A company needs to partition the Amazon S3 storage that the company uses for a data lake. The partitioning will use a path of the S3 object keys in the following format: s3://bucket/prefix/year=2023/month=01/day=01.
A data engineer must ensure that the AWS Glue Data Catalog synchronizes with the S3 storage when the company adds new partitions to the bucket.
Which solution will meet these requirements with the LEAST latency?

A. Schedule an AWS Glue crawler to run every morning.

B. Manually run the AWS Glue CreatePartition API twice each day.

C. Use code that writes data to Amazon S3 to invoke the Boto3 AWS Glue create_partition API call.

D. Run the MSCK REPAIR TABLE command from the AWS Glue console.

A company stores details about transactions in an Amazon S3 bucket. The company wants to log all writes to the S3 bucket into another S3 bucket that is in the same AWS Region.
Which solution will meet this requirement with the LEAST operational effort?

A. Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket to invoke an AWS Lambda function. Program the Lambda function to write the event to Amazon Kinesis Data Firehose. Configure Kinesis Data Firehose to write the event to the logs S3 bucket.

B. Create a trail of management events in AWS CloudTraiL. Configure the trail to receive data from the transactions S3 bucket. Specify an empty prefix and write-only events. Specify the logs S3 bucket as the destination bucket.

C. Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket to invoke an AWS Lambda function. Program the Lambda function to write the events to the logs S3 bucket.

D. Create a trail of data events in AWS CloudTraiL. Configure the trail to receive data from the transactions S3 bucket. Specify an empty prefix and write-only events. Specify the logs S3 bucket as the destination bucket.

A company stores petabytes of data in thousands of Amazon S3 buckets in the S3 Standard storage class. The data supports analytics workloads that have unpredictable and variable data access patterns.
The company does not access some data for months. However, the company must be able to retrieve all data within milliseconds. The company needs to optimize S3 storage costs.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use S3 Storage Lens standard metrics to determine when to move objects to more cost-optimized storage classes. Create S3 Lifecycle policies for the S3 buckets to move objects to cost-optimized storage classes. Continue to refine the S3 Lifecycle policies in the future to optimize storage costs.

B. Use S3 Storage Lens activity metrics to identify S3 buckets that the company accesses infrequently. Configure S3 Lifecycle rules to move objects from S3 Standard to the S3 Standard-Infrequent Access (S3 Standard-IA) and S3 Glacier storage classes based on the age of the data.

C. Use S3 Intelligent-Tiering. Activate the Deep Archive Access tier.

D. Use S3 Intelligent-Tiering. Use the default access tier.

A company currently stores all of its data in Amazon S3 by using the S3 Standard storage class.
A data engineer examined data access patterns to identify trends. During the first 6 months, most data files are accessed several times each day. Between 6 months and 2 years, most data files are accessed once or twice each month. After 2 years, data files are accessed only once or twice each year.
The data engineer needs to use an S3 Lifecycle policy to develop new data storage rules. The new storage solution must continue to provide high availability.
Which solution will meet these requirements in the MOST cost-effective way?

A. Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. Transfer objects to S3 Glacier Flexible Retrieval after 2 years.

B. Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. Transfer objects to S3 Glacier Flexible Retrieval after 2 years.

C. Transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. Transfer objects to S3 Glacier Deep Archive after 2 years.

D. Transition objects to S3 One Zone-Infrequent Access (S3 One Zone-IA) after 6 months. Transfer objects to S3 Glacier Deep Archive after 2 years.

A company stores 10 to 15 TB of uncompressed .csv files in Amazon S3. The company is evaluating Amazon Athena as a one-time query engine.
The company wants to transform the data to optimize query runtime and storage costs.
Which file format and compression solution will meet these requirements for Athena queries?

A. .csv format compressed with zip

B. JSON format compressed with bzip2

C. Apache Parquet format compressed with Snappy

D. Apache Avro format compressed with LZO

A data engineer needs to debug an AWS Glue job that reads from Amazon S3 and writes to Amazon Redshift. The data engineer enabled the bookmark feature for the AWS Glue job.
The data engineer has set the maximum concurrency for the AWS Glue job to 1.
The AWS Glue job is successfully writing the output to Amazon Redshift. However, the Amazon S3 files that were loaded during previous runs of the AWS Glue job are being reprocessed by subsequent runs.
What is the likely reason the AWS Glue job is reprocessing the files?

A. The AWS Glue job does not have the s3:GetObjectAcl permission that is required for bookmarks to work correctly.

B. The maximum concurrency for the AWS Glue job is set to 1.

C. The data engineer incorrectly specified an older version of AWS Glue for the Glue job.

D. The AWS Glue job does not have a required commit statement.

A company is planning to migrate on-premises Apache Hadoop clusters to Amazon EMR. The company also needs to migrate a data catalog into a persistent storage solution.
The company currently stores the data catalog in an on-premises Apache Hive metastore on the Hadoop clusters. The company requires a serverless solution to migrate the data catalog.
Which solution will meet these requirements MOST cost-effectively?

A. Use AWS Database Migration Service (AWS DMS) to migrate the Hive metastore into Amazon S3. Configure AWS Glue Data Catalog to scan Amazon S3 to produce the data catalog.

B. Configure a Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use AWS Glue Data Catalog to store the company’s data catalog as an external data catalog.

C. Configure an external Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use Amazon Aurora MySQL to store the company’s data catalog.

D. Configure a new Hive metastore in Amazon EMR. Migrate the existing on-premises Hive metastore into Amazon EMR. Use the new metastore as the company’s data catalog.

A data engineer needs to build an extract, transform, and load (ETL) job. The ETL job will process daily incoming .csv files that users upload to an Amazon S3 bucket. The size of each S3 object is less than 100 MB.
Which solution will meet these requirements MOST cost-effectively?

A. Write a custom Python application. Host the application on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster.

B. Write a PySpark ETL script. Host the script on an Amazon EMR cluster.

C. Write an AWS Glue PySpark job. Use Apache Spark to transform the data.

D. Write an AWS Glue Python shell job. Use pandas to transform the data.

A manufacturing company wants to collect data from sensors. A data engineer needs to implement a solution that ingests sensor data in near real time.
The solution must store the data to a persistent data store. The solution must store the data in nested JSON format. The company must have the ability to query from the data store with a latency of less than 10 milliseconds.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use a self-hosted Apache Kafka cluster to capture the sensor data. Store the data in Amazon S3 for querying.

B. Use AWS Lambda to process the sensor data. Store the data in Amazon S3 for querying.

C. Use Amazon Kinesis Data Streams to capture the sensor data. Store the data in Amazon DynamoDB for querying.

D. Use Amazon Simple Queue Service (Amazon SQS) to buffer incoming sensor data. Use AWS Glue to store the data in Amazon RDS for querying.

A company has developed several AWS Glue extract, transform, and load (ETL) jobs to validate and transform data from Amazon S3. The ETL jobs load the data into Amazon RDS for MySQL in batches once every day. The ETL jobs use a DynamicFrame to read the S3 data.
The ETL jobs currently process all the data that is in the S3 bucket. However, the company wants the jobs to process only the daily incremental data.
Which solution will meet this requirement with the LEAST coding effort?

A. Create an ETL job that reads the S3 file status and logs the status in Amazon DynamoDB.

B. Enable job bookmarks for the ETL jobs to update the state after a run to keep track of previously processed data.

C. Enable job metrics for the ETL jobs to help keep track of processed objects in Amazon CloudWatch.

D. Configure the ETL jobs to delete processed objects from Amazon S3 after each run.

A company stores datasets in JSON format and .csv format in an Amazon S3 bucket. The company has Amazon RDS for Microsoft SQL Server databases, Amazon DynamoDB tables that are in provisioned capacity mode, and an Amazon Redshift cluster. A data engineering team must develop a solution that will give data scientists the ability to query all data sources by using syntax similar to SQL.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use Amazon Athena to query the data. Use SQL for structured data sources. Use PartiQL for data that is stored in JSON format.

B. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use Redshift Spectrum to query the data. Use SQL for structured data sources. Use PartiQL for data that is stored in JSON format.

C. Use AWS Glue to crawl the data sources. Store metadata in the AWS Glue Data Catalog. Use AWS Glue jobs to transform data that is in JSON format to Apache Parquet or .csv format. Store the transformed data in an S3 bucket. Use Amazon Athena to query the original and transformed data from the S3 bucket.

D. Use AWS Lake Formation to create a data lake. Use Lake Formation jobs to transform the data from all data sources to Apache Parquet format. Store the transformed data in an S3 bucket. Use Amazon Athena or Redshift Spectrum to query the data.

A retail company uses Amazon Aurora PostgreSQL to process and store live transactional data. The company uses an Amazon Redshift cluster for a data warehouse.
An extract, transform, and load (ETL) job runs every morning to update the Redshift cluster with new data from the PostgreSQL database. The company has grown rapidly and needs to cost optimize the Redshift cluster.
A data engineer needs to create a solution to archive historical data. The data engineer must be able to run analytics queries that effectively combine data from live transactional data in PostgreSQL, current data in Redshift, and archived historical data. The solution must keep only the most recent 15 months of data in Amazon Redshift to reduce costs.
Which combination of steps will meet these requirements? (Choose two.)

A. Configure the Amazon Redshift Federated Query feature to query live transactional data that is in the PostgreSQL database.

B. Configure Amazon Redshift Spectrum to query live transactional data that is in the PostgreSQL database.

C. Schedule a monthly job to copy data that is older than 15 months to Amazon S3 by using the UNLOAD command. Delete the old data from the Redshift cluster. Configure Amazon Redshift Spectrum to access historical data in Amazon S3.

D. Schedule a monthly job to copy data that is older than 15 months to Amazon S3 Glacier Flexible Retrieval by using the UNLOAD command. Delete the old data from the Redshift cluster. Configure Redshift Spectrum to access historical data from S3 Glacier Flexible Retrieval.

E. Create a materialized view in Amazon Redshift that combines live, current, and historical data from different sources.

A data engineer needs to use AWS Step Functions to design an orchestration workflow. The workflow must parallel process a large collection of data files and apply a specific transformation to each file.
Which Step Functions state should the data engineer use to meet these requirements?

A. Parallel state

B. Choice state

C. Map state

D. Wait state

A company needs to set up a data catalog and metadata management for data sources that run in the AWS Cloud. The company will use the data catalog to maintain the metadata of all the objects that are in a set of data stores. The data stores include structured sources such as Amazon RDS and Amazon Redshift. The data stores also include semistructured sources such as JSON files and .xml files that are stored in Amazon S3.
The company needs a solution that will update the data catalog on a regular basis. The solution also must detect changes to the source metadata.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon Aurora as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the Aurora data catalog. Schedule the Lambda functions to run periodically.

B. Use the AWS Glue Data Catalog as the central metadata repository. Use AWS Glue crawlers to connect to multiple data stores and to update the Data Catalog with metadata changes. Schedule the crawlers to run periodically to update the metadata catalog.

C. Use Amazon DynamoDB as the data catalog. Create AWS Lambda functions that will connect to the data catalog. Configure the Lambda functions to gather the metadata information from multiple sources and to update the DynamoDB data catalog. Schedule the Lambda functions to run periodically.

D. Use the AWS Glue Data Catalog as the central metadata repository. Extract the schema for Amazon RDS and Amazon Redshift sources, and build the Data Catalog. Use AWS Glue crawlers for data that is in Amazon S3 to infer the schema and to automatically update the Data Catalog.

A retail company stores transactions, store locations, and customer information tables in four reserved ra3.4xlarge Amazon Redshift cluster nodes. All three tables use even table distribution.
The company updates the store location table only once or twice every few years.
A data engineer notices that Redshift queues are slowing down because the whole store location table is constantly being broadcast to all four compute nodes for most queries. The data engineer wants to speed up the query performance by minimizing the broadcasting of the store location table.
Which solution will meet these requirements in the MOST cost-effective way?

A. Change the distribution style of the store location table from EVEN distribution to ALL distribution.

B. Change the distribution style of the store location table to KEY distribution based on the column that has the highest dimension.

C. Add a join column named store_id into the sort key for all the tables.

D. Upgrade the Redshift reserved node to a larger instance size in the same instance family.

A manufacturing company collects sensor data from its factory floor to monitor and enhance operational efficiency. The company uses Amazon Kinesis Data Streams to publish the data that the sensors collect to a data stream. Then Amazon Kinesis Data Firehose writes the data to an Amazon S3 bucket.
The company needs to display a real-time view of operational efficiency on a large screen in the manufacturing facility.
Which solution will meet these requirements with the LOWEST latency?

A. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Use a connector for Apache Flink to write data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.

B. Configure the S3 bucket to send a notification to an AWS Lambda function when any new object is created. Use the Lambda function to publish the data to Amazon Aurora. Use Aurora as a source to create an Amazon QuickSight dashboard.

C. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to process the sensor data. Create a new Data Firehose delivery stream to publish data directly to an Amazon Timestream database. Use the Timestream database as a source to create an Amazon QuickSight dashboard.

D. Use AWS Glue bookmarks to read sensor data from the S3 bucket in real time. Publish the data to an Amazon Timestream database. Use the Timestream database as a source to create a Grafana dashboard.

A retail company uses AWS Glue for extract, transform, and load (ETL) operations on a dataset that contains information about customer orders. The company wants to implement specific validation rules to ensure data accuracy and consistency.
Which solution will meet these requirements?

A. Use AWS Glue job bookmarks to track the data for accuracy and consistency.

B. Create custom AWS Glue Data Quality rulesets to define specific data quality checks.

C. Use the built-in AWS Glue Data Quality transforms for standard data quality validations.

D. Use AWS Glue Data Catalog to maintain a centralized data schema and metadata repository.

A company maintains multiple extract, transform, and load (ETL) workflows that ingest data from the company's operational databases into an Amazon S3 based data lake. The ETL workflows use AWS Glue and Amazon EMR to process data.
The company wants to improve the existing architecture to provide automated orchestration and to require minimal manual effort.
Which solution will meet these requirements with the LEAST operational overhead?

A. AWS Glue workflows

B. AWS Step Functions tasks

C. AWS Lambda functions

D. Amazon Managed Workflows for Apache Airflow (Amazon MWAA) workflows

A data engineer needs Amazon Athena queries to finish faster. The data engineer notices that all the files the Athena queries use are currently stored in uncompressed .csv format. The data engineer also notices that users perform most queries by selecting a specific column.
Which solution will MOST speed up the Athena query performance?

A. Change the data format from .csv to JSON format. Apply Snappy compression.

B. Compress the .csv files by using Snappy compression.

C. Change the data format from .csv to Apache Parquet. Apply Snappy compression.

D. Compress the .csv files by using gzip compression.

A data engineer needs to maintain a central metadata repository that users access through Amazon EMR and Amazon Athena queries. The repository needs to provide the schema and properties of many tables. Some of the metadata is stored in Apache Hive. The data engineer needs to import the metadata from Hive into the central metadata repository.
Which solution will meet these requirements with the LEAST development effort?

A. Use Amazon EMR and Apache Ranger.

B. Use a Hive metastore on an EMR cluster.

C. Use the AWS Glue Data Catalog.

D. Use a metastore on an Amazon RDS for MySQL DB instance.

A company receives call logs as Amazon S3 objects that contain sensitive customer information. The company must protect the S3 objects by using encryption. The company must also use encryption keys that only specific employees can access.
Which solution will meet these requirements with the LEAST effort?

A. Use an AWS CloudHSM cluster to store the encryption keys. Configure the process that writes to Amazon S3 to make calls to CloudHSM to encrypt and decrypt the objects. Deploy an IAM policy that restricts access to the CloudHSM cluster.

B. Use server-side encryption with customer-provided keys (SSE-C) to encrypt the objects that contain customer information. Restrict access to the keys that encrypt the objects.

C. Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt the objects that contain customer information. Configure an IAM policy that restricts access to the KMS keys that encrypt the objects.

D. Use server-side encryption with Amazon S3 managed keys (SSE-S3) to encrypt the objects that contain customer information. Configure an IAM policy that restricts access to the Amazon S3 managed keys that encrypt the objects.

A company uses an AWS Lambda function to transfer files from a legacy SFTP environment to Amazon S3 buckets. The Lambda function is VPC enabled to ensure that all communications between the Lambda function and other AVS services that are in the same VPC environment will occur over a secure network.
The Lambda function is able to connect to the SFTP environment successfully. However, when the Lambda function attempts to upload files to the S3 buckets, the Lambda function returns timeout errors. A data engineer must resolve the timeout issues in a secure way.
Which solution will meet these requirements in the MOST cost-effective way?

A. Create a NAT gateway in the public subnet of the VPC. Route network traffic to the NAT gateway.

B. Create a VPC gateway endpoint for Amazon S3. Route network traffic to the VPC gateway endpoint.

C. Create a VPC interface endpoint for Amazon S3. Route network traffic to the VPC interface endpoint.

D. Use a VPC internet gateway to connect to the internet. Route network traffic to the VPC internet gateway.

A company has multiple applications that use datasets that are stored in an Amazon S3 bucket. The company has an ecommerce application that generates a dataset that contains personally identifiable information (PII). The company has an internal analytics application that does not require access to the PII.
To comply with regulations, the company must not share PII unnecessarily. A data engineer needs to implement a solution that with redact PII dynamically, based on the needs of each application that accesses the dataset.
Which solution will meet the requirements with the LEAST operational overhead?

A. Create an S3 bucket policy to limit the access each application has. Create multiple copies of the dataset. Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.

B. Create an S3 Object Lambda endpoint. Use the S3 Object Lambda endpoint to read data from the S3 bucket. Implement redaction logic within an S3 Object Lambda function to dynamically redact PII based on the needs of each application that accesses the data.

C. Use AWS Glue to transform the data for each application. Create multiple copies of the dataset. Give each dataset copy the appropriate level of redaction for the needs of the application that accesses the copy.

D. Create an API Gateway endpoint that has custom authorizers. Use the API Gateway endpoint to read data from the S3 bucket. Initiate a REST API call to dynamically redact PII based on the needs of each application that accesses the data.

A data engineer must orchestrate a data pipeline that consists of one AWS Lambda function and one AWS Glue job. The solution must integrate with AWS services.
Which solution will meet these requirements with the LEAST management overhead?

A. Use an AWS Step Functions workflow that includes a state machine. Configure the state machine to run the Lambda function and then the AWS Glue job.

B. Use an Apache Airflow workflow that is deployed on an Amazon EC2 instance. Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job.

C. Use an AWS Glue workflow to run the Lambda function and then the AWS Glue job.

D. Use an Apache Airflow workflow that is deployed on Amazon Elastic Kubernetes Service (Amazon EKS). Define a directed acyclic graph (DAG) in which the first task is to call the Lambda function and the second task is to call the AWS Glue job.

A company currently uses a provisioned Amazon EMR cluster that includes general purpose Amazon EC2 instances. The EMR cluster uses EMR managed scaling between one to five task nodes for the company’s long-running Apache Spark extract, transform, and load (ETL) job. The company runs the ETL job every day.
When the company runs the ETL job, the EMR cluster quickly scales up to five nodes. The EMR cluster often reaches maximum CPU usage, but the memory usage remains under 30%.
The company wants to modify the EMR cluster configuration to reduce the EMR costs to run the daily ETL job.
Which solution will meet these requirements MOST cost-effectively?

A. Increase the maximum number of task nodes for EMR managed scaling to 10.

B. Change the task node type from general purpose EC2 instances to memory optimized EC2 instances.

C. Switch the task node type from general purpose Re instances to compute optimized EC2 instances.

D. Reduce the scaling cooldown period for the provisioned EMR cluster.

A financial company wants to use Amazon Athena to run on-demand SQL queries on a petabyte-scale dataset to support a business intelligence (BI) application. An AWS Glue job that runs during non-business hours updates the dataset once every day. The BI application has a standard data refresh frequency of 1 hour to comply with company policies.
A data engineer wants to cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs.
Which solution will meet these requirements with the LEAST operational overhead?

A. Configure an Amazon S3 Lifecycle policy to move data to the S3 Glacier Deep Archive storage class after 1 day.

B. Use the query result reuse feature of Amazon Athena for the SQL queries.

C. Add an Amazon ElastiCache cluster between the BI application and Athena.

D. Change the format of the files that are in the dataset to Apache Parquet.

A data engineer set up an AWS Lambda function to read an object that is stored in an Amazon S3 bucket. The object is encrypted by an AWS KMS key.
The data engineer configured the Lambda function’s execution role to access the S3 bucket. However, the Lambda function encountered an error and failed to retrieve the content of the object.
What is the likely cause of the error?

A. The data engineer misconfigured the permissions of the S3 bucket. The Lambda function could not access the object.

B. The Lambda function is using an outdated SDK version, which caused the read failure.

C. The S3 bucket is located in a different AWS Region than the Region where the data engineer works. Latency issues caused the Lambda function to encounter an error.

D. The Lambda function’s execution role does not have the necessary permissions to access the KMS key that can decrypt the S3 object.

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?

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

B. Amazon AppFlow

C. AWS Glue Data Catalog

D. Amazon Kinesis

A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling.
Which solution will meet this requirement?

A. Turn on concurrency scaling in workload management (WLM) for Redshift Serverless workgroups.

B. Turn on concurrency scaling at the workload management (WLM) queue level in the Redshift cluster.

C. Turn on concurrency scaling in the settings during the creation of any new Redshift cluster.

D. Turn on concurrency scaling for the daily usage quota for the Redshift cluster.

A data engineer needs to use an Amazon QuickSight dashboard that is based on Amazon Athena queries on data that is stored in an Amazon S3 bucket. When the data engineer connects to the QuickSight dashboard, the data engineer receives an error message that indicates insufficient permissions.
Which factors could cause to the permissions-related errors? (Choose two.)

A. There is no connection between QuickSight and Athena.

B. The Athena tables are not cataloged.

C. QuickSight does not have access to the S3 bucket.

D. QuickSight does not have access to decrypt S3 data.

E. There is no IAM role assigned to QuickSight.

An airline company is collecting metrics about flight activities for analytics. The company is conducting a proof of concept (POC) test to show how analytics can provide insights that the company can use to increase on-time departures.
The POC test uses objects in Amazon S3 that contain the metrics in .csv format. The POC test uses Amazon Athena to query the data. The data is partitioned in the S3 bucket by date.
As the amount of data increases, the company wants to optimize the storage solution to improve query performance.
Which combination of solutions will meet these requirements? (Choose two.)

A. Add a randomized string to the beginning of the keys in Amazon S3 to get more throughput across partitions.

B. Use an S3 bucket that is in the same account that uses Athena to query the data.

C. Use an S3 bucket that is in the same AWS Region where the company runs Athena queries.

D. Preprocess the .csv data to JSON format by fetching only the document keys that the query requires.

E. Preprocess the .csv data to Apache Parquet format by fetching only the data blocks that are needed for predicates.

A company wants to implement real-time analytics capabilities. The company wants to use Amazon Kinesis Data Streams and Amazon Redshift to ingest and process streaming data at the rate of several gigabytes per second. The company wants to derive near real-time insights by using existing business intelligence (BI) and analytics tools.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Kinesis Data Streams to stage data in Amazon S3. Use the COPY command to load data from Amazon S3 directly into Amazon Redshift to make the data immediately available for real-time analysis.

B. Access the data from Kinesis Data Streams by using SQL queries. Create materialized views directly on top of the stream. Refresh the materialized views regularly to query the most recent stream data.

C. Create an external schema in Amazon Redshift to map the data from Kinesis Data Streams to an Amazon Redshift object. Create a materialized view to read data from the stream. Set the materialized view to auto refresh.

D. Connect Kinesis Data Streams to Amazon Kinesis Data Firehose. Use Kinesis Data Firehose to stage the data in Amazon S3. Use the COPY command to load the data from Amazon S3 to a table in Amazon Redshift.

A company needs to send customer call data from its on-premises PostgreSQL database to AWS to generate near real-time insights. The solution must capture and load updates from operational data stores that run in the PostgreSQL database. The data changes continuously.
A data engineer configures an AWS Database Migration Service (AWS DMS) ongoing replication task. The task reads changes in near real time from the PostgreSQL source database transaction logs for each table. The task then sends the data to an Amazon Redshift cluster for processing.
The data engineer discovers latency issues during the change data capture (CDC) of the task. The data engineer thinks that the PostgreSQL source database is causing the high latency.
Which solution will confirm that the PostgreSQL database is the source of the high latency?

A. Use Amazon CloudWatch to monitor the DMS task. Examine the CDCIncomingChanges metric to identify delays in the CDC from the source database.

B. Verify that logical replication of the source database is configured in the postgresql.conf configuration file.

C. Enable Amazon CloudWatch Logs for the DMS endpoint of the source database. Check for error messages.

D. Use Amazon CloudWatch to monitor the DMS task. Examine the CDCLatencySource metric to identify delays in the CDC from the source database.

A company stores employee data in Amazon Resdshift. A table names Employee uses columns named Region ID, Department ID, and Role ID as a compound sort key.
Which queries will MOST increase the speed of query by using a compound sort key of the table? (Choose two.)

A. Select *from Employee where Region ID=’North America’;

B. Select *from Employee where Region ID=’North America’ and Department ID=20;

C. Select *from Employee where Department ID=20 and Region ID=’North America’;

D. Select *from Employee where Role ID=50;

E. Select *from Employee where Region ID=’North America’ and Role ID=50;

A data engineer is configuring an AWS Glue job to read data from an Amazon S3 bucket. The data engineer has set up the necessary AWS Glue connection details and an associated IAM role. However, when the data engineer attempts to run the AWS Glue job, the data engineer receives an error message that indicates that there are problems with the Amazon S3 VPC gateway endpoint.
The data engineer must resolve the error and connect the AWS Glue job to the S3 bucket.
Which solution will meet this requirement?

A. Update the AWS Glue security group to allow inbound traffic from the Amazon S3 VPC gateway endpoint.

B. Configure an S3 bucket policy to explicitly grant the AWS Glue job permissions to access the S3 bucket.

C. Review the AWS Glue job code to ensure that the AWS Glue connection details include a fully qualified domain name.

D. Verify that the VPC’s route table includes inbound and outbound routes for the Amazon S3 VPC gateway endpoint.

A retail company has a customer data hub in an Amazon S3 bucket. Employees from many countries use the data hub to support company-wide analytics. A governance team must ensure that the company's data analysts can access data only for customers who are within the same country as the analysts.
Which solution will meet these requirements with the LEAST operational effort?

A. Create a separate table for each country’s customer data. Provide access to each analyst based on the country that the analyst serves.

B. Register the S3 bucket as a data lake location in AWS Lake Formation. Use the Lake Formation row-level security features to enforce the company’s access policies.

C. Move the data to AWS Regions that are close to the countries where the customers are. Provide access to each analyst based on the country that the analyst serves.

D. Load the data into Amazon Redshift. Create a view for each country. Create separate IAM roles for each country to provide access to data from each country. Assign the appropriate roles to the analysts.

A company is building an analytics solution. The solution uses Amazon S3 for data lake storage and Amazon Redshift for a data warehouse. The company wants to use Amazon Redshift Spectrum to query the data that is in Amazon S3.
Which actions will provide the FASTEST queries? (Choose two.)

A. Use gzip compression to compress individual files to sizes that are between 1 GB and 5 GB.

B. Use a columnar storage file format.

C. Partition the data based on the most common query predicates.

D. Split the data into files that are less than 10 KB.

E. Use file formats that are not splittable.

A data engineer must manage the ingestion of real-time streaming data into AWS. The data engineer wants to perform real-time analytics on the incoming streaming data by using time-based aggregations over a window of up to 30 minutes. The data engineer needs a solution that is highly fault tolerant.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use an AWS Lambda function that includes both the business and the analytics logic to perform time-based aggregations over a window of up to 30 minutes for the data in Amazon Kinesis Data Streams.

B. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data that might occasionally contain duplicates by using multiple types of aggregations.

C. Use an AWS Lambda function that includes both the business and the analytics logic to perform aggregations for a tumbling window of up to 30 minutes, based on the event timestamp.

D. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to analyze the data by using multiple types of aggregations to perform time-based analytics over a window of up to 30 minutes.

A company is using Amazon Redshift to build a data warehouse solution. The company is loading hundreds of files into a fact table that is in a Redshift cluster.
The company wants the data warehouse solution to achieve the greatest possible throughput. The solution must use cluster resources optimally when the company loads data into the fact table.
Which solution will meet these requirements?

A. Use multiple COPY commands to load the data into the Redshift cluster.

B. Use S3DistCp to load multiple files into Hadoop Distributed File System (HDFS). Use an HDFS connector to ingest the data into the Redshift cluster.

C. Use a number of INSERT statements equal to the number of Redshift cluster nodes. Load the data in parallel into each node.

D. Use a single COPY command to load the data into the Redshift cluster.

A data engineer must build an extract, transform, and load (ETL) pipeline to process and load data from 10 source systems into 10 tables that are in an Amazon Redshift database. All the source systems generate .csv, JSON, or Apache Parquet files every 15 minutes. The source systems all deliver files into one Amazon S3 bucket. The file sizes range from 10 MB to 20 GB. The ETL pipeline must function correctly despite changes to the data schema.
Which data pipeline solutions will meet these requirements? (Choose two.)

A. Use an Amazon EventBridge rule to run an AWS Glue job every 15 minutes. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.

B. Use an Amazon EventBridge rule to invoke an AWS Glue workflow job every 15 minutes. Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.

C. Configure an AWS Lambda function to invoke an AWS Glue crawler when a file is loaded into the S3 bucket. Configure an AWS Glue job to process and load the data into the Amazon Redshift tables. Create a second Lambda function to run the AWS Glue job. Create an Amazon EventBridge rule to invoke the second Lambda function when the AWS Glue crawler finishes running successfully.

D. Configure an AWS Lambda function to invoke an AWS Glue workflow when a file is loaded into the S3 bucket. Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.

E. Configure an AWS Lambda function to invoke an AWS Glue job when a file is loaded into the S3 bucket. Configure the AWS Glue job to read the files from the S3 bucket into an Apache Spark DataFrame. Configure the AWS Glue job to also put smaller partitions of the DataFrame into an Amazon Kinesis Data Firehose delivery stream. Configure the delivery stream to load data into the Amazon Redshift tables.

A company uses AWS Step Functions to orchestrate a data pipeline. The pipeline consists of Amazon EMR jobs that ingest data from data sources and store the data in an Amazon S3 bucket. The pipeline also includes EMR jobs that load the data to Amazon Redshift.
The company's cloud infrastructure team manually built a Step Functions state machine. The cloud infrastructure team launched an EMR cluster into a VPC to support the EMR jobs. However, the deployed Step Functions state machine is not able to run the EMR jobs.
Which combination of steps should the company take to identify the reason the Step Functions state machine is not able to run the EMR jobs? (Choose two.)

A. Use AWS CloudFormation to automate the Step Functions state machine deployment. Create a step to pause the state machine during the EMR jobs that fail. Configure the step to wait for a human user to send approval through an email message. Include details of the EMR task in the email message for further analysis.

B. Verify that the Step Functions state machine code has all IAM permissions that are necessary to create and run the EMR jobs. Verify that the Step Functions state machine code also includes IAM permissions to access the Amazon S3 buckets that the EMR jobs use. Use Access Analyzer for S3 to check the S3 access properties.

C. Check for entries in Amazon CloudWatch for the newly created EMR cluster. Change the AWS Step Functions state machine code to use Amazon EMR on EKS. Change the IAM access policies and the security group configuration for the Step Functions state machine code to reflect inclusion of Amazon Elastic Kubernetes Service (Amazon EKS).

D. Query the flow logs for the VPC. Determine whether the traffic that originates from the EMR cluster can successfully reach the data providers. Determine whether any security group that might be attached to the Amazon EMR cluster allows connections to the data source servers on the informed ports.

E. Check the retry scenarios that the company configured for the EMR jobs. Increase the number of seconds in the interval between each EMR task. Validate that each fallback state has the appropriate catch for each decision state. Configure an Amazon Simple Notification Service (Amazon SNS) topic to store the error messages.

A security company stores IoT data that is in JSON format in an Amazon S3 bucket. The data structure can change when the company upgrades the IoT devices. The company wants to create a data catalog that includes the IoT data. The company's analytics department will use the data catalog to index the data.
Which solution will meet these requirements MOST cost-effectively?

A. Create an AWS Glue Data Catalog. Configure an AWS Glue Schema Registry. Create a new AWS Glue workload to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless.

B. Create an Amazon Redshift provisioned cluster. Create an Amazon Redshift Spectrum database for the analytics department to explore the data that is in Amazon S3. Create Redshift stored procedures to load the data into Amazon Redshift.

C. Create an Amazon Athena workgroup. Explore the data that is in Amazon S3 by using Apache Spark through Athena. Provide the Athena workgroup schema and tables to the analytics department.

D. Create an AWS Glue Data Catalog. Configure an AWS Glue Schema Registry. Create AWS Lambda user defined functions (UDFs) by using the Amazon Redshift Data API. Create an AWS Step Functions job to orchestrate the ingestion of the data that the analytics department will use into Amazon Redshift Serverless.

A company wants to use machine learning (ML) to perform analytics on data that is in an Amazon S3 data lake. The company has two data transformation requirements that will give consumers within the company the ability to create reports.
The company must perform daily transformations on 300 GB of data that is in a variety format that must arrive in Amazon S3 at a scheduled time. The company must perform one-time transformations of terabytes of archived data that is in the S3 data lake. The company uses Amazon Managed Workflows for Apache Airflow (Amazon MWAA) Directed Acyclic Graphs (DAGs) to orchestrate processing.
Which combination of tasks should the company schedule in the Amazon MWAA DAGs to meet these requirements MOST cost-effectively? (Choose two.)

A. For daily incoming data, use AWS Glue crawlers to scan and identify the schema.

B. For daily incoming data, use Amazon Athena to scan and identify the schema.

C. For daily incoming data, use Amazon Redshift to perform transformations.

D. For daily and archived data, use Amazon EMR to perform data transformations.

E. For archived data, use Amazon SageMaker to perform data transformations.

A company maintains an Amazon Redshift provisioned cluster that the company uses for extract, transform, and load (ETL) operations to support critical analysis tasks. A sales team within the company maintains a Redshift cluster that the sales team uses for business intelligence (BI) tasks.
The sales team recently requested access to the data that is in the ETL Redshift cluster so the team can perform weekly summary analysis tasks. The sales team needs to join data from the ETL cluster with data that is in the sales team's BI cluster.
The company needs a solution that will share the ETL cluster data with the sales team without interrupting the critical analysis tasks. The solution must minimize usage of the computing resources of the ETL cluster.
Which solution will meet these requirements?

A. Set up the sales team BI cluster as a consumer of the ETL cluster by using Redshift data sharing.

B. Create materialized views based on the sales team’s requirements. Grant the sales team direct access to the ETL cluster.

C. Create database views based on the sales team’s requirements. Grant the sales team direct access to the ETL cluster.

D. Unload a copy of the data from the ETL cluster to an Amazon S3 bucket every week. Create an Amazon Redshift Spectrum table based on the content of the ETL cluster.

A company stores data in a data lake that is in Amazon S3. Some data that the company stores in the data lake contains personally identifiable information (PII). Multiple user groups need to access the raw data. The company must ensure that user groups can access only the PII that they require.
Which solution will meet these requirements with the LEAST effort?

A. Use Amazon Athena to query the data. Set up AWS Lake Formation and create data filters to establish levels of access for the company’s IAM roles. Assign each user to the IAM role that matches the user’s PII access requirements.

B. Use Amazon QuickSight to access the data. Use column-level security features in QuickSight to limit the PII that users can retrieve from Amazon S3 by using Amazon Athena. Define QuickSight access levels based on the PII access requirements of the users.

C. Build a custom query builder UI that will run Athena queries in the background to access the data. Create user groups in Amazon Cognito. Assign access levels to the user groups based on the PII access requirements of the users.

D. Create IAM roles that have different levels of granular access. Assign the IAM roles to IAM user groups. Use an identity-based policy to assign access levels to user groups at the column level.

An insurance company stores transaction data that the company compressed with gzip.
The company needs to query the transaction data for occasional audits.
Which solution will meet this requirement in the MOST cost-effective way?

A. Store the data in Amazon Glacier Flexible Retrieval. Use Amazon S3 Glacier Select to query the data.

B. Store the data in Amazon S3. Use Amazon S3 Select to query the data.

C. Store the data in Amazon S3. Use Amazon Athena to query the data.

D. Store the data in Amazon Glacier Instant Retrieval. Use Amazon Athena to query the data.

Two developers are working on separate application releases. The developers have created feature branches named Branch A and Branch B by using a GitHub repository’s master branch as the source.
The developer for Branch A deployed code to the production system. The code for Branch B will merge into a master branch in the following week’s scheduled application release.
Which command should the developer for Branch B run before the developer raises a pull request to the master branch?

A. git diff branchB mastergit commit -m

B. git pull master

C. git rebase master

D. git fetch -b master

A company has a data warehouse that contains a table that is named Sales. The company stores the table in Amazon Redshift. The table includes a column that is named city_name. The company wants to query the table to find all rows that have a city_name that starts with "San" or "El".
Which SQL query will meet this requirement?

A. Select * from Sales where city_name ~ ‘$(San|El)*’;

B. Select * from Sales where city_name ~ ‘^(San|El)*’;

C. Select * from Sales where city_name ~’$(San&El)*’;

D. Select * from Sales where city_name ~ ‘^(San&El)*’;

A data engineer is building a data orchestration workflow. The data engineer plans to use a hybrid model that includes some on-premises resources and some resources that are in the cloud. The data engineer wants to prioritize portability and open source resources.
Which service should the data engineer use in both the on-premises environment and the cloud-based environment?

A. AWS Data Exchange

B. Amazon Simple Workflow Service (Amazon SWF)

C. Amazon Managed Workflows for Apache Airflow (Amazon MWAA)

D. AWS Glue

A data engineer creates an AWS Glue Data Catalog table by using an AWS Glue crawler that is named Orders. The data engineer wants to add the following new partitions:
s3://transactions/orders/order_date=2023-01-01
s3://transactions/orders/order_date=2023-01-02
The data engineer must edit the metadata to include the new partitions in the table without scanning all the folders and files in the location of the table.
Which data definition language (DDL) statement should the data engineer use in Amazon Athena?

A. ALTER TABLE Orders ADD PARTITION(order_date=’2023-01-01’) LOCATION ‘s3://transactions/orders/order_date=2023-01-01’;ALTER TABLE Orders ADD PARTITION(order_date=’2023-01-02’) LOCATION ‘s3://transactions/orders/order_date=2023-01-02’;

B. MSCK REPAIR TABLE Orders;

C. REPAIR TABLE Orders;

D. ALTER TABLE Orders MODIFY PARTITION(order_date=’2023-01-01’) LOCATION ‘s3://transactions/orders/2023-01-01’;ALTER TABLE Orders MODIFY PARTITION(order_date=’2023-01-02’) LOCATION ‘s3://transactions/orders/2023-01-02’;