Question 1

A Cloud Data Engineering Team is implementing a system for real-time data ingestion through an API. The architecture needs to include data transformation before storage. The system must handle large files and store them efficiently post-transformation. The team is focused on using a serverless architecture on AWS, with an emphasis on Infrastructure as Code (IaC) for standardized and repeatable deployments across various environments.

Which combination of actions should the Cloud Data Engineering Team take to implement IaC for serverless deployments of data ingestion and transformation pipelines? (Select THREE)

Options :

A : Set up AWS Data Pipeline in the AWS SAM template for data movement and transformation.

B : Use AWS Elastic MapReduce (EMR) for data transformation in Kinesis Firehose.

C : Define an Amazon API Gateway in the AWS SAM template for data ingestion.

D : Configure DynamoDB in the AWS SAM template for storing the transformed data.

E : Utilize AWS Serverless Application Model (SAM) to declare AWS Lambda functions integrated with Amazon Kinesis Data Firehose.

F : Configure Amazon S3 bucket creation in the AWS SAM template for storing the transformed data.

Answer: C,E,F

Question 2

The Data Engineering team of a large online retailer is building an AWS-based solution to analyze customer shopping patterns. They need a data structure that efficiently supports frequent insertions and deletions of products, as well as quick lookups to determine if a product is present or absent in the inventory.

What data structure and AWS service should they use to optimize these operations?

Options :

A : Store the product data as a balanced tree (B-tree) in Amazon RDS, taking advantage of SQL queries for insertion, deletion, and lookup operations.

B : Utilize a hash table structure within Amazon ElastiCache using Redis, which provides quick lookups and easy handling of product insertions and deletions.

C : Use a queue structure in Amazon SQS to manage product insertions and deletions, checking for product presence by polling the queue.

D : Implement a binary search tree (BST) stored in Amazon DynamoDB, allowing for efficient lookups, insertions, and deletions based on product IDs.

Answer: B

Question 3

A Data Engineering Team is working on a project where they need to run SQL queries on their Amazon Redshift cluster from various applications and microservices.

These applications are diverse, running on different platforms and languages, and the team requires a simple, scalable, and secure way to execute these queries without managing database connections. They also need the ability to retrieve query results asynchronously.

Which Amazon Redshift feature should the team use to meet these requirements most effectively?

Options :

A : Establish direct JDBC/ODBC connections from each application to the Redshift cluster for query execution.

B : Implement AWS Lambda functions to manage Redshift query executions and handle database connections.

C : Use Amazon Redshift Spectrum for querying data across applications without direct cluster access.

D : Utilize the Amazon Redshift Data API to execute queries and retrieve results asynchronously without managing database connections.

Answer: D

Question 4

A Cloud Data Engineer is tasked with setting up a secure and reliable connection from an AWS-based analytics service to an on-premises Microsoft SQL Server database. The goal is to perform regular data import operations.

Which approach would be most effective and secure for accomplishing this task?

Options :

A : Configure an AWS Lambda function within the VPC to use a JDBC connection with the database credentials hard-coded for simplicity and speed.

B : Implement an AWS Direct Connect link between the on-premises network and AWS, and use a JDBC connection with database credentials stored in Amazon RDS.

C : Use AWS DataSync with an ODBC connection to the SQL Server, ensuring the connection over the internet is encrypted with TLS.

D : Establish a VPN connection between the on-premises network and AWS VPC, and then use JDBC with SSL encryption to connect to the SQL Server.

Answer: D

Question 5

A data engineer has a one-time task to read data from objects that are in Apache Parquet format in an Amazon S3 bucket. The data engineer needs to query only one column of the data. Which solution will meet these requirements with the LEAST operational overhead?

Options :

A :

Confiqure an AWS Lambda function to load data from the S3 bucket into a pandas dataframe- Write a SQL SELECT statement on the dataframe to query the required column.

B : Use S3 Select to write a SQL SELECT statement to retrieve the required column from the S3 objects.

C : Prepare an AWS Glue DataBrew project to consume the S3 objects and to query the required column.

D :

Run an AWS Glue crawler on the S3 objects. Use a SQL SELECT statement in Amazon Athena to query the required column.

Answer: B,D

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