A company is building a service to monitor fleets of vehicles. The company collects IoT data from a device in each vehicle and loads the data into Amazon
Redshift in near-real time. Fleet owners upload .csv files containing vehicle reference data into Amazon S3 at different times throughout the day. A nightly process loads the vehicle reference data from Amazon S3 into Amazon Redshift. The company joins the IoT data from the device and the vehicle reference data to power reporting and dashboards. Fleet owners are frustrated by waiting a day for the dashboards to update.
Which solution would provide the SHORTEST delay between uploading reference data to Amazon S3 and the change showing up in the owners' dashboards?

A. Use S3 event notifications to trigger an AWS Lambda function to copy the vehicle reference data into Amazon Redshift immediately when the reference data is uploaded to Amazon S3.

B. Create and schedule an AWS Glue Spark job to run every 5 minutes. The job inserts reference data into Amazon Redshift.

C. Send reference data to Amazon Kinesis Data Streams. Configure the Kinesis data stream to directly load the reference data into Amazon Redshift in real time.

D. Send the reference data to an Amazon Kinesis Data Firehose delivery stream. Configure Kinesis with a buffer interval of 60 seconds and to directly load the data into Amazon Redshift.

A manufacturing company wants to create an operational analytics dashboard to visualize metrics from equipment in near-real time. The company uses Amazon
Kinesis Data Streams to stream the data to other applications. The dashboard must automatically refresh every 5 seconds. A data analytics specialist must design a solution that requires the least possible implementation effort.
Which solution meets these requirements?

A. Use Amazon Kinesis Data Firehose to store the data in Amazon S3. Use Amazon QuickSight to build the dashboard.

B. Use Apache Spark Streaming on Amazon EMR to read the data in near-real time. Develop a custom application for the dashboard by using D3.js.

C. Use Amazon Kinesis Data Firehose to push the data into an Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster. Visualize the data by using an OpenSearch Dashboards (Kibana).

D. Use AWS Glue streaming ETL to store the data in Amazon S3. Use Amazon QuickSight to build the dashboard.

A financial company hosts a data lake in Amazon S3 and a data warehouse on an Amazon Redshift cluster. The company uses Amazon QuickSight to build dashboards and wants to secure access from its on-premises Active Directory to Amazon QuickSight.
How should the data be secured?

A. Use an Active Directory connector and single sign-on (SSO) in a corporate network environment.

B. Use a VPC endpoint to connect to Amazon S3 from Amazon QuickSight and an IAM role to authenticate Amazon Redshift.

C. Establish a secure connection by creating an S3 endpoint to connect Amazon QuickSight and a VPC endpoint to connect to Amazon Redshift.

D. Place Amazon QuickSight and Amazon Redshift in the security group and use an Amazon S3 endpoint to connect Amazon QuickSight to Amazon S3.

A company has an encrypted Amazon Redshift cluster. The company recently enabled Amazon Redshift audit logs and needs to ensure that the audit logs are also encrypted at rest. The logs are retained for 1 year. The auditor queries the logs once a month.
What is the MOST cost-effective way to meet these requirements?

A. Encrypt the Amazon S3 bucket where the logs are stored by using AWS Key Management Service (AWS KMS). Copy the data into the Amazon Redshift cluster from Amazon S3 on a daily basis. Query the data as required.

B. Disable encryption on the Amazon Redshift cluster, configure audit logging, and encrypt the Amazon Redshift cluster. Use Amazon Redshift Spectrum to query the data as required.

C. Enable default encryption on the Amazon S3 bucket where the logs are stored by using AES-256 encryption. Copy the data into the Amazon Redshift cluster from Amazon S3 on a daily basis. Query the data as required.

D. Enable default encryption on the Amazon S3 bucket where the logs are stored by using AES-256 encryption. Use Amazon Redshift Spectrum to query the data as required.

A company that monitors weather conditions from remote construction sites is setting up a solution to collect temperature data from the following two weather stations.
✑ Station A, which has 10 sensors
✑ Station B, which has five sensors
These weather stations were placed by onsite subject-matter experts.
Each sensor has a unique ID. The data collected from each sensor will be collected using Amazon Kinesis Data Streams.
Based on the total incoming and outgoing data throughput, a single Amazon Kinesis data stream with two shards is created. Two partition keys are created based on the station names. During testing, there is a bottleneck on data coming from Station A, but not from Station B. Upon review, it is confirmed that the total stream throughput is still less than the allocated Kinesis Data Streams throughput.
How can this bottleneck be resolved without increasing the overall cost and complexity of the solution, while retaining the data collection quality requirements?

A. Increase the number of shards in Kinesis Data Streams to increase the level of parallelism.

B. Create a separate Kinesis data stream for Station A with two shards, and stream Station A sensor data to the new stream.

C. Modify the partition key to use the sensor ID instead of the station name.

D. Reduce the number of sensors in Station A from 10 to 5 sensors.

A company that produces network devices has millions of users. Data is collected from the devices on an hourly basis and stored in an Amazon S3 data lake.
The company runs analyses on the last 24 hours of data flow logs for abnormality detection and to troubleshoot and resolve user issues. The company also analyzes historical logs dating back 2 years to discover patterns and look for improvement opportunities.
The data flow logs contain many metrics, such as date, timestamp, source IP, and target IP. There are about 10 billion events every day.
How should this data be stored for optimal performance?

A. In Apache ORC partitioned by date and sorted by source IP

B. In compressed .csv partitioned by date and sorted by source IP

C. In Apache Parquet partitioned by source IP and sorted by date

D. In compressed nested JSON partitioned by source IP and sorted by date

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 stores its sales and marketing data that includes personally identifiable information (PII) in Amazon S3. The company allows its analysts to launch their own Amazon EMR cluster and run analytics reports with the data. To meet compliance requirements, the company must ensure the data is not publicly accessible throughout this process. A data engineer has secured Amazon S3 but must ensure the individual EMR clusters created by the analysts are not exposed to the public internet.
Which solution should the data engineer to meet this compliance requirement with LEAST amount of effort?

A. Create an EMR security configuration and ensure the security configuration is associated with the EMR clusters when they are created.

B. Check the security group of the EMR clusters regularly to ensure it does not allow inbound traffic from IPv4 0.0.0.0/0 or IPv6 ::/0.

C. Enable the block public access setting for Amazon EMR at the account level before any EMR cluster is created.

D. Use AWS WAF to block public internet access to the EMR clusters across the board.

A company stores revenue data in Amazon Redshift. A data analyst needs to create a dashboard so that the company's sales team can visualize historical revenue and accurately forecast revenue for the upcoming months.
Which solution will MOST cost-effectively meet these requirements?

A. Create an Amazon QuickSight analysis by using the data in Amazon Redshift. Add a custom field in QuickSight that applies a linear regression function to the data. Publish the analysis as a dashboard.

B. Create a JavaScript dashboard by using D3.js charts and the data in Amazon Redshift. Export the data to Amazon SageMaker. Run a Python script to run a regression model to forecast revenue. Import the data back into Amazon Redshift. Add the new forecast information to the dashboard.

C. Create an Amazon QuickSight analysis by using the data in Amazon Redshift. Add a forecasting widget Publish the analysis as a dashboard.

D. Create an Amazon SageMaker model for forecasting. Integrate the model with an Amazon QuickSight dataset. Create a widget for the dataset. Publish the analysis as a dashboard.

A company is Running Apache Spark on an Amazon EMR cluster. The Spark job writes to an Amazon S3 bucket. The job fails and returns an HTTP 503 `Slow
Down` AmazonS3Exception error.
Which actions will resolve this error? (Choose two.)

A. Add additional prefixes to the S3 bucket

B. Reduce the number of prefixes in the S3 bucket

C. Increase the EMR File System (EMRFS) retry limit

D. Disable dynamic partition pruning in the Spark configuration for the cluster

E. Add more partitions in the Spark configuration for the cluster

A data analyst is designing an Amazon QuickSight dashboard using centralized sales data that resides in Amazon Redshift. The dashboard must be restricted so that a salesperson in Sydney, Australia, can see only the Australia view and that a salesperson in New York can see only United States (US) data.
What should the data analyst do to ensure the appropriate data security is in place?

A. Place the data sources for Australia and the US into separate SPICE capacity pools.

B. Set up an Amazon Redshift VPC security group for Australia and the US.

C. Deploy QuickSight Enterprise edition to implement row-level security (RLS) to the sales table.

D. Deploy QuickSight Enterprise edition and set up different VPC security groups for Australia and the US.

A power utility company is deploying thousands of smart meters to obtain real-time updates about power consumption. The company is using Amazon Kinesis
Data Streams to collect the data streams from smart meters. The consumer application uses the Kinesis Client Library (KCL) to retrieve the stream data. The company has only one consumer application.
The company observes an average of 1 second of latency from the moment that a record is written to the stream until the record is read by a consumer application. The company must reduce this latency to 500 milliseconds.
Which solution meets these requirements?

A. Use enhanced fan-out in Kinesis Data Streams.

B. Increase the number of shards for the Kinesis data stream.

C. Reduce the propagation delay by overriding the KCL default settings.

D. Develop consumers by using Amazon Kinesis Data Firehose.

A manufacturing company has been collecting IoT sensor data from devices on its factory floor for a year and is storing the data in Amazon Redshift for daily analysis. A data analyst has determined that, at an expected ingestion rate of about 2 TB per day, the cluster will be undersized in less than 4 months. A long-term solution is needed. The data analyst has indicated that most queries only reference the most recent 13 months of data, yet there are also quarterly reports that need to query all the data generated from the past 7 years. The chief technology officer (CTO) is concerned about the costs, administrative effort, and performance of a long-term solution.
Which solution should the data analyst use to meet these requirements?

A. Create a daily job in AWS Glue to UNLOAD records older than 13 months to Amazon S3 and delete those records from Amazon Redshift. Create an external table in Amazon Redshift to point to the S3 location. Use Amazon Redshift Spectrum to join to data that is older than 13 months.

B. Take a snapshot of the Amazon Redshift cluster. Restore the cluster to a new cluster using dense storage nodes with additional storage capacity.

C. Execute a CREATE TABLE AS SELECT (CTAS) statement to move records that are older than 13 months to quarterly partitioned data in Amazon Redshift Spectrum backed by Amazon S3.

D. Unload all the tables in Amazon Redshift to an Amazon S3 bucket using S3 Intelligent-Tiering. Use AWS Glue to crawl the S3 bucket location to create external tables in an AWS Glue Data Catalog. Create an Amazon EMR cluster using Auto Scaling for any daily analytics needs, and use Amazon Athena for the quarterly reports, with both using the same AWS Glue Data Catalog.

An energy company collects voltage data in real time from sensors that are attached to buildings. The company wants to receive notifications when a sequence of two voltage drops is detected within 10 minutes of a sudden voltage increase at the same building. All notifications must be delivered as quickly as possible. The system must be highly available. The company needs a solution that will automatically scale when this monitoring feature is implemented in other cities. The notification system is subscribed to an Amazon Simple Notification Service (Amazon SNS) topic for remediation.
Which solution will meet these requirements?

A. Create an Amazon Managed Streaming for Apache Kafka cluster to ingest the data. Use an Apache Spark Streaming with Apache Kafka consumer API in an automatically scaled Amazon EMR cluster to process the incoming data. Use the Spark Streaming application to detect the known event sequence and send the SNS message.

B. Create a REST-based web service by using Amazon API Gateway in front of an AWS Lambda function. Create an Amazon RDS for PostgreSQL database with sufficient Provisioned IOPS to meet current demand. Configure the Lambda function to store incoming events in the RDS for PostgreSQL database, query the latest data to detect the known event sequence, and send the SNS message.

C. Create an Amazon Kinesis Data Firehose delivery stream to capture the incoming sensor data. Use an AWS Lambda transformation function to detect the known event sequence and send the SNS message.

D. Create an Amazon Kinesis data stream to capture the incoming sensor data. Create another stream for notifications. Set up AWS Application Auto Scaling on both streams. Create an Amazon Kinesis Data Analytics for Java application to detect the known event sequence, and add a message to the message stream Configure an AWS Lambda function to poll the message stream and publish to the SNS topic.

A data architect is building an Amazon S3 data lake for a bank. The goal is to provide a single data repository for customer data needs, such as personalized recommendations. The bank uses Amazon Kinesis Data Firehose to ingest customers' personal information bank accounts, and transactions in near-real time from a transactional relational database. The bank requires all personally identifiable information (PII) that is stored in the AWS Cloud to be masked.
Which solution will meet these requirements?

A. Invoke an AWS Lambda function from Kinesis Data Firehose to mask PII before delivering the data into Amazon S3.

B. Use Amazon Made, and configure it to discover and mask PII.

C. Enable server-side encryption (SSE) in Amazon S3.

D. Invoke Amazon Comprehend from Kinesis Data Firehose to detect and mask PII before delivering the data into Amazon S3.

A company operates toll services for highways across the country and collects data that is used to understand usage patterns. Analysts have requested the ability to run traffic reports in near-real time. The company is interested in building an ingestion pipeline that loads all the data into an Amazon Redshift cluster and alerts operations personnel when toll traffic for a particular toll station does not meet a specified threshold. Station data and the corresponding threshold values are stored in Amazon S3.
Which approach is the MOST efficient way to meet these requirements?

A. Use Amazon Kinesis Data Firehose to collect data and deliver it to Amazon Redshift and Amazon Kinesis Data Analytics simultaneously. Create a reference data source in Kinesis Data Analytics to temporarily store the threshold values from Amazon S3 and compare the count of vehicles for a particular toll station against its corresponding threshold value. Use AWS Lambda to publish an Amazon Simple Notification Service (Amazon SNS) notification if the threshold is not met.

B. Use Amazon Kinesis Data Streams to collect all the data from toll stations. Create a stream in Kinesis Data Streams to temporarily store the threshold values from Amazon S3. Send both streams to Amazon Kinesis Data Analytics to compare the count of vehicles for a particular toll station against its corresponding threshold value. Use AWS Lambda to publish an Amazon Simple Notification Service (Amazon SNS) notification if the threshold is not met. Connect Amazon Kinesis Data Firehose to Kinesis Data Streams to deliver the data to Amazon Redshift.

C. Use Amazon Kinesis Data Firehose to collect data and deliver it to Amazon Redshift. Then, automatically trigger an AWS Lambda function that queries the data in Amazon Redshift, compares the count of vehicles for a particular toll station against its corresponding threshold values read from Amazon S3, and publishes an Amazon Simple Notification Service (Amazon SNS) notification if the threshold is not met.

D. Use Amazon Kinesis Data Firehose to collect data and deliver it to Amazon Redshift and Amazon Kinesis Data Analytics simultaneously. Use Kinesis Data Analytics to compare the count of vehicles against the threshold value for the station stored in a table as an in-application stream based on information stored in Amazon S3. Configure an AWS Lambda function as an output for the application that will publish an Amazon Simple Queue Service (Amazon SQS) notification to alert operations personnel if the threshold is not met.

A company wants to improve the data load time of a sales data dashboard. Data has been collected as .csv files and stored within an Amazon S3 bucket that is partitioned by date. The data is then loaded to an Amazon Redshift data warehouse for frequent analysis. The data volume is up to 500 GB per day.
Which solution will improve the data loading performance?

A. Compress .csv files and use an INSERT statement to ingest data into Amazon Redshift.

B. Split large .csv files, then use a COPY command to load data into Amazon Redshift.

C. Use Amazon Kinesis Data Firehose to ingest data into Amazon Redshift.

D. Load the .csv files in an unsorted key order and vacuum the table in Amazon Redshift.

A company has 10-15 ׀¢׀’ 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 option for data format and compression meets these requirements?

A. CSV compressed with zip

B. JSON compressed with bzip2

C. Apache Parquet compressed with Snappy

D. Apache Avro compressed with LZO

A technology company has an application with millions of active users every day. The company queries daily usage data with Amazon Athena to understand how users interact with the application. The data includes the date and time, the location ID, and the services used. The company wants to use Athena to run queries to analyze the data with the lowest latency possible.
Which solution meets these requirements?

A. Store the data in Apache Avro format with the date and time as the partition, with the data sorted by the location ID.

B. Store the data in Apache Parquet format with the date and time as the partition, with the data sorted by the location ID.

C. Store the data in Apache ORC format with the location ID as the partition, with the data sorted by the date and time.

D. Store the data in .csv format with the location ID as the partition, with the data sorted by the date and time.

A company wants to research user turnover by analyzing the past 3 months of user activities. With millions of users, 1.5 TB of uncompressed data is generated each day. A 30-node Amazon Redshift cluster with 2.56 TB of solid state drive (SSD) storage for each node is required to meet the query performance goals.
The company wants to run an additional analysis on a year's worth of historical data to examine trends indicating which features are most popular. This analysis will be done once a week.
What is the MOST cost-effective solution?

A. Increase the size of the Amazon Redshift cluster to 120 nodes so it has enough storage capacity to hold 1 year of data. Then use Amazon Redshift for the additional analysis.

B. Keep the data from the last 90 days in Amazon Redshift. Move data older than 90 days to Amazon S3 and store it in Apache Parquet format partitioned by date. Then use Amazon Redshift Spectrum for the additional analysis.

C. Keep the data from the last 90 days in Amazon Redshift. Move data older than 90 days to Amazon S3 and store it in Apache Parquet format partitioned by date. Then provision a persistent Amazon EMR cluster and use Apache Presto for the additional analysis.

D. Resize the cluster node type to the dense storage node type (DS2) for an additional 16 TB storage capacity on each individual node in the Amazon Redshift cluster. Then use Amazon Redshift for the additional analysis.

A company is planning to do a proof of concept for a machine learning (ML) project using Amazon SageMaker with a subset of existing on-premises data hosted in the company's 3 TB data warehouse. For part of the project, AWS Direct Connect is established and tested. To prepare the data for ML, data analysts are performing data curation. The data analysts want to perform multiple step, including mapping, dropping null fields, resolving choice, and splitting fields. The company needs the fastest solution to curate the data for this project.
Which solution meets these requirements?

A. Ingest data into Amazon S3 using AWS DataSync and use Apache Spark scrips to curate the data in an Amazon EMR cluster. Store the curated data in Amazon S3 for ML processing.

B. Create custom ETL jobs on-premises to curate the data. Use AWS DMS to ingest data into Amazon S3 for ML processing.

C. Ingest data into Amazon S3 using AWS DMS. Use AWS Glue to perform data curation and store the data in Amazon S3 for ML processing.

D. Take a full backup of the data store and ship the backup files using AWS Snowball. Upload Snowball data into Amazon S3 and schedule data curation jobs using AWS Batch to prepare the data for ML.

A technology company is creating a dashboard that will visualize and analyze time-sensitive data. The data will come in through Amazon Kinesis Data Firehose with the butter interval set to 60 seconds. The dashboard must support near-real-time data.
Which visualization solution will meet these requirements?

A. Select Amazon OpenSearch Service (Amazon Elasticsearch Service) as the endpoint for Kinesis Data Firehose. Set up an OpenSearch Dashboards (Kibana) using the data in Amazon OpenSearch Service (Amazon ES) with the desired analyses and visualizations.

B. Select Amazon S3 as the endpoint for Kinesis Data Firehose. Read data into an Amazon SageMaker Jupyter notebook and carry out the desired analyses and visualizations.

C. Select Amazon Redshift as the endpoint for Kinesis Data Firehose. Connect Amazon QuickSight with SPICE to Amazon Redshift to create the desired analyses and visualizations.

D. Select Amazon S3 as the endpoint for Kinesis Data Firehose. Use AWS Glue to catalog the data and Amazon Athena to query it. Connect Amazon QuickSight with SPICE to Athena to create the desired analyses and visualizations.

A marketing company has data in Salesforce, MySQL, and Amazon S3. The company wants to use data from these three locations and create mobile dashboards for its users. The company is unsure how it should create the dashboards and needs a solution with the least possible customization and coding.
Which solution meets these requirements?

A. Use Amazon Athena federated queries to join the data sources. Use Amazon QuickSight to generate the mobile dashboards.

B. Use AWS Lake Formation to migrate the data sources into Amazon S3. Use Amazon QuickSight to generate the mobile dashboards.

C. Use Amazon Redshift federated queries to join the data sources. Use Amazon QuickSight to generate the mobile dashboards.

D. Use Amazon QuickSight to connect to the data sources and generate the mobile dashboards.

A company analyzes historical data and needs to query data that is stored in Amazon S3. New data is generated daily as .csv files that are stored in Amazon S3.
The company's analysts are using Amazon Athena to perform SQL queries against a recent subset of the overall data. The amount of data that is ingested into
Amazon S3 has increased substantially over time, and the query latency also has increased.
Which solutions could the company implement to improve query performance? (Choose two.)

A. Use MySQL Workbench on an Amazon EC2 instance, and connect to Athena by using a JDBC or ODBC connector. Run the query from MySQL Workbench instead of Athena directly.

B. Use Athena to extract the data and store it in Apache Parquet format on a daily basis. Query the extracted data.

C. Run a daily AWS Glue ETL job to convert the data files to Apache Parquet and to partition the converted files. Create a periodic AWS Glue crawler to automatically crawl the partitioned data on a daily basis.

D. Run a daily AWS Glue ETL job to compress the data files by using the .gzip format. Query the compressed data.

E. Run a daily AWS Glue ETL job to compress the data files by using the .lzo format. Query the compressed data.

A company hosts an on-premises PostgreSQL database that contains historical data. An internal legacy application uses the database for read-only activities. The company's business team wants to move the data to a data lake in Amazon S3 as soon as possible and enrich the data for analytics.
The company has set up an AWS Direct Connect connection between its VPC and its on-premises network. A data analytics specialist must design a solution that achieves the business team's goals with the least operational overhead.
Which solution meets these requirements?

A. Upload the data from the on-premises PostgreSQL database to Amazon S3 by using a customized batch upload process. Use the AWS Glue crawler to catalog the data in Amazon S3. Use an AWS Glue job to enrich and store the result in a separate S3 bucket in Apache Parquet format. Use Amazon Athena to query the data.

B. Create an Amazon RDS for PostgreSQL database and use AWS Database Migration Service (AWS DMS) to migrate the data into Amazon RDS. Use AWS Data Pipeline to copy and enrich the data from the Amazon RDS for PostgreSQL table and move the data to Amazon S3. Use Amazon Athena to query the data.

C. Configure an AWS Glue crawler to use a JDBC connection to catalog the data in the on-premises database. Use an AWS Glue job to enrich the data and save the result to Amazon S3 in Apache Parquet format. Create an Amazon Redshift cluster and use Amazon Redshift Spectrum to query the data.

D. Configure an AWS Glue crawler to use a JDBC connection to catalog the data in the on-premises database. Use an AWS Glue job to enrich the data and save the result to Amazon S3 in Apache Parquet format. Use Amazon Athena to query the data.

An ecommerce company ingests a large set of clickstream data in JSON format and stores the data in Amazon S3. Business analysts from multiple product divisions need to use Amazon Athena to analyze the data. The company's analytics team must design a solution to monitor the daily data usage for Athena by each product division. The solution also must produce a warning when a division exceeds its quota.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use a CREATE TABLE AS SELECT (CTAS) statement to create separate tables for each product division. Use AWS Budgets to track Athena usage. Configure a threshold for the budget. Use Amazon Simple Notification Service (Amazon SNS) to send notifications when thresholds are breached.

B. Create an AWS account for each division. Provide cross-account access to an AWS Glue Data Catalog to all the accounts. Set an Amazon CloudWatch alarm to monitor Athena usage. Use Amazon Simple Notification Service (Amazon SNS) to send notifications.

C. Create an Athena workgroup for each division. Configure a data usage control for each workgroup and a time period of 1 day. Configure an action to send notifications to an Amazon Simple Notification Service (Amazon SNS) topic.

D. Create an AWS account for each division. Configure an AWS Glue Data Catalog in each account. Set an Amazon CloudWatch alarm to monitor Athena usage. Use Amazon Simple Notification Service (Amazon SNS) to send notifications.

A data engineer is using AWS Glue ETL jobs to process data at frequent intervals. The processed data is then copied into Amazon S3. The ETL jobs run every 15 minutes. The AWS Glue Data Catalog partitions need to be updated automatically after the completion of each job.
Which solution will meet these requirements MOST cost-effectively?

A. Use the AWS Glue Data Catalog to manage the data catalog. Define an AWS Glue workflow for the ETL process. Define a trigger within the workflow that can start the crawler when an ETL job run is complete.

B. Use the AWS Glue Data Catalog to manage the data catalog. Use AWS Glue Studio to manage ETL jobs. Use the AWS Glue Studio feature that supports updates to the AWS Glue Data Catalog during job runs.

C. Use an Apache Hive metastore to manage the data catalog. Update the AWS Glue ETL code to include the enableUpdateCatalog and partitionKeys arguments.

D. Use the AWS Glue Data Catalog to manage the data catalog. Update the AWS Glue ETL code to include the enableUpdateCatalog and partitionKeys arguments.

A company is streaming its high-volume billing data (100 MBps) to Amazon Kinesis Data Streams. A data analyst partitioned the data on account_id to ensure that all records belonging to an account go to the same Kinesis shard and order is maintained. While building a custom consumer using the Kinesis Java SDK, the data analyst notices that, sometimes, the messages arrive out of order for account_id. Upon further investigation, the data analyst discovers the messages that are out of order seem to be arriving from different shards for the same account_id and are seen when a stream resize runs.
What is an explanation for this behavior and what is the solution?

A. There are multiple shards in a stream and order needs to be maintained in the shard. The data analyst needs to make sure there is only a single shard in the stream and no stream resize runs.

B. The hash key generation process for the records is not working correctly. The data analyst should generate an explicit hash key on the producer side so the records are directed to the appropriate shard accurately.

C. The records are not being received by Kinesis Data Streams in order. The producer should use the PutRecords API call instead of the PutRecord API call with the SequenceNumberForOrdering parameter.

D. The consumer is not processing the parent shard completely before processing the child shards after a stream resize. The data analyst should process the parent shard completely first before processing the child shards.

A large financial company is running its ETL process. Part of this process is to move data from Amazon S3 into an Amazon Redshift cluster. The company wants to use the most cost-efficient method to load the dataset into Amazon Redshift.
Which combination of steps would meet these requirements? (Choose two.)

A. Use the COPY command with the manifest file to load data into Amazon Redshift.

B. Use S3DistCp to load files into Amazon Redshift.

C. Use temporary staging tables during the loading process.

D. Use the UNLOAD command to upload data into Amazon Redshift.

E. Use Amazon Redshift Spectrum to query files from Amazon S3.

A web retail company wants to implement a near-real-time clickstream analytics solution. The company wants to analyze the data with an open-source package.
The analytics application will process the raw data only once, but other applications will need immediate access to the raw data for up to 1 year.
Which solution meets these requirements with the LEAST amount of operational effort?

A. Use Amazon Kinesis Data Streams to collect the data. Use Amazon EMR with Apache Flink to consume and process the data from the Kinesis data stream. Set the retention period of the Kinesis data stream to 8.760 hours.

B. Use Amazon Kinesis Data Streams to collect the data. Use Amazon Kinesis Data Analytics with Apache Flink to process the data in real time. Set the retention period of the Kinesis data stream to 8,760 hours.

C. Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to collect the data. Use Amazon EMR with Apache Flink to consume and process the data from the Amazon MSK stream. Set the log retention hours to 8,760.

D. Use Amazon Kinesis Data Streams to collect the data. Use Amazon EMR with Apache Flink to consume and process the data from the Kinesis data stream. Create an Amazon Kinesis Data Firehose delivery stream to store the data in Amazon S3. Set an S3 Lifecycle policy to delete the data after 365 days.

A data analyst is designing a solution to interactively query datasets with SQL using a JDBC connection. Users will join data stored in Amazon S3 in Apache ORC format with data stored in Amazon OpenSearch Service (Amazon Elasticsearch Service) and Amazon Aurora MySQL.
Which solution will provide the MOST up-to-date results?

A. Use AWS Glue jobs to ETL data from Amazon ES and Aurora MySQL to Amazon S3. Query the data with Amazon Athena.

B. Use Amazon DMS to stream data from Amazon ES and Aurora MySQL to Amazon Redshift. Query the data with Amazon Redshift.

C. Query all the datasets in place with Apache Spark SQL running on an AWS Glue developer endpoint.

D. Query all the datasets in place with Apache Presto running on Amazon EMR.

A company has developed an Apache Hive script to batch process data stared in Amazon S3. The script needs to run once every day and store the output in
Amazon S3. The company tested the script, and it completes within 30 minutes on a small local three-node cluster.
Which solution is the MOST cost-effective for scheduling and executing the script?

A. Create an AWS Lambda function to spin up an Amazon EMR cluster with a Hive execution step. Set KeepJobFlowAliveWhenNoSteps to false and disable the termination protection flag. Use Amazon CloudWatch Events to schedule the Lambda function to run daily.

B. Use the AWS Management Console to spin up an Amazon EMR cluster with Python Hue. Hive, and Apache Oozie. Set the termination protection flag to true and use Spot Instances for the core nodes of the cluster. Configure an Oozie workflow in the cluster to invoke the Hive script daily.

C. Create an AWS Glue job with the Hive script to perform the batch operation. Configure the job to run once a day using a time-based schedule.

D. Use AWS Lambda layers and load the Hive runtime to AWS Lambda and copy the Hive script. Schedule the Lambda function to run daily by creating a workflow using AWS Step Functions.

A company is providing analytics services to its sales and marketing departments. The departments can access the data only through their business intelligence
(BI) tools, which run queries on Amazon Redshift using an Amazon Redshift internal user to connect. Each department is assigned a user in the Amazon Redshift database with the permissions needed for that department. The marketing data analysts must be granted direct access to the advertising table, which is stored in
Apache Parquet format in the marketing S3 bucket of the company data lake. The company data lake is managed by AWS Lake Formation. Finally, access must be limited to the three promotion columns in the table.
Which combination of steps will meet these requirements? (Choose three.)

A. Grant permissions in Amazon Redshift to allow the marketing Amazon Redshift user to access the three promotion columns of the advertising external table.

B. Create an Amazon Redshift Spectrum IAM role with permissions for Lake Formation. Attach it to the Amazon Redshift cluster.

C. Create an Amazon Redshift Spectrum IAM role with permissions for the marketing S3 bucket. Attach it to the Amazon Redshift cluster.

D. Create an external schema in Amazon Redshift by using the Amazon Redshift Spectrum IAM role. Grant usage to the marketing Amazon Redshift user.

E. Grant permissions in Lake Formation to allow the Amazon Redshift Spectrum role to access the three promotion columns of the advertising table.

F. Grant permissions in Lake Formation to allow the marketing IAM group to access the three promotion columns of the advertising table.

An online retail company is migrating its reporting system to AWS. The company's legacy system runs data processing on online transactions using a complex series of nested Apache Hive queries. Transactional data is exported from the online system to the reporting system several times a day. Schemas in the files are stable between updates.
A data analyst wants to quickly migrate the data processing to AWS, so any code changes should be minimized. To keep storage costs low, the data analyst decides to store the data in Amazon S3. It is vital that the data from the reports and associated analytics is completely up to date based on the data in Amazon S3.
Which solution meets these requirements?

A. Create an AWS Glue Data Catalog to manage the Hive metadata. Create an AWS Glue crawler over Amazon S3 that runs when data is refreshed to ensure that data changes are updated. Create an Amazon EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.

B. Create an AWS Glue Data Catalog to manage the Hive metadata. Create an Amazon EMR cluster with consistent view enabled. Run emrfs sync before each analytics step to ensure data changes are updated. Create an EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.

C. Create an Amazon Athena table with CREATE TABLE AS SELECT (CTAS) to ensure data is refreshed from underlying queries against the raw dataset. Create an AWS Glue Data Catalog to manage the Hive metadata over the CTAS table. Create an Amazon EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.

D. Use an S3 Select query to ensure that the data is properly updated. Create an AWS Glue Data Catalog to manage the Hive metadata over the S3 Select table. Create an Amazon EMR cluster and use the metadata in the AWS Glue Data Catalog to run Hive processing queries in Amazon EMR.

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.

A company currently uses Amazon Athena to query its global datasets. The regional data is stored in Amazon S3 in the us-east-1 and us-west-2 Regions. The data is not encrypted. To simplify the query process and manage it centrally, the company wants to use Athena in us-west-2 to query data from Amazon S3 in both
Regions. The solution should be as low-cost as possible.
What should the company do to achieve this goal?

A. Use AWS DMS to migrate the AWS Glue Data Catalog from us-east-1 to us-west-2. Run Athena queries in us-west-2.

B. Run the AWS Glue crawler in us-west-2 to catalog datasets in all Regions. Once the data is crawled, run Athena queries in us-west-2.

C. Enable cross-Region replication for the S3 buckets in us-east-1 to replicate data in us-west-2. Once the data is replicated in us-west-2, run the AWS Glue crawler there to update the AWS Glue Data Catalog in us-west-2 and run Athena queries.

D. Update AWS Glue resource policies to provide us-east-1 AWS Glue Data Catalog access to us-west-2. Once the catalog in us-west-2 has access to the catalog in us-east-1, run Athena queries in us-west-2.

An Amazon Redshift database contains sensitive user data. Logging is necessary to meet compliance requirements. The logs must contain database authentication attempts, connections, and disconnections. The logs must also contain each query run against the database and record which database user ran each query.
Which steps will create the required logs?

A. Enable Amazon Redshift Enhanced VPC Routing. Enable VPC Flow Logs to monitor traffic.

B. Allow access to the Amazon Redshift database using AWS IAM only. Log access using AWS CloudTrail.

C. Enable audit logging for Amazon Redshift using the AWS Management Console or the AWS CLI.

D. Enable and download audit reports from AWS Artifact.

A media company has a streaming playback application. The company needs to collect and analyze data to provide near-real-time feedback on playback issues within 30 seconds. The company requires a consumer application to identify playback issues, such as decreased quality during a specified time frame. The data will be streamed in JSON format. The schema can change over time.
Which solution will meet these requirements?

A. Send the data to Amazon Kinesis Data Firehose with delivery to Amazon S3. Configure an S3 event to invoke an AWS Lambda function to process and analyze the data.

B. Send the data to Amazon Managed Streaming for Apache Kafka. Configure Amazon Kinesis Data Analytics for SQL Application as the consumer application to process and analyze the data.

C. Send the data to Amazon Kinesis Data Firehose with delivery to Amazon S3. Configure Amazon S3 to initiate an event for AWS Lambda to process and analyze the data.

D. Send the data to Amazon Kinesis Data Streams. Configure an Amazon Kinesis Data Analytics for Apache Flink application as the consumer application to process and analyze the data.

A hospital uses wearable medical sensor devices to collect data from patients. The hospital is architecting a near-real-time solution that can ingest the data securely at scale. The solution should also be able to remove the patient's protected health information (PHI) from the streaming data and store the data in durable storage.
Which solution meets these requirements with the least operational overhead?

A. Ingest the data using Amazon Kinesis Data Streams, which invokes an AWS Lambda function using Kinesis Client Library (KCL) to remove all PHI. Write the data in Amazon S3.

B. Ingest the data using Amazon Kinesis Data Firehose to write the data to Amazon S3. Have Amazon S3 trigger an AWS Lambda function that parses the sensor data to remove all PHI in Amazon S3.

C. Ingest the data using Amazon Kinesis Data Streams to write the data to Amazon S3. Have the data stream launch an AWS Lambda function that parses the sensor data and removes all PHI in Amazon S3.

D. Ingest the data using Amazon Kinesis Data Firehose to write the data to Amazon S3. Implement a transformation AWS Lambda function that parses the sensor data to remove all PHI.

A medical company has a system with sensor devices that read metrics and send them in real time to an Amazon Kinesis data stream. The Kinesis data stream has multiple shards. The company needs to calculate the average value of a numeric metric every second and set an alarm for whenever the value is above one threshold or below another threshold. The alarm must be sent to Amazon Simple Notification Service (Amazon SNS) in less than 30 seconds.
Which architecture meets these requirements?

A. Use an Amazon Kinesis Data Firehose delivery stream to read the data from the Kinesis data stream with an AWS Lambda transformation function that calculates the average per second and sends the alarm to Amazon SNS.

B. Use an AWS Lambda function to read from the Kinesis data stream to calculate the average per second and sent the alarm to Amazon SNS.

C. Use an Amazon Kinesis Data Firehose deliver stream to read the data from the Kinesis data stream and store it on Amazon S3. Have Amazon S3 trigger an AWS Lambda function that calculates the average per second and sends the alarm to Amazon SNS.

D. Use an Amazon Kinesis Data Analytics application to read from the Kinesis data stream and calculate the average per second. Send the results to an AWS Lambda function that sends the alarm to Amazon SNS.

A company uses Amazon Redshift as its data warehouse. A new table includes some columns that contain sensitive data and some columns that contain non- sensitive data. The data in the table eventually will be referenced by several existing queries that run many times each day.
A data analytics specialist must ensure that only members of the company's auditing team can read the columns that contain sensitive data. All other users must have read-only access to the columns that contain non-sensitive data.
Which solution will meet these requirements with the LEAST operational overhead?

A. Grant the auditing team permission to read from the table. Load the columns that contain non-sensitive data into a second table. Grant the appropriate users read-only permissions to the second table.

B. Grant all users read-only permissions to the columns that contain non-sensitive data. Use the GRANT SELECT command to allow the auditing team to access the columns that contain sensitive data.

C. Grant all users read-only permissions to the columns that contain non-sensitive data. Attach an IAM policy to the auditing team with an explicit. Allow action that grants access to the columns that contain sensitive data.

D. Grant the auditing team permission to read from the table. Create a view of the table that includes the columns that contain non-sensitive data. Grant the appropriate users read-only permissions to that view.

A social media company is using business intelligence tools to analyze its data for forecasting. The company is using Apache Kafka to ingest the low-velocity data in near-real time. The company wants to build dynamic dashboards with machine learning (ML) insights to forecast key business trends. The dashboards must provide hourly updates from data in Amazon S3. Various teams at the company want to view the dashboards by using Amazon QuickSight with ML insights. The solution also must correct the scalability problems that the company experiences when it uses its current architecture to ingest data.
Which solution will MOST cost-effectively meet these requirements?

A. Replace Kafka with Amazon Managed Streaming for Apache Kafka. Ingest the data by using AWS Lambda, and store the data in Amazon S3. Use QuickSight Standard edition to refresh the data in SPICE from Amazon S3 hourly and create a dynamic dashboard with forecasting and ML insights.

B. Replace Kafka with an Amazon Kinesis data stream. Use an Amazon Kinesis Data Firehose delivery stream to consume the data and store the data in Amazon S3. Use QuickSight Enterprise edition to refresh the data in SPICE from Amazon S3 hourly and create a dynamic dashboard with forecasting and ML insights.

C. Configure the Kafka-Kinesis-Connector to publish the data to an Amazon Kinesis Data Firehose delivery stream that is configured to store the data in Amazon S3. Use QuickSight Enterprise edition to refresh the data in SPICE from Amazon S3 hourly and create a dynamic dashboard with forecasting and ML insights.

D. Configure the Kafka-Kinesis-Connector to publish the data to an Amazon Kinesis Data Firehose delivery stream that is configured to store the data in Amazon S3. Configure an AWS Glue crawler to crawl the data. Use an Amazon Athena data source with QuickSight Standard edition to refresh the data in SPICE hourly and create a dynamic dashboard with forecasting and ML insights.

A company needs to store objects containing log data in JSON format. The objects are generated by eight applications running in AWS. Six of the applications generate a total of 500 KiB of data per second, and two of the applications can generate up to 2 MiB of data per second.
A data engineer wants to implement a scalable solution to capture and store usage data in an Amazon S3 bucket. The usage data objects need to be reformatted, converted to .csv format, and then compressed before they are stored in Amazon S3. The company requires the solution to include the least custom code possible and has authorized the data engineer to request a service quota increase if needed.
Which solution meets these requirements?

A. Configure an Amazon Kinesis Data Firehose delivery stream for each application. Write AWS Lambda functions to read log data objects from the stream for each application. Have the function perform reformatting and .csv conversion. Enable compression on all the delivery streams.

B. Configure an Amazon Kinesis data stream with one shard per application. Write an AWS Lambda function to read usage data objects from the shards. Have the function perform .csv conversion, reformatting, and compression of the data. Have the function store the output in Amazon S3.

C. Configure an Amazon Kinesis data stream for each application. Write an AWS Lambda function to read usage data objects from the stream for each application. Have the function perform .csv conversion, reformatting, and compression of the data. Have the function store the output in Amazon S3.

D. Store usage data objects in an Amazon DynamoDB table. Configure a DynamoDB stream to copy the objects to an S3 bucket. Configure an AWS Lambda function to be triggered when objects are written to the S3 bucket. Have the function convert the objects into .csv format.

A retail company wants to use Amazon QuickSight to generate dashboards for web and in-store sales. A group of 50 business intelligence professionals will develop and use the dashboards. Once ready, the dashboards will be shared with a group of 1,000 users.
The sales data comes from different stores and is uploaded to Amazon S3 every 24 hours. The data is partitioned by year and month, and is stored in Apache
Parquet format. The company is using the AWS Glue Data Catalog as its main data catalog and Amazon Athena for querying. The total size of the uncompressed data that the dashboards query from at any point is 200 GB.
Which configuration will provide the MOST cost-effective solution that meets these requirements?

A. Load the data into an Amazon Redshift cluster by using the COPY command. Configure 50 author users and 1,000 reader users. Use QuickSight Enterprise edition. Configure an Amazon Redshift data source with a direct query option.

B. Use QuickSight Standard edition. Configure 50 author users and 1,000 reader users. Configure an Athena data source with a direct query option.

C. Use QuickSight Enterprise edition. Configure 50 author users and 1,000 reader users. Configure an Athena data source and import the data into SPICE. Automatically refresh every 24 hours.

D. Use QuickSight Enterprise edition. Configure 1 administrator and 1,000 reader users. Configure an S3 data source and import the data into SPICE. Automatically refresh every 24 hours.

A company has developed several AWS Glue jobs to validate and transform its data from Amazon S3 and load it into Amazon RDS for MySQL in batches once every day. The ETL jobs read the S3 data using a DynamicFrame. Currently, the ETL developers are experiencing challenges in processing only the incremental data on every run, as the AWS Glue job processes all the S3 input data on each run.
Which approach would allow the developers to solve the issue with minimal coding effort?

A. Have the ETL jobs read the data from Amazon S3 using a DataFrame.

B. Enable job bookmarks on the AWS Glue jobs.

C. Create custom logic on the ETL jobs to track the processed S3 objects.

D. Have the ETL jobs delete the processed objects or data from Amazon S3 after each run.

A mortgage company has a microservice for accepting payments. This microservice uses the Amazon DynamoDB encryption client with AWS KMS managed keys to encrypt the sensitive data before writing the data to DynamoDB. The finance team should be able to load this data into Amazon Redshift and aggregate the values within the sensitive fields. The Amazon Redshift cluster is shared with other data analysts from different business units.
Which steps should a data analyst take to accomplish this task efficiently and securely?

A. Create an AWS Lambda function to process the DynamoDB stream. Decrypt the sensitive data using the same KMS key. Save the output to a restricted S3 bucket for the finance team. Create a finance table in Amazon Redshift that is accessible to the finance team only. Use the COPY command to load the data from Amazon S3 to the finance table.

B. Create an AWS Lambda function to process the DynamoDB stream. Save the output to a restricted S3 bucket for the finance team. Create a finance table in Amazon Redshift that is accessible to the finance team only. Use the COPY command with the IAM role that has access to the KMS key to load the data from S3 to the finance table.

C. Create an Amazon EMR cluster with an EMR_EC2_DefaultRole role that has access to the KMS key. Create Apache Hive tables that reference the data stored in DynamoDB and the finance table in Amazon Redshift. In Hive, select the data from DynamoDB and then insert the output to the finance table in Amazon Redshift.

D. Create an Amazon EMR cluster. Create Apache Hive tables that reference the data stored in DynamoDB. Insert the output to the restricted Amazon S3 bucket for the finance team. Use the COPY command with the IAM role that has access to the KMS key to load the data from Amazon S3 to the finance table in Amazon Redshift.

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 execute. The company needs to optimize query execution and avoid any downtime.
What is the MOST cost-effective solution?

A. Enable 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 launched a service that produces millions of messages every day and uses Amazon Kinesis Data Streams as the streaming service.
The company uses the Kinesis SDK to write data to Kinesis Data Streams. A few months after launch, a data analyst found that write performance is significantly reduced. The data analyst investigated the metrics and determined that Kinesis is throttling the write requests. The data analyst wants to address this issue without significant changes to the architecture.
Which actions should the data analyst take to resolve this issue? (Choose two.)

A. Increase the Kinesis Data Streams retention period to reduce throttling.

B. Replace the Kinesis API-based data ingestion mechanism with Kinesis Agent.

C. Increase the number of shards in the stream using the UpdateShardCount API.

D. Choose partition keys in a way that results in a uniform record distribution across shards.

E. Customize the application code to include retry logic to improve performance.

A global company has different sub-organizations, and each sub-organization sells its products and services in various countries. The company's senior leadership wants to quickly identify which sub-organization is the strongest performer in each country. All sales data is stored in Amazon S3 in Parquet format.
Which approach can provide the visuals that senior leadership requested with the least amount of effort?

A. Use Amazon QuickSight with Amazon Athena as the data source. Use heat maps as the visual type.

B. Use Amazon QuickSight with Amazon S3 as the data source. Use heat maps as the visual type.

C. Use Amazon QuickSight with Amazon Athena as the data source. Use pivot tables as the visual type.

D. Use Amazon QuickSight with Amazon S3 as the data source. Use pivot tables as the visual type.

A company has a business unit uploading .csv files to an Amazon S3 bucket. The company's data platform team has set up an AWS Glue crawler to do discovery, and create tables and schemas. An AWS Glue job writes processed data from the created tables to an Amazon Redshift database. The AWS Glue job handles column mapping and creating the Amazon Redshift table appropriately. When the AWS Glue job is rerun for any reason in a day, duplicate records are introduced into the Amazon Redshift table.
Which solution will update the Redshift table without duplicates when jobs are rerun?

A. Modify the AWS Glue job to copy the rows into a staging table. Add SQL commands to replace the existing rows in the main table as postactions in the DynamicFrameWriter class.

B. Load the previously inserted data into a MySQL database in the AWS Glue job. Perform an upsert operation in MySQL, and copy the results to the Amazon Redshift table.

C. Use Apache Spark’s DataFrame dropDuplicates() API to eliminate duplicates and then write the data to Amazon Redshift.

D. Use the AWS Glue ResolveChoice built-in transform to select the most recent value of the column.