Question 1

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 2

An analytics team at a retail corporation is looking to process their vast amounts of transactional data stored in Amazon S3 using complex analytical queries. The team plans to use Amazon EMR with Apache Hive for their batch processing needs. They require a metadata management system that will allow them to define, organize, and query their data structure.

What component should the team implement within their Amazon EMR environment to manage their metadata effectively and ensure compatibility with Hive?

Options :

A : Use the EMR File System (EMRFS) to handle Hive metadata alongside the transactional data in S3.

B : Deploy an Amazon RDS instance configured as an external Hive Metastore for the EMR cluster.

C : Configure the EMR cluster to use the Glue Data Catalog as a drop-in replacement for the Hive Metastore.

D : Implement Amazon DynamoDB to store and manage Hive metadata for EMR processing tasks.

Answer: C

Question 3

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 4

A company plans to migrate its on-premises Oracle database to Amazon Aurora PostgreSQL. They need to ensure a seamless migration of both the database schema and the data with minimal downtime. The company also requires a tool to convert the source database schema to be compatible with Amazon Aurora PostgreSQL.

Which combination of AWS services should the company use to migrate the database schema and data?

Options :

A : Utilize Amazon RDS for data migration and AWS Lambda for schema conversion.

B : Use AWS Database Migration Service (DMS) for data migration and AWS Schema Conversion Tool (SCT) for schema conversion.

C : Implement AWS Glue for both schema conversion and data migration.

D : Apply Amazon S3 transfer acceleration for data migration and Amazon Athena for schema conversion.

Answer: B

Question 5

A retail company receives a daily .xls file with customer data, which is uploaded to Amazon S3. The file size is about 2 GB. A data engineer is tasked with combining the customer first name and last name fields and then identifying the total number of unique customer entries in the file.

Which AWS service or feature should the data engineer use to determine the count of distinct customers with minimal operational effort?

Options :

A : Employ Amazon S3 Select to perform the concatenation and distinct count directly on the S3 file.

B : Utilize AWS Lambda with Amazon S3 trigger to process the file and calculate the number of distinct customer names.

C : Use Amazon Athena to run a SQL query that concatenates the names and counts distinct entries after converting the file to CSV format.

D : Set up an AWS Glue DataBrew job to concatenate the fields and use the COUNT_DISTINCT function to find the number of unique customers.

Answer: D

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