A company has multiple data workflows to ingest data from its operational databases into its data lake on Amazon S3. The workflows use AWS Glue and Amazon EMR for data processing and ETL. The company wants to enhance its architecture to provide automated orchestration and minimize manual intervention.
Which solution should the company use to manage the data workflows to meet these requirements?

A. AWS Glue workflows
B. AWS Step Functions
C. AWS Lambda
D. AWS Batch

A data analyst is using AWS Glue to organize, cleanse, validate, and format a 200 GB dataset. The data analyst triggered the job to run with the Standard worker type. After 3 hours, the AWS Glue job status is still RUNNING. Logs from the job run show no error codes. The data analyst wants to improve the job execution time without overprovisioning.
Which actions should the data analyst take?

A. Enable job bookmarks in AWS Glue to estimate the number of data processing units (DPUs). Based on the profiled metrics, increase the value of the executor-cores job parameter.
B. Enable job metrics in AWS Glue to estimate the number of data processing units (DPUs). Based on the profiled metrics, increase the value of the maximum capacity job parameter.
C. Enable job metrics in AWS Glue to estimate the number of data processing units (DPUs). Based on the profiled metrics, increase the value of the spark.yarn.executor.memoryOverhead job parameter.
D. Enable job bookmarks in AWS Glue to estimate the number of data processing units (DPUs). Based on the profiled metrics, increase the value of the num-executors job parameter.

A company is using an AWS Lambda function to run Amazon Athena queries against a cross-account AWS Glue Data Catalog. A query returns the following error:
HIVE_METASTORE_ERROR
The error message states that the response payload size exceeds the maximum allowed size. The queried table is already partitioned, and the data is stored in an Amazon S3 bucket in the Apache Hive partition format.
Which solution will resolve this error?

A. Modify the Lambda function to upload the query response payload as an object into the S3 bucket. Include an S3 object presigned URL as the payload in the Lambda function response.
B. Run the MSCK REPAIR TABLE command on the queried table.
C. Create a separate folder in the S3 bucket. Move the data files that need to be queried into that folder. Create an AWS Glue crawler that points to the folder instead of the S3 bucket.
D. Check the schema of the queried table for any characters that Athena does not support. Replace any unsupported characters with characters that Athena supports.

A company has a data warehouse in Amazon Redshift that is approximately 500 TB in size. New data is imported every few hours and read-only queries are run throughout the day and evening. There is a particularly heavy load with no writes for several hours each morning on business days. During those hours, some queries are queued and take a long time to run. The company needs to optimize query performance and avoid any downtime.
What is the MOST cost-effective solution?

A. Turn on concurrency scaling in the workload management (WLM) queue.
B. Add more nodes using the AWS Management Console during peak hours. Set the distribution style to ALL.
C. Use elastic resize to quickly add nodes during peak times. Remove the nodes when they are not needed.
D. Use a snapshot, restore, and resize operation. Switch to the new target cluster.

A company wants to use an automatic machine learning (ML) Random Cut Forest (RCF) algorithm to visualize complex real-world scenarios, such as detecting seasonality and trends, excluding outers, and imputing missing values.
The team working on this project is non-technical and is looking for an out-of-the-box solution that will require the LEAST amount of management overhead.
Which solution will meet these requirements?

A. Use an AWS Glue ML transform to create a forecast and then use Amazon QuickSight to visualize the data.
B. Use Amazon QuickSight to visualize the data and then use ML-powered forecasting to forecast the key business metrics.
C. Use a pre-build ML AMI from the AWS Marketplace to create forecasts and then use Amazon QuickSight to visualize the data.
D. Use calculated fields to create a new forecast and then use Amazon QuickSight to visualize the data.

An advertising company has a data lake that is built on Amazon S3. The company uses AWS Glue Data Catalog to maintain the metadata. The data lake is several years old and its overall size has increased exponentially as additional data sources and metadata are stored in the data lake. The data lake administrator wants to implement a mechanism to simplify permissions management between Amazon S3 and the Data Catalog to keep them in sync.
Which solution will simplify permissions management with minimal development effort?

A. Set AWS Identity and Access Management (IAM) permissions for AWS Glue
B. Use AWS Lake Formation permissions
C. Manage AWS Glue and S3 permissions by using bucket policies
D. Use Amazon Cognito user pools

A global pharmaceutical company receives test results for new drugs from various testing facilities worldwide.
The results are sent in millions of 1 KB-sized JSON objects to an Amazon S3 bucket owned by the company.
The data engineering team needs to process those files, convert them into Apache Parquet format, and load them into Amazon Redshift for data analysts to perform dashboard reporting. The engineering team uses AWS Glue to process the objects, AWS Step Functions for process orchestration, and Amazon CloudWatch for job scheduling.
More testing facilities were recently added, and the time to process files is increasing.
What will MOST efficiently decrease the data processing time?

A. Use AWS Lambda to group the small files into larger files. Write the files back to Amazon S3. Process the files using AWS Glue and load them into Amazon Redshift tables.
B. Use the AWS Glue dynamic frame file grouping option while ingesting the raw input files. Process the files and load them into Amazon Redshift tables.
C. Use the Amazon Redshift COPY command to move the files from Amazon S3 into Amazon Redshift tables directly. Process the files in Amazon Redshift.
D. Use Amazon EMR instead of AWS Glue to group the small input files. Process the files in Amazon EMR and load them into Amazon Redshift tables.

An operations team notices that a few AWS Glue jobs for a given ETL application are failing. The AWS Glue jobs read a large number of small JSON files from an Amazon S3 bucket and write the data to a different S3 bucket in Apache Parquet format with no major transformations. Upon initial investigation, a data engineer notices the following error message in the History tab on the AWS Glue console: "Command Failed with Exit Code 1."
Upon further investigation, the data engineer notices that the driver memory profile of the failed jobs crosses the safe threshold of 50% usage quickly and reaches 9095% soon after. The average memory usage across all executors continues to be less than 4%.
The data engineer also notices the following error while examining the related Amazon CloudWatch Logs.

What should the data engineer do to solve the failure in the MOST cost-effective way?

A. Change the worker type from Standard to G.2X.
B. Modify the AWS Glue ETL code to use the `groupFiles': `inPartition' feature.
C. Increase the fetch size setting by using AWS Glue dynamics frame.
D. Modify maximum capacity to increase the total maximum data processing units (DPUs) used.

An online retailer is rebuilding its inventory management system and inventory reordering system to automatically reorder products by using Amazon Kinesis Data Streams. The inventory management system uses the Kinesis Producer Library (KPL) to publish data to a stream. The inventory reordering system uses the Kinesis Client Library (KCL) to consume data from the stream. The stream has been configured to scale as needed. Just before production deployment, the retailer discovers that the inventory reordering system is receiving duplicated data.
Which factors could be causing the duplicated data? (Choose two.)

A. The producer has a network-related timeout.
B. The stream's value for the IteratorAgeMilliseconds metric is too high.
C. There was a change in the number of shards, record processors, or both.
D. The AggregationEnabled configuration property was set to true.
E. The max_records configuration property was set to a number that is too high.

A marketing company wants to improve its reporting and business intelligence capabilities. During the planning phase, the company interviewed the relevant stakeholders and discovered that:
The operations team reports are run hourly for the current month's data.
The sales team wants to use multiple Amazon QuickSight dashboards to show a rolling view of the last 30 days based on several categories. The sales team also wants to view the data as soon as it reaches the reporting backend.
The finance team's reports are run daily for last month's data and once a month for the last 24 months of data.
Currently, there is 400 TB of data in the system with an expected additional 100 TB added every month. The company is looking for a solution that is as cost-effective as possible.
Which solution meets the company's requirements?

A. Store the last 24 months of data in Amazon Redshift. Configure Amazon QuickSight with Amazon Redshift as the data source.
B. Store the last 2 months of data in Amazon Redshift and the rest of the months in Amazon S3. Set up an external schema and table for Amazon Redshift Spectrum. Configure Amazon QuickSight with Amazon Redshift as the data source.
C. Store the last 24 months of data in Amazon S3 and query it using Amazon Redshift Spectrum. Configure Amazon QuickSight with Amazon Redshift Spectrum as the data source.
D. Store the last 2 months of data in Amazon Redshift and the rest of the months in Amazon S3. Use a long- running Amazon EMR with Apache Spark cluster to query the data as needed. Configure Amazon QuickSight with Amazon EMR as the data source.