Question 1

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 2

A company is migrating a legacy application to an Amazon S3 based data lake. A data engineer reviewed data

that is associated with the legacy application. The data engineer found that the legacy data contained some

duplicate information.

The data engineer must identify and remove duplicate information from the legacy application data.

Which solution will meet these requirements with the LEAST operational overhead?

Options :

A :

Write a custom extract, transform, and load (ETL) job in Python. Use the DataFramedrop duplicatesf) function by importingthe Pandas library to perform data deduplication.

B :

Write an AWS Glue extract, transform, and load (ETL) job. Usethe FindMatches machine learning(ML) transform to transform the data to perform data deduplication.

C :

Write a custom extract, transform, and load (ETL) job in Python. Import the Python dedupe library. Use the dedupe library to perform data deduplication.

D :

Write an AWS Glue extract, transform, and load (ETL) job. Import the Python dedupe library. Use the dedupe library to perform data deduplication.

Answer: B

Question 3

A Cloud Data Engineer is troubleshooting a batch processing workflow where Amazon EC2 instances intermittently fail to process and load transformed data into an Amazon RDS instance. The EC2 instances transform data using a Python script and should handle varying loads efficiently. The Data Engineer observes that the EC2 instances are not scaling as expected, leading to timeouts and failed data loads during peak hours.

Which actions should the Data Engineer take to identify and rectify the scaling issues and prevent future timeouts and load failures? (Select TWO)

Options :

A : Transition to AWS Lambda for data transformation, using its automatic scaling to manage the varying load without manual intervention.

B : Increase the instance size of the EC2 instances to provide more consistent performance under peak loads.

C : Implement Amazon CloudWatch alarms to monitor EC2 CPU utilization and trigger scaling events more effectively.

D : Modify the EC2 Auto Scaling group policies to better match the workload patterns, ensuring that scaling occurs in anticipation of load increases.

E : Optimize the Python data transformation script to decrease the execution time and reduce the processing load on each EC2 instance.

Answer: C,D

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 business specializing in big data analytics is transitioning its data processing from an on-site data center to AWS to cut down on management complexity. They're interested in adopting a serverless architecture wherever possible.

Their current setup involves heavy use of Apache Pig, Apache Oozie, Apache Spark, Apache HBase, and Apache Flink, handling petabytes of data with rapid processing times. The business requires that their new cloud-based solution offer comparable, if not superior, processing performance.

Which AWS service should they choose to achieve serverless ETL at scale?

Options :

A : Use serverless Amazon Athena for interactive query services.

B : Use Amazon S3 with S3 Select for optimized data retrieval.

C : Use Amazon Kinesis Data Analytics for real-time analytics.

D : Use AWS Glue for serverless ETL operations.

Answer: D

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