Question 1

A data engineer needs to create an AWS Lambda function that converts the format of data from .csv to Apache Parquet. The Lambda function must run only if a user uploads a .csv file to an Amazon S3 bucket. Which solution will meet these requirements with the LEAST operational overhead?

Options :

A :

Create an S3 event notification that has an event type of s3:ObjectCreated:*. Use a filter rule to generate notifications only when the suffix includes .csv. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification.

B :

Create an S3 event notification that has an event type of s3:ObjectTagging:* for objects that have a tag set to .csv. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification.

C :

Create an S3 event notification that has an event type of s3:*. Use a filter rule to generate notifications only when the suffix includes .csv. Set the Amazon Resource Name (ARN) of the Lambda function as the destination for the event notification.

D :

Create an S3 event notification that has an event type of s3:ObjectCreated:*. Use a filter rule to generate notifications only when the suffix includes .csv. Set an Amazon Simple Notification Service (Amazon SNS) topic as the destination for the event notification. Subscribe the Lambda function to the SNS topic.

Answer: A

Question 2

A Data Engineering Consultant is reviewing the performance of an Amazon Redshift cluster used by a digital marketing agency. The cluster processes extensive user interaction and advertising campaign data. The consultant notes that, during specific aggregation queries involving a join between a user_activities table (distributed on user_id) and a campaigns table (distributed on campaign_id), certain nodes are running at high CPU utilization while others are barely used. These discrepancies in resource usage are leading to longer than expected query execution times.

What change can the consultant recommend to address this performance issue?

Options :

A : Convert the join operations in the aggregation queries to LEFT JOIN instead of INNER JOIN to ensure better CPU utilization.

B : Increase the Redshift cluster-s node count to provide additional resources and distribute the workload more evenly.

C : Switch the distribution style of the user_activities table to an even distribution to balance the load across all nodes.

D : Optimize the VACUUM operation to reorganize the data and improve query performance.

Answer: C

Question 3

A data engineer at a financial analysis firm is responsible for enriching their AWS-based data lake with external financial datasets. The firm requires regular updates of stock market data and economic indicators from various data providers, which should be integrated seamlessly into their Amazon S3-based data lake for further processing and analysis.

Which AWS service should the data engineer use to automate the ingestion of these external financial datasets into their data lake with minimal effort?

Options :

A : Implement an AWS Glue crawler to extract data from external data sources and load it into the data lake.

B : Configure Amazon AppFlow to create flows between the data providers and S3, syncing the financial datasets to the data lake.

C : Set up a recurring AWS Lambda function to retrieve and upload data from third-party APIs to their S3 data lake.

D : Use AWS Data Exchange to subscribe to relevant financial datasets and directly import them into their S3 data lake.

Answer: D

Question 4

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

Question 5

A software development team is deploying an application on Amazon Elastic Container Service (ECS) using Fargate. The application requires persistent storage to manage stateful data. The team needs to select an appropriate storage solution that can be attached to the ECS containers.

Which storage option should the team use for persistent storage with their ECS containers on Fargate?

Options :

A : Implement instance store volumes provided by the underlying EC2 instances of Fargate.

B : Use Amazon Elastic File System (EFS) volumes with the ECS containers.

C : Attach Amazon Elastic Block Store (EBS) volumes directly to the ECS containers.

D : Use Amazon S3 buckets by mounting them directly onto the ECS containers.

Answer: B

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