Question 1

A mobile gaming company experiences predictable spikes in server load every Friday evening when users are most active. Their player data is stored in an Amazon DynamoDB table set to provisioned capacity mode. During the rest of the week, the server traffic is much lower. The company needs to manage the DynamoDB capacity in a way that keeps costs low while ensuring the game servers remain responsive during busy periods.

Which strategy should the company adopt to optimize DynamoDB performance for the expected traffic pattern while maintaining cost efficiency?

Options :

A : Implement DynamoDB auto-scaling to adjust the provisioned capacity based on the usage patterns automatically.

B : Manually adjust the provisioned capacity each Friday to meet the expected peak and lower it after the busy period ends.

C : Switch to DynamoDB on-demand capacity mode, allowing the table to automatically scale to accommodate the varying loads.

D : Split the player data across multiple DynamoDB tables to distribute the load, adjusting provisioned capacity for each table accordingly.

Answer: A

Question 2

A company receives a data file from a partner each day in an Amazon S3 bucket. The company uses a daily

AW5 Glue extract, transform, and load (ETL) pipeline to clean and transform each data file. The output of the

ETL pipeline is written to a CSV file named Dairy.csv in a second 53 bucket.

Occasionally, the daily data file is empty or is missing values for required fields. When the file is missing

data, the company can use the previous day's CSV file.

A data engineer needs to ensure that the previous day's data file is overwritten only if the new daily file is

complete and valid.

Which solution will meet these requirements with the LEAST effort?

Options :

A :

Invoke an AWS Lambda function to check the file for missing data and to fill in missing values in required fields.

B :

Configure the AWS Glue ETL pipeline to use AWS Glue Data Quality rules. Develop rules in Data Quality Definition Language (DQDL) to check for missing values in required files and empty files.

C :

Use AWS Glue Studio to change the code in the ETL pipeline to fill in any missing values in the required fields with the most common values for each field.

D :

Run a SQL query in Amazon Athena to read the CSV file and drop missing rows. Copy the corrected CSV file to the second S3 bucket.

Answer: B

Question 3

A data engineer is using Amazon Athena to analyze sales data that is in Amazon S3. The data engineer writes

a query to retrieve sales amounts for 2023 for several products from a table named sales_data. However, the

query does not return results for all of the products that are in the sales_data table. The data engineer needs to

troubleshoot the query to resolve the issue.

The data engineer's original query is as follows:

SELECT product_name, sum(sales_amount)

FROM sales_data

WHERE year = 2023

GROUP BY product_name

How should the data engineer modify the Athena query to meet these requirements?

Options :

A : Replace sum(sales amount) with count(*J for the aggregation.

B : Change WHERE year = 2023 to WHERE extractlyear FROM sales data) = 2023.

C : Add HAVING sumfsales amount) > 0 after the GROUP BY clause.

D : Remove the GROUP BY clause

Answer: B

Question 4

As a Cloud Data Engineer, you are tasked with troubleshooting a recurring issue in an AWS Glue job that is supposed to transform a large dataset. The job fails intermittently, with logs indicating memory errors. The dataset being processed is not unusually large, and similar jobs have run successfully in the past.

Which of the following steps should you take first to resolve this issue?

Options :

A : Check the script for inefficient transformations, such as unnecessary shuffles or large joins.

B : Switch the ETL job to use G.2X instances instead of the standard instances.

C : Increase the number of Data Processing Units (DPUs) allocated to the job.

D : Configure the job to write intermediate results to Amazon S3 to free up memory.

Answer: A

Question 5

A company uses an Amazon S3 bucket to store data in both JSON and .csv formats. They also operate databases on Amazon RDS for Microsoft SQL Server, have Amazon DynamoDB tables in provisioned capacity mode, and run queries on an Amazon Redshift cluster. They need a straightforward solution that allows their data scientists to perform SQL-like queries across all these data sources.

What is the simplest solution for enabling these queries with the least operational overhead?

Options :

A : Enable AWS Glue to catalog the data sources and use Amazon Athena with standard SQL for structured data and PartiQL for the JSON data.

B : Use AWS Glue to catalog and transform JSON data into .csv format using Glue jobs, then query all data using Amazon Athena.

C : Catalog the data with AWS Glue, and query using Redshift Spectrum, applying standard SQL to structured data and PartiQL to JSON.

D : Set up a data lake with AWS Lake Formation, convert all data to Apache Parquet using Lake Formation jobs, and query the data using Amazon Athena or Redshift Spectrum.

Answer: A

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