Question 1

A sales company uses AWS Glue ETL to collect, process, and ingest data into an Amazon S3 bucket. The AWS Glue pipeline creates a new file in the S3 bucket every hour. File sizes vary from 200 KB to 300 KB. The company wants to build a sales prediction model by using data from the previous 5 years. The historic data includes 44,000 files. The company builds a second AWS Glue ETL pipeline by using the smallest worker type. The second pipeline retrieves the historic files from the S3 bucket and processes the files for downstream analysis. The company notices significant performance issues with the second ETL pipeline. The company needs to improve the performance of the second pipeline. Which solution will meet this requirement MOST cost-effectively?

Options :

A : Use a larger worker type.

B : Increase the number of workers in the AWS Glue ETL jobs.

C : Use the AWS Glue DynamicFrame grouping option.

D : Enable AWS Glue auto scaling.

Answer: D

Question 2

As a data engineering consultant, you've been approached by a client who's experiencing difficulties with their Amazon DynamoDB setup. They've noticed that their query performance degrades as their dataset grows, despite having a primary key design that they believed would be optimal. They ask for your advice on how they can maintain consistent, fast query performance as their dataset scales.

Which of the following suggestions would be the most effective for maintaining query performance in DynamoDB at scale?

Options :

A : Suggest optimizing the table-s primary key by considering composite keys, which combine partition keys and sort keys to form a unique identifier for each item.

B : Advise on sharding the data by creating multiple tables with the same schema based on access patterns, thus distributing the load more evenly across the tables.

C : Recommend implementing DynamoDB Accelerator (DAX) to provide a fully managed, highly available, in-memory cache, particularly for read-intensive application workloads.

D : Propose the use of DynamoDB Streams to capture table activity and automate the redistribution of items to different partitions based on their access patterns using AWS Lambda functions.

Answer: C

Question 3

A media company uses software as a service (SaaS) applications to gather data by using third-party tools. The

company needs to store the data in an Amazon S3 bucket. The company will use Amazon Redshift to perform

analytics based on the data.

Which AWS service or feature will meet these requirements with the LEAST operational overhead?

Options :

A : Amazon Managed Streaming for Apache Kafka (Amazon MSK)

B : Amazon AppFlow

C : AWS Glue Data Catalog

D : Amazon Kinesis

Answer: B

Question 4

A data engineer needs to securely transfer 5 TB of data from an on-premises data center to an Amazon S3

bucket. Approximately 5% of the data changes every day. Updates to the data need to be regularlyproliferated

to the S3 bucket. The data includes files that are in multiple formats. The data engineer needs to automate the

transfer process and must schedule the process to run periodically.

Which AWS service should the data engineer use to transfer the data in the MOST operationally efficient

way?

Options :

A : AWS DataSync

B : AWS Glue

C : AWS Direct Connect

D : Amazon S3 Transfer Acceleration

Answer: A

Question 5

A growing fintech company is leveraging an Amazon Redshift cluster equipped with dense compute (DC2) nodes. To accommodate their expanding user base, the company needs to dynamically adjust both read and write capacities based on varying workloads. The data engineer has been tasked to configure the Redshift cluster to automatically add additional query processing power.

What action should the data engineer take to enable this functionality?

Options :

A : Enable elasticity at the Redshift cluster by configuring Elastic IP addresses for each node.

B : Configure the Redshift cluster to utilize Elastic Resize for adjusting node count based on demand.

C : Implement automatic WLM on the Redshift cluster to dynamically manage query queues.

D : Activate concurrency scaling in the Redshift cluster-s workload management (WLM) configuration.

Answer: D

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