DEA-C01 — questions

A company uses AWS Glue Apache Spark jobs to handle extract, transform, and load (ETL) workloads. The company has enabled logging and monitoring for all AWS Glue jobs. One of the AWS Glue jobs begins to fail. A data engineer investigates the error and wants to examine metrics for all individual stages within the job. How can the data engineer access the stage metrics?

A data engineer notices slow query performance on a highly partitioned table that is in Amazon Athena. The table contains daily data for the previous 5 years, partitioned by date. The data engineer wants to improve query performance and to automate partition management. Which solution will meet these requirements?

A data engineer is using an Apache Iceberg framework to build a data lake that contains 100 ТВ of data. The data engineer wants to run AWS Glue Apache Spark jobs that use the Iceberg framework. What combination of steps will meet these requirements? (Choose two.)

A data engineer is configuring an AWS Glue Apache Spark extract, transform, and load (ETL) job. The job contains a sort-merge join of two large and equally sized DataFrames. The job is failing with the following error: No space left on device. Which solution will resolve the error?

A company needs to implement a workflow to process transactions. Each transaction goes through multiple levels of validation. Each validation level depends on the preceding validation level. The workflow must either process or reject each transaction within 24-hours. The workflow must run for less than 24 hours total. Which solution will meet these requirements with the LEAST operational cost?

A company runs an Apache Spark application every night in an Amazon EMR cluster. The company uses Amazon EC2 instances to supply compute capacity for the EMR cluster. The company deployed the Spark application in cluster mode. An error occurs in the Spark application. A log for the error is stored in the application's Spark driver standard error logs. A data engineer needs to investigate the error. Where can the data engineer find this error log?

A company processes 500 GB of audience and advertising data daily, storing CSV files in Amazon S3 with schemas registered in AWS Glue Data Catalog. They need to convert these files to Apache Parquet format and store them in an S3 bucket. The solution requires a long-running workflow with 15 GiB memory capacity to process the data concurrently, followed by a correlation process that begins only after the first two processes complete. Which solution will meet these requirements with the LEAST operational overhead?

A company uses an organization in AWS Organizations to manage multiple AWS accounts. The company uses an enhanced fanout data stream in Amazon Kinesis Data Streams to receive streaming data from multiple producers. The company runs the data stream in an account named Account A. The company wants to use an AWS Lambda function in an account named Account В to process the data from the data stream. The company creates a Lambda execution role in Account В that has permissions to access data from the data stream in Account A. What additional step must the company take to meet this requirement?

A company needs to use Amazon Athena to analyze data that is in an Amazon S3 bucket. A data engineer needs to configure AWS Glue table partitions for year, month, and day. The data engineer needs to create the partitions every day to adjust to schema changes in the data. Which solution will meet these requirements?

A data engineer is building a solution to detect sensitive information that is stored in a data lake across multiple Amazon S3 buckets. The solution must detect personally identifiable information (PII) that is in a proprietary data format. Which solution will meet these requirements with the LEAST operational overhead?

A ride-sharing company stores records for all rides in an Amazon DynamoDB table. The table includes the following columns and types of values: The table currently contains billions of items. The table is partitioned by RideID and uses TripStartTime as the sort key. The company wants to use the data to build a personal interface to give drivers the ability to view the rides that each driver has completed, based on RideStatus. The solution must access the necessary data without scanning the entire table. Which solution will meet these requirements?

A company stores information about its subscribers in an Amazon S3 bucket. The company runs an analysis every time a subscriber ends their subscription. The company uses AWS Lambda functions to respond to events from the S3 bucket by performing analyses. The Lambda functions clean data from the S3 bucket and initiate an AWS Glue workflow. The Lambda functions have 128 MB of memory and 512 MB of ephemeral storage. The Lambda functions have a timeout of 15 seconds. All three functions successfully finish running. However, CPU usage is often near 100%, which causes slow performance. The company wants to improve the performance of the functions and reduce the total runtime of the pipeline. Which solution will meet these requirements?

A company uses a data stream in Amazon Kinesis Data Streams to collect transactional data from multiple sources. The company uses an AWS Glue extract, transform, and load (ETL) pipeline to look for outliers in the data from the stream. When the workflow detects an outlier, it sends a notification to an Amazon Simple Notification Service (Amazon SNS) topic. The SNS topic initiates a second workflow to retrieve logs for the outliers and stores the logs in an Amazon S3 bucket. The company experiences delays in the notifications to the SNS topic during periods when the data stream is processing a high volume of data. When the company examines Amazon CloudWatch logs, the company notices a high value for the glue.driver.BlockManager.disk.diskSpaceUsed_MB metric when the traffic is high. The company must resolve this issue. Which solution will meet this requirement with the LEAST operational effort?

A company has a data processing pipeline that runs multiple SQL queries in sequence against an Amazon Redshift cluster. The company merges with a second company. The original company modifies a query that aggregates sales revenue data to join sales tables from both companies. The sales table for the first company is named Table S1. The sales table for the second company is named Table S2. Table S1 contains 10 billion records. Table S2 contains 900 million records. The query becomes slow after the modification. A data engineer must improve the query performance. Which solutions will meet these requirements? (Choose two.)

A gaming company uses AWS Glue to perform read and write operations on Apache Iceberg tables for real-time streaming data. The data in the Iceberg tables is in Apache Parquet format. The company is experiencing slow query performance. Which solutions will improve query performance? (Choose two.)

A data engineer at a company is optimizing extract, transform, and load (ETL) workflows. The current architecture uses Amazon EMR and Apache Spark for large-scale transformations and AWS Glue for other ETL tasks. The workflows load processed data into an Amazon S3 based data lake. The company wants to move to a fully managed serverless solution that can orchestrate multiple ETL jobs and automate execution. The new solution must continue to use Spark to process data. The company needs to orchestrate and automate the ETL workflows with minimal manual intervention. Which solution will meet these requirements?

A company needs to aggregate and filter a large amount of streaming data in real-time with low latency. The company needs to store the data in Amazon S3 for analysis. Which solution will meet these requirements in the MOST operationally efficient way?

A company is developing machine learning (ML) models. A data engineer needs to apply data quality rules to training data. The company stores the training data in an Amazon S3 bucket. Which solution will meet these requirements with the LEAST operational overhead?

A retail company stores point-of-sale transaction data in an Amazon RDS for MySQL database. The company maintains historical sales analytics in Amazon Redshift. The company needs to create daily reports that combine the current day's transactions with historical sales patterns for trend analysis. The company requires a solution that provides near real-time insights while minimizing data transfer costs and maintenance overhead. Which solution will meet these requirements?

A company needs to implement a data mesh architecture in which domains for trading, risk, and compliance teams each have own their data. The teams need to share specific views with one another. The teams have over 1,000 tables across 50 databases in AWS Glue Data Catalog. All three teams use Amazon Athena to perform on-demand analysis. The teams use Amazon Redshift to generate complex reports. The compliance team must audit all data access. Access to personally identifiable information (PII) data must be restricted. The company requires a scalable solution to meet the team requirements. The solution must provide the ability to perform analysis across team domains. Which solution will meet these requirements?