Question 1

A Data Engineering Consultant is tasked with analyzing a large dataset of historical sales data stored in Amazon S3 using Athena. The data consists of multiple compressed CSV files partitioned by year and month. To gain insights from this data, the consultant plans to use Athena notebooks integrated with Apache Spark for more complex data transformations and analysis.

What is the most effective approach to achieve this task?

Options :

A : Utilize AWS Lake Formation to create a data lake, ensuring the data in S3 is registered and accessible via Athena, and then perform Spark transformations and analytics in the Athena notebook.

B : Launch an Amazon EMR cluster with Apache Spark, read the S3 data into Spark DataFrames, perform the necessary transformations, and then query the results using Athena from within the EMR notebook.

C : Set up an AWS Glue DataBrew job to preprocess the data, and then load the transformed data into Athena for querying and analysis with Spark in the Athena notebook.

D : Directly use Athena with the Athena notebook interface to query the S3 data and perform data transformations and analysis using Apache Spark, without needing to set up any additional services.

Answer: D

Question 2

A marketing firm analyzes social media engagement data which is collected daily and saved as .csv files in an Amazon S3 bucket. The firm's data engineer needs to ensure that the S3 data is cataloged daily for use with AWS analytics services.

What steps should the data engineer take to catalog the social media data files in the AWS Glue Data Catalog each day with the least manual effort?

Options :

A : Implement an Amazon EventBridge rule to trigger an AWS Step Functions workflow that runs the Glue crawler daily.

B : Assign an IAM role with the necessary Glue permissions to the AWS Glue crawler, point it to the S3 bucket-s social media data, and set a daily schedule for the crawler.

C : Configure an AWS Batch job to execute the AWS Glue crawler for the S3 data, with a scheduled CloudWatch Events rule to trigger the job each day.

D : Use AWS Lambda to invoke an AWS Glue crawler for the social media data in S3, running the function with a CloudWatch Events rule on a daily schedule.

Answer: B

Question 3

A data engineering team at an e-commerce company is optimizing their query performance on Amazon Redshift. They have numerous complex queries running against large joined datasets and have identified that some queries are not as efficient as they could be. The team has been advised to leverage Redshift’s query optimization features to improve performance. They are considering the use of materialized views to speed up these queries.

Which feature should the team implement to enhance their query performance in Redshift, especially for frequently executed queries that join multiple tables?

Options :

A : Activate Redshift’s automatic workload management (WLM) to dynamically manage memory and prioritize queries.

B : Configure Redshift Spectrum to offload the complex join queries and utilize external tables for better performance.

C : Use Redshift’s Query Optimizer with materialized views to precompute and store the results of complex joins.

D : Enable short query acceleration in Redshift to prioritize execution of the complex join queries.

Answer: C

Question 4

A financial services company is in need of an enhanced storage solution for their transactional relational database system. The solution must ensure high durability, strong consistency, and enhanced read performance for real-time analytics. Additionally, the company needs to maintain high availability and disaster recovery capabilities. Cost management is also a priority, but not at the expense of these performance characteristics. The company's current infrastructure does not require multiple geographical regions.

Which combination of AWS storage services and features should the consultant recommend to meet these requirements? (Select TWO)

Options :

A : Use Amazon DynamoDB with provisioned capacity to manage cost while offering built-in high availability and durability.

B : Implement Amazon EFS with lifecycle management policies to transition older transaction data to EFS Infrequent Access.

C : Utilize Amazon ElastiCache to improve read performance and reduce the load on the primary transaction database.

D : Deploy Amazon Aurora Global Database for high performance, availability, and cross-region disaster recovery.

E : Enable Amazon RDS Multi-AZ deployments for high availability and durability with synchronous data replication.

Answer: C,E

Question 5

A telecommunications company collects network usage data throughout each day at a rate of several thousand

data points each second. The company runs an application to process the usage data in real time. The

company aggregates and stores the data in an Amazon Aurora DB instance.

Sudden drops in network usage usually indicate a network outage. The company must be able to identify

sudden drops in network usage so the company can take immediate remedial actions.

Which solution will meet this requirement with the LEAST latency?

Options :

A :

Create an AWS Lambda function to query Aurora for drops in network usage. Use Amazon EventBridge to automatically invoke the Lambda function every minute.

B :

Modify the processing application to publish the data to an Amazon Kinesis data stream. Create an Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) application to detect drops in network usage.

C :

Replace the Aurora database with an Amazon DynamoDB table. Create an AWS Lambda function to query the DynamoDB table for drops in network usage every minute. Use DynamoDB Accelerator (DAX) between the processing application and DynamoDB table.

D :

Create an AWS Lambda function within the Database Activity Streams feature of Aurora to detect drops in network usage.

Answer: B

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