Question 1

A Cloud Data Engineering team is managing a data lake built on Amazon S3. The data lake is continuously updated with new data while also being accessed for various analytics and reporting purposes. To safeguard against accidental deletion or overwriting of data and to ensure the ability to restore previous versions of data files, the team needs to implement an appropriate data protection strategy on S3.

What should they prioritize to meet these requirements effectively?

Options :

A : Use S3 Lifecycle policies to transition older, unused data to S3 Glacier, and enable MFA Delete on the bucket to prevent accidental data deletions.

B : Enable versioning on the S3 buckets and periodically create and store snapshots of the entire data lake to a separate S3 bucket for backup purposes.

C : Enable S3 versioning across all buckets in the data lake, allowing each object version to be preserved, retrieved, and restored if needed.

D : Implement Amazon S3 Intelligent-Tiering on all objects in the data lake, ensuring that older versions of files are automatically moved to cost-effective storage tiers.

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 Data Engineering Consultant has been approached by a fintech company, AlphaFinance, which offers investment portfolio tracking for their customers. They want to create a daily report that shows the average value of all portfolios for the last 30 days, grouped by investment type (e.g., stocks, bonds, real estate). The data for each customer's portfolio is stored in an Amazon Redshift cluster, updated daily.

Which approach should the consultant recommend to efficiently derive this report?

Options :

A : Use Redshift-s window function, specifically AVG() with ROWS BETWEEN, to calculate the rolling average of the portfolio values over the last 30 days, and then group the results by investment type.

B : Create a batch process that runs every day, iterating over each day in the past 30 days, fetching portfolio values, and then manually averaging them out for each investment type.

C : Extract the data from Amazon Redshift into Amazon S3, use AWS Glue to pivot the data on investment types, and then calculate the 30-day average for each type.

D : Utilize Amazon QuickSight-s pivot table features to directly visualize and group the data by investment type, and then compute the 30-day rolling average.

Answer: A

Question 4

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

Question 5

A data analytics team is working with time-sensitive financial data. Their primary dataset resides in an Amazon Redshift cluster, but crucial, frequently updated transactional data is stored in a PostgreSQL database on Amazon RDS. They need a solution to analyze combined data sets in real-time, ensuring that the most current transactional information is included in their analysis without the overhead of continually transferring data between the two systems.

What approach should they consider to efficiently perform these real-time, integrated queries?

Options :

A : Utilize Athena Federated Queries to perform real-time querying across both the Redshift data and the RDS PostgreSQL database.

B : Implement Redshift Spectrum for querying external data, creating external tables that reflect the RDS PostgreSQL data.

C : Regularly export transactional data from RDS PostgreSQL to S3 and use Redshift Spectrum for querying alongside Redshift data.

D : Use Redshift Federated Query to directly integrate and query data from the Redshift cluster and the RDS PostgreSQL database in real-time.

Answer: D