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The problem described requires identifying matching records even when there is no unique identifier. AWS Lake Formation FindMatches is designed for this purpose. It uses machine learning (ML) to deduplicate and find matching records in datasets that do not share a common identifier.Alternatives Considered:A (Amazon Made pattern matching): Amazon Made is not a service in AWS, and pattern matching typically refers to regular expressions, which are not suitable for deduplication without a common identifier.B (AWS Glue PySpark Filter class): PySpark's Filter class can help refine datasets, but it does not offer the ML-based matching capabilities required to find matches between records without unique identifiers.C (Partition tables on a unique identifier): Partitioning requires a unique identifier, which the question states is unavailable.AWS Glue Documentation on Lake Formation FindMatchesFindMatches in AWS Lake FormationD Train and use the AWS Lake Formation FindMatches transform in the ETL job: FindMatches is a transform available in AWS Lake Formation that uses ML to discover duplicate records or related records that might not have a common unique identifier. It can be integrated into an AWS Glue ETL job to perform deduplication or matching tasks. FindMatches is highly effective in scenarios where records do not share a key, such as customer records from different sources that need to be merged or reconciled.

The requirement is to detect violations of data access policies and receive notifications with the username of the violator. AWS CloudTrail can provide object-level tracking for S3 to capture detailed API actions on specific S3 objects, including the user who performed the action.AWS CloudTrail:CloudTrail can monitor API calls made to an S3 bucket, including object-level API actions such as GetObject, PutObject, and DeleteObject. This will help detect access violations based on the API calls made by different users.CloudTrail logs include details such as the user identity, which is essential for meeting the requirement of including the username in notifications.The CloudTrail logs can be forwarded to Amazon CloudWatch to trigger alarms based on certain access patterns (e.g., violations of specific policies).Amazon CloudWatch:By forwarding CloudTrail logs to CloudWatch, you can set up alarms that are triggered when a specific condition is met, such as unauthorized access or policy violations. The alarm can include detailed information from the CloudTrail log, including the username.Alternatives Considered:A (AWS Config rules): While AWS Config can track resource configurations and compliance, it does not provide real-time, detailed tracking of object-level events like CloudTrail does.B (CloudWatch metrics): CloudWatch does not gather object-level metrics for S3 directly. For this use case, CloudTrail provides better granularity.D (S3 server access logs): S3 server access logs can monitor access, but they do not provide the real-time monitoring and alerting features that CloudTrail with CloudWatch alarms offer. They also do not include API-level granularity like CloudTrail.AWS CloudTrail Integration with S3Amazon CloudWatch Alarms

To prevent unintentional file deletions and meet the requirement with minimal operational overhead, enabling S3 Versioning is the best solution.S3 Versioning:S3 Versioning allows multiple versions of an object to be stored in the same S3 bucket. When a file is deleted or overwritten, S3 preserves the previous versions, which means you can recover from accidental deletions or modifications.Enabling versioning requires minimal overhead, as it is a bucket-level setting and does not require additional backup processes or data replication.Users can recover specific versions of files that were unintentionally deleted, meeting the needs of the data engineer to avoid accidental data loss.Alternatives Considered:A (Manual backups): Manually backing up the bucket requires higher operational effort and maintenance compared to enabling S3 Versioning, which is automated.C (S3 Replication): Replication ensures data is copied to another bucket but does not provide protection against accidental deletion. It would increase operational costs without solving the core issue of accidental deletion.D (S3 Glacier): Storing data in Glacier provides long-term archival storage but is not designed to prevent accidental deletion. Glacier is also more suitable for archival and infrequently accessed data, not for active logs.Amazon S3 Versioning DocumentationS3 Data Protection Best Practices

The company's Apache Spark ETL job on Amazon EMR uses high CPU but low memory, meaning that compute-optimized EC2 instances would be the most cost-effective choice. These instances are designed for highperformance compute applications, where CPU usage is high, but memory needs are minimal, which is exactly the case here.Compute Optimized Instances:Compute-optimized instances, such as the C5 series, provide a higher ratio of CPU to memory, which is more suitable for jobs with high CPU usage and relatively low memory consumption.Switching from general-purpose EC2 instances to compute-optimized instances can reduce costs while improving performance, as these instances are optimized for workloads like Spark jobs that perform a lot of computation.Managed Scaling: The EMR cluster's scaling is currently managed between 1 and 5 nodes, so changing the instance type will leverage the current scaling strategy but optimize it for the workload.Alternatives Considered:A (Increase task nodes to 10): Increasing the number of task nodes would increase costs without necessarily improving performance. Since memory usage is low, the bottleneck is more likely the CPU, which computeoptimized instances can handle better.B (Memory optimized instances): Memory-optimized instances are not suitable since the current job is CPU-bound, and memory usage remains low (under 30%).D (Reduce scaling cooldown): This could marginally improve scaling speed but does not address the need for cost optimization and improved CPU performance.Amazon EMR Cluster OptimizationCompute Optimized EC2 Instances

In this scenario, the company is using Amazon Elastic Kubernetes Service (EKS) and wants secure access to DynamoDB without embedding credentials inside the application containers. The best practice is to use IAM roles for service accounts (IRSA), which allows assigning IAM roles to Kubernetes service accounts. This lets the EKS pods assume specific IAM roles securely, without the need to store credentials in containers.IAM Roles for Service Accounts (IRSA):With IRSA, each pod in the EKS cluster can assume an IAM role that grants access to DynamoDB without needing to manage long-term credentials. The IAM role can be attached to the service account associated with the pod.This ensures least privilege access, improving security by preventing credentials from being embedded in the containers.Alternatives Considered:A (Storing AWS credentials in S3): Storing AWS credentials in S3 and retrieving them introduces security risks and violates the principle of not embedding credentials.C (IAM user access keys in environment variables): This also embeds credentials, which is not recommended.D (Kubernetes secrets): Storing user access keys as secrets is an option, but it still involves handling long-term credentials manually, which is less secure than using IRSA.IAM Best Practices for Amazon EKSSecure Access to DynamoDB from EKS

Option A is the most operationally efficient way to meet the requirements because it minimizes the number of steps and services involved in the data export process. AWS Glue is a fully managed service that can extract, transform, and load (ETL) data from various sources to various destinations, including Amazon S3. AWS Glue can also convert data to different formats, such as Parquet, which is a columnar storage format that is optimized for analytics. By creating a view in the SQL Server databases that contains the required data elements, the AWS Glue job can select the data directly from the view without having to perform any joins or transformations on the source data. The AWS Glue job can then transfer the data in Parquet format to an S3 bucket and run on a daily schedule.Option B is not operationally efficient because it involves multiple steps and services to export the data. SQL Server Agent is a tool that can run scheduled tasks on SQL Server databases, such as executing SQL queries.However, SQL Server Agent cannot directly export data to S3, so the query output must be saved as .csv objects on the EC2 instance. Then, an S3 event must be configured to trigger an AWS Lambda function that can transform the .csv objects to Parquet format and upload them to S3. This option adds complexity and latency to the data export process and requires additional resources and configuration.Option C is not operationally efficient because it introduces an unnecessary step of running an AWS Glue crawler to read the view. An AWS Glue crawler is a service that can scan data sources and create metadata tables in the AWS Glue Data Catalog. The Data Catalog is a central repository that stores information about the data sources, such as schema, format, and location. However, in this scenario, the schema and format of the data elements are already known and fixed, so there is no need to run a crawler to discover them. The AWS Glue job can directly select the data from the view without using the Data Catalog. Running a crawler adds extra time and cost to the data export process.Option D is not operationally efficient because it requires custom code and configuration to query the databases and transform the data. An AWS Lambda function is a service that can run code in response to events or triggers, such as Amazon EventBridge. Amazon EventBridge is a service that can connect applications and services with event sources, such as schedules, and route them to targets, such as Lambda functions. However, in this scenario, using a Lambda function to query the databases and transform the data is not the best option because it requires writing and maintaining code that uses JDBC to connect to the SQL Server databases, retrieve the required data, convert the data to Parquet format, and transfer the data to S3. This option also has limitations on the execution time, memory, and concurrency of the Lambda function, which may affect the performance and reliability of the data export process.AWS Certified Data Engineer - Associate DEA-C01 Complete Study GuideAWS Glue DocumentationWorking with Views in AWS GlueConverting to Columnar Formats

The STL ALERT EVENT LOG table view records anomalies when the query optimizer identifies conditions that might indicate performance issues. These conditions include skewed data distribution, missing statistics, nested loop joins, and broadcasted data. The STL ALERT EVENT LOG table view can help the data engineer to identify and troubleshoot the root causes of performance issues and optimize the query execution plan. The other table views are not relevant for this requirement. STL USAGE CONTROL records the usage limits and quotas for Amazon Redshift resources. STL QUERY METRICS records the execution time and resource consumption of queries. STL PLAN INFO records the query execution plan and the steps involved in each query.Reference:STL ALERT EVENT LOGSystem Tables and ViewsAWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

Amazon Athena is a serverless interactive query service that allows you to analyze data in Amazon S3 using standard SQL. Athena supports various data formats, such as CSV, JSON, ORC, Avro, and Parquet. However, not all data formats are equally efficient for querying. Some data formats, such as CSV and JSON, are row-oriented, meaning that they store data as a sequence of records, each with the same fields. Row-oriented formats are suitable for loading and exporting data, but they are not optimal for analytical queries that often access only a subset of columns. Row-oriented formats also do not support compression or encoding techniques that can reduce the data size and improve the query performance. On the other hand, some data formats, such as ORC and Parquet, are column-oriented, meaning that they store data as a collection of columns, each with a specific data type. Column-oriented formats are ideal for analytical queries that often filter, aggregate, or join data by columns. Column-oriented formats also support compression and encoding techniques that can reduce the data size and improve the query performance. For example, Parquet supports dictionary encoding, which replaces repeated values with numeric codes, and run-length encoding, which replaces consecutive identical values with a single value and a count. Parquet also supports various compression algorithms, such as Snappy, GZIP, and ZSTD, that can further reduce the data size and improve the query performance. Therefore, creating an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source and writing the data into the data lake in Apache Parquet format will meet the requirements most cost-effectively. AWS Glue is a fully managed service that provides a serverless data integration platform for data preparation, data cataloging, and data loading. AWS Glue ETL jobs allow you to transform and load data from various sources into various targets, using either a graphical interface (AWS Glue Studio) or a code-based interface (AWS Glue console or AWS Glue API). By using AWS Glue ETL jobs, you can easily convert the data from CSV to Parquet format, without having to write or manage any code. Parquet is a column-oriented format that allows Athena to scan only the relevant columns and skip the rest, reducing the amount of data read from S3. This solution will also reduce the cost of Athena queries, as Athena charges based on the amount of data scanned from S3. The other options are not as cost-effective as creating an AWS Glue ETL job to write the data into the data lake in Parquet format. Using an AWS Glue PySpark job to ingest the source data into the data lake in .csv format will not improve the query performance or reduce the query cost, as .csv is a row-oriented format that does not support columnar access or compression. Creating an AWS Glue ETL job to ingest the data into the data lake in JSON format will not improve the query performance or reduce the query cost, as JSON is also a row-oriented format that does not support columnar access or compression. Using an AWS Glue PySpark job to ingest the source data into the data lake in Apache Avro format will improve the query performance, as Avro is a column-oriented format that supports compression and encoding, but it will require more operational effort, as you will need to write and maintain PySpark code to convert the data from CSV to Avro format.Reference: Amazon Athena Choosing the Right Data Format AWS Glue [AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide], Chapter 5: Data Analysis and Visualization, Section 5.1: Amazon Athena

AWS Lake Formation is a service that helps you build, secure, and manage data lakes on Amazon S3. You can use AWS Lake Formation to register the S3 path as a data lake location, and enable fine-grained access control to limit access to the records based on the HR department's Region. You can use data filters to specify which S3 prefixes or partitions each HR department can access, and grant permissions to the IAM roles of the HR departments accordingly.This solution will meet the requirement with the least operational overhead, as it simplifies the data lake management and security, and leverages the existing IAM roles of the HR departments12.The other options are not optimal for the following reasons:A . Use data filters for each Region to register the S3 paths as data locations. This option is not possible, as data filters are not used to register S3 paths as data locations, but to grant permissions to access specific S3 prefixes or partitions within a data location. Moreover, this option does not specify how to limit access to the records based on the HR department's Region.C . Modify the IAM roles of the HR departments to add a data filter for each department's Region. This option is not possible, as data filters are not added to IAM roles, but to permissions granted by AWS Lake Formation.Moreover, this option does not specify how to register the S3 path as a data lake location, or how to enable fine-grained access control in AWS Lake Formation.E . Create a separate S3 bucket for each Region. Configure an IAM policy to allow S3 access. Restrict access based on Region. This option is not recommended, as it would require more operational overhead to create and manage multiple S3 buckets, and to configure and maintain IAM policies for each HR department. Moreover, this option does not leverage the benefits of AWS Lake Formation, such as data cataloging, data transformation, and data governance.1: AWS Lake Formation2: AWS Lake Formation Permissions: AWS Identity and Access Management: Amazon S3

To identify the reason why the Step Functions state machine is not able to run the EMR jobs, the company should take the following steps:Verify that the Step Functions state machine code has all IAM permissions that are necessary to create and run the EMR jobs. The state machine code should have an IAM role that allows it to invoke the EMR APIs, such as RunJobFlow, AddJobFlowSteps, and DescribeStep. The state machine code should also have IAM permissions to access the Amazon S3 buckets that the EMR jobs use as input and output locations.The company can use Access Analyzer for S3 to check the access policies and permissions of the S3 buckets12. Therefore, option B is correct.Query the flow logs for the VPC. The flow logs can provide information about the network traffic to and from the EMR cluster that is launched in the VPC. The company can use the flow logs to determine whether the traffic that originates from the EMR cluster can successfully reach the data providers, such as Amazon RDS, Amazon Redshift, or other external sources. The company can also determine whether any security group that might be attached to the EMR cluster allows connections to the data source servers on the informed ports.The company can use Amazon VPC Flow Logs or Amazon CloudWatch Logs Insights to query the flow logs3. Therefore, option D is correct.Option A is incorrect because it suggests using AWS CloudFormation to automate the Step Functions state machine deployment. While this is a good practice to ensure consistency and repeatability of the deployment, it does not help to identify the reason why the state machine is not able to run the EMR jobs. Moreover, creating a step to pause the state machine during the EMR jobs that fail and wait for a human user to send approval through an email message is not a reliable way to troubleshoot the issue. The company should use the Step Functions console or API to monitor the execution history and status of the state machine, and use Amazon CloudWatch to view the logs and metrics of the EMR jobs .Option C is incorrect because it suggests changing the AWS Step Functions state machine code to use Amazon EMR on EKS. Amazon EMR on EKS is a service that allows you to run EMR jobs on Amazon Elastic Kubernetes Service (Amazon EKS) clusters. While this service has some benefits, such as lower cost and faster execution time, it does not support all the features and integrations that EMR on EC2 does, such as EMR Notebooks, EMR Studio, and EMRFS. Therefore, changing the state machine code to use EMR on EKS may not be compatible with the existing data pipeline and may introduce new issues.Option E is incorrect because it suggests checking the retry scenarios that the company configured for the EMR jobs. While this is a good practice to handle transient failures and errors, it does not help to identify the root cause of why the state machine is not able to run the EMR jobs. Moreover, increasing the number of seconds in the interval between each EMR task may not improve the success rate of the jobs, and may increase the execution time and cost of the state machine. Configuring an Amazon SNS topic to store the error messages may help to notify the company of any failures, but it does not provide enough information to troubleshoot the issue.1: Manage an Amazon EMR Job - AWS Step Functions2: Access Analyzer for S3 - Amazon Simple Storage Service3: Working with Amazon EMR and VPC Flow Logs - Amazon EMR[4]: Analyzing VPC Flow Logs with Amazon CloudWatch Logs Insights - Amazon Virtual Private Cloud[5]: Monitor AWS Step Functions - AWS Step Functions[6]: Monitor Amazon EMR clusters - Amazon EMR[7]: Amazon EMR on Amazon EKS - Amazon EMR