Question 1

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 2

A Data Engineering Team at a financial services company is developing a data API to serve real-time, user-specific transactional data from an Amazon RDS for PostgreSQL database to their mobile banking application.

The data is highly dynamic, with frequent reads and writes. The API must offer low latency and high availability, and be capable of scaling automatically to handle peak loads during business hours.

Given these requirements, which architecture should the team implement?

Options :

A : Set up an Amazon ECS cluster with Fargate to host a custom-built REST API, connected to the RDS instance. Implement an in-memory caching layer within the API application to cache common queries and reduce direct calls to the database.

B : Use Amazon API Gateway integrated with AWS AppSync, which directly connects to the RDS for PostgreSQL database. Leverage the GraphQL capabilities of AppSync for efficient, tailored queries.

C : Employ Amazon API Gateway with an AWS Step Functions state machine to orchestrate Lambda functions for different query types, reducing the load on RDS by distributing the processing. Use Amazon DynamoDB to cache frequently accessed data.

D : Configure Amazon API Gateway with a serverless AWS Lambda function. The Lambda function should use efficient connection management strategies when accessing the RDS instance. Utilize Amazon ElastiCache to cache frequent queries and reduce database load.

Answer: D

Question 3

A Cloud Data Engineering Consultant is tasked with setting up a secure data lake for a financial institution using AWS Lake Formation. The institution requires strict management of permissions to ensure that analysts have access only to the specific data necessary for their analysis tasks and are restricted from accessing sensitive customer financial records. The consultant must configure Lake Formation in such a way that permissions are granularly managed while facilitating the ease of access for authorized users.

Which of the following actions should the consultant take to correctly manage permissions through Lake Formation for the given requirements? (Select TWO)

Options :

A : Grant column-level permissions to the analysts' IAM roles for non-sensitive data tables within Lake Formation, ensuring precise access to data necessary for their tasks.

B : Apply tag-based access control in Lake Formation, tagging sensitive data and assigning corresponding permission tags to analysts' IAM roles for fine-grained access.

C : Set up database-level permissions for all analysts, granting SELECT privileges to ensure that analysts have broad access for exploratory analysis within the Lake Formation.

D : Implement row-level security by creating Lake Formation access control lists that map to analysts' roles, permitting them to query only the rows pertinent to their analysis.

E : Configure Lake Formation to use named resource links for sensitive tables, providing analysts with indirect access to data through a resource link that obscures the underlying data source.

Answer: A,B

Question 4

In an effort to optimize their online gaming platform's performance, a Cloud Data Engineer decides to analyze the application's debug logs. These logs are generated by various game servers and automatically streamed to CloudWatch Logs. The engineer needs a quick and efficient way to perform exploratory analysis on these logs to identify any recurrent patterns or anomalies indicative of performance issues.

Which AWS service or feature should the engineer leverage for this task?

Options :

A : Employ Amazon OpenSearch Service for real-time analysis of the logs directly from CloudWatch.

B : Utilize CloudWatch Logs Insights for executing queries against the log data and gaining immediate insights.

C : Use AWS Lambda to process and transfer the logs from CloudWatch to Amazon S3, and then analyze them using Amazon Athena.

D : Set up an Amazon EMR cluster with Hadoop to import logs from CloudWatch and conduct a detailed analysis.

Answer: B

Question 5

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

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