Question 1

A Data Engineering Team is designing a system to manage a series of dependent data processing and transformation jobs. These jobs involve various AWS services, including AWS Lambda for data manipulation, Amazon S3 for data storage, and AWS Glue for ETL operations. The team needs a robust solution to orchestrate these jobs, ensuring that they execute in a specific sequence and with conditional error handling.

Which AWS service should the team use for the most effective orchestration of these data processing workflows?

Options :

A : Implement Amazon Managed Workflows for Apache Airflow (MWAA) for managing job sequences and dependencies

B : Use AWS Step Functions to coordinate the various data processing and transformation tasks across different AWS services in a cost-effective manner.

C : Configure Amazon EventBridge to trigger and manage the sequence of data processing jobs efficiently.

D : Utilize AWS Glue Workflows for orchestrating the data processing jobs, focusing on ETL operations.

Answer: B

Question 2

A data engineer needs to streamline a process where a specific AWS Lambda function processes data and subsequently triggers an AWS Glue ETL job. This automated workflow should be simple to manage and fully integrated within the AWS ecosystem.

Which AWS service or feature should the data engineer use to orchestrate this sequence of actions with minimal administrative effort?

Options :

A : Set up an AWS CodePipeline where the first stage executes the Lambda function and the subsequent stage triggers the AWS Glue job.

B : Use AWS Batch to manage a job workflow, first executing the Lambda function as a job and then the AWS Glue job.

C : Configure Amazon EventBridge to trigger the Lambda function and then the AWS Glue job upon successful completion.

D : Use AWS Glue triggers to initiate the Lambda function and then the AWS Glue job in sequence.

Answer: D

Question 3

A media company wants to improve a system that recommends media content to customer based on user behavior and preferences. To improve the recommendation system, the company needs to incorporate insights from third-party datasets into the company's existing analytics platform. The company wants to minimize the effort and time required to incorporate third-party datasets. Which solution will meet these requirements with the LEAST operational overhead?

Options :

A : Use API calls to access and integrate third-party datasets from AWS Data Exchange.

B : Use API calls to access and integrate third-party datasets from AWS

C :

Use Amazon Kinesis Data Streams to access and integrate third-party datasets from AWS CodeCommit repositories.

D :

Use Amazon Kinesis Data Streams to access and integrate third-party datasets from Amazon Elastic Container Registry (Amazon ECR).

Answer: A

Question 4

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?

Options :

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

Question 5

A data analytics team is using Amazon Redshift to query a product_table that contains a year column with the format YYYY-MM-DD. They are attempting to retrieve all sales data for the year 2023 using the following SQL query:

SELECT product_name, sum(price)

FROM product_table

WHERE year = 2023

GROUP BY product_name

However, the query does not return any results. What could be the reason for this issue?

Options :

A : An ORDER BY clause needs to be added to the query to properly sort and display the results.

B : The query needs a HAVING clause to include products with non-zero prices: HAVING sum(price) > 0.

C : The WHERE clause is incorrectly filtering the year column; it should use the EXTRACT(YEAR FROM YEAR) function: WHERE EXTRACT(YEAR FROM YEAR) = 2023.

D : The sum(price) function should be replaced with count(*) to count all product occurrences in 2023

Answer: C

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