effective, and scalable auditing solution, which option should they choose?

Options :

A : Advise the company to utilize Amazon Athena with CloudTrail logs stored in Amazon S3, implementing partitioning and data lake best practices for optimal querying and analysis of API activities.

B : Propose integrating AWS CloudTrail with Amazon CloudWatch Logs and setting up CloudWatch Logs Insights to perform real-time analysis and monitoring of API activities for EC2 and RDS instances.

C : Recommend setting up AWS Glue crawlers to catalog CloudTrail logs stored in Amazon S3 and use AWS Glue ETL jobs along with Amazon QuickSight for visual analytics of API activities.

D : Configure AWS CloudTrail Lake to store and analyze activity logs for a year, utilizing its query features for in-depth analysis of specific API activities on EC2 and RDS instances.

Answer: D

Question 3

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 4

A Data Engineering Team is using AWS CloudFormation to automate the deployment of their data processing infrastructure, which includes an Amazon EMR cluster for big data processing. They need to ensure that the EMR cluster configuration can be dynamically updated based on different environments (development, staging, production) without creating separate templates for each environment.

Which specific CloudFormation feature should they use to meet this requirement most effectively?

Options :

A : Implement CloudFormation Custom Resources to enable custom processing logic and create environment-specific configurations for the EMR cluster.

B : Employ CloudFormation Mappings to define environment-specific values within the same template, allowing the template to dynamically alter the EMR cluster configuration based on the input environment type.

C : Use CloudFormation StackSets to manage multiple stacks across different accounts and regions with a single template, adapting to each environment-s specific requirements.

D : Utilize CloudFormation Parameter Labels to organize and manage the parameters for different environments, thereby allowing dynamic updates to the EMR cluster configuration based on the selected environment.

Answer: B

Question 5

A company stores CSV files in an Amazon S3 bucket. A data engineer needs to process the data in the CSV

files and store the processed data in a new S3 bucket.

The process needs to rename a column, remove specific columns, ignore the second row of each file, create a

new column based on the values of the first row of the data, and filter the results by a numeric value of a

column.

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

Options :

A : Use AWS Glue Python jobs to read and transform the CSV files.

B : Use an AWS Glue custom crawler to read and transform the CSV files.

C : Use an AWS Glue workflow to build a set of jobs to crawl and transform the CSV files.

D : Use AWS Glue DataBrew recipes to read and transform the CSV files.

Answer: D

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