A financial company recently added more features to its mobile app. The new features required the company to create a new topic in an existing Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster. A few days after the company added the new topic, Amazon CloudWatch raised an alarm on the RootDiskUsed metric for the MSK cluster. How should the company address the CloudWatch alarm?
Answer(s): A
The CloudWatch alarm on RootDiskUsed indicates that the storage space allocated to the MSK broker instances is nearing its capacity. Since new features and a new topic were added to the Kafka cluster, it is highly probable that the data volume being stored on the brokers has increased significantly, leading to the storage capacity issue.Option A, expanding the storage of the MSK broker, directly addresses the problem of insufficient disk space. Expanding the storage ensures that the Kafka brokers have enough space to store the incoming data, thereby resolving the CloudWatch alarm. Furthermore, configuring the MSK cluster storage to expand automatically avoids similar issues in the future as data volume grows. MSK supports storage auto-scaling feature.Option B, expanding the storage of Apache ZooKeeper nodes, is incorrect. ZooKeeper is primarily responsible for metadata management and cluster coordination, not data storage. The data resides on the Kafka brokers. Increasing the storage for ZooKeeper will not alleviate the root disk utilization issue of the brokers.Option C, updating the MSK broker instance to a larger instance type, might provide more storage depending on the instance type, but it's an indirect and potentially more expensive solution. While a larger instance type could increase compute and network resources as well, the primary problem identified by the CloudWatch alarm is storage capacity. It's best to directly address the specific issue rather than resorting to a more comprehensive (and likely pricier) upgrade of the instance type, unless CPU or Network are bottlenecking. Also, restarting the MSK cluster introduces unnecessary downtime.Option D, specifying the Target Volume-in-GiB parameter for the existing topic, is also incorrect. The TargetVolume-in-GiB parameter is used when creating or updating a tiered storage topic, indicating how much data to keep on the primary tier (broker storage) before tiering it to the secondary tier (S3). This parameter does not directly affect the total storage available to the brokers. This is the tiered storage feature, not an expansion of the overall broker storage. It helps in managing storage costs when dealing with data archiving to reduce the storage costs but is not applicable to the described scenario.Therefore, expanding the MSK broker storage and configuring it to expand automatically is the most direct and efficient solution to address the CloudWatch alarm on RootDiskUsed .Supporting Documentation:Amazon MSK Storage Expansion: Amazon MSK Monitoring with CloudWatch: Amazon MSK Tiered Storage:
A data engineer needs to build an enterprise data catalog based on the company's Amazon S3 buckets and Amazon RDS databases. The data catalog must include storage format metadata for the data in the catalog. Which solution will meet these requirements with the LEAST effort?
Answer(s): B
Here's a detailed justification for why option B is the best solution, along with supporting concepts and links:The requirement is to create an enterprise data catalog with storage format metadata for data residing in S3 buckets and RDS databases, minimizing manual effort.Option B, using AWS Glue crawlers and classifiers, directly addresses these needs efficiently. AWS Glue crawlers can automatically scan data sources like S3 and RDS. [ https://docs.aws.amazon.com/glue/latest/dg/add-crawler.html]Crucially, Glue crawlers utilize classifiers to infer the schema and data format (e.g., CSV, JSON, Parquet) of the data. These classifiers are pre-built to recognize common formats, and custom classifiers can be added for less standard formats. [ https://docs.aws.amazon.com/glue/latest/dg/add-classifier.html] This automated format recognition significantly reduces manual effort. The metadata, including the data format, is then stored within the AWS Glue Data Catalog.Option A suggests manual inspection and updates by data stewards. This approach is time-consuming and prone to errors, directly contradicting the "least effort" requirement.Option C proposes using Amazon Macie. While Macie is valuable for identifying sensitive data, it is not designed for building comprehensive data catalogs or focusing on storage format metadata. Macie's primary concern is data security and compliance, not general metadata management. [ https://aws.amazon.com/macie/]Option D suggests using custom scripts to scan data elements and assign classifications. This involves significant development and maintenance overhead, violating the "least effort" requirement. It also duplicates functionality already provided by AWS Glue crawlers and classifiers.In summary, AWS Glue crawlers with classifiers provide an automated and efficient way to discover, classify, and catalog data, including its storage format. This eliminates the need for manual inspection or custom scripting, making it the optimal solution for meeting the requirements with the least amount of effort.
A company analyzes data in a data lake every quarter to perform inventory assessments. A data engineer uses AWS Glue DataBrew to detect any personally identifiable formation (PII) about customers within the data. The company's privacy policy considers some custom categories of information to be PII. However, the categories are not included in standard DataBrew data quality rules. The data engineer needs to modify the current process to scan for the custom PII categories across multiple datasets within the data lake. Which solution will meet these requirements with the LEAST operational overhead?
The best solution is B. Implement custom data quality rules in DataBrew. Apply the custom rules across datasets.Here's why:DataBrew's Custom Rules: AWS Glue DataBrew allows users to create and apply custom data quality rules. This means the data engineer can define rules specifically tailored to identify the custom PII categories defined in the company's privacy policy. This aligns directly with the requirement to scan for these specific categories.Scalability and Automation: Once the custom rules are defined in DataBrew, they can be easily applied across multiple datasets within the data lake. This ensures a consistent and automated approach to PII detection, reducing manual effort and improving efficiency for the quarterly inventory assessments.Least Operational Overhead: Creating and managing custom rules within DataBrew is a low-overhead approach. DataBrew provides a user-friendly interface and pre-built functionalities that minimize the need for extensive coding or complex infrastructure management.Why other options are less ideal:A: Manually review the data: Manual review is not scalable or efficient, especially when dealing with large datasets in a data lake. It's also prone to human error. C: Develop custom Python scripts: While this approach is feasible, it introduces more operational overhead. It requires developing, testing, and maintaining custom scripts, as well as integrating them with DataBrew. Furthermore, DataBrew already provides the mechanism to define custom rules, making the scripts redundant. D: Implement regex patterns during ETL: While this can be part of an overall data governance strategy, it's less flexible for ongoing monitoring of existing data in the lake. It addresses PII during ingestion but does not readily apply to datasets already present. Additionally, DataBrew's UI and rule-based approach offer easier management compared to embedding complex regex patterns in ETL pipelines.Authoritative Links:AWS Glue DataBrew Features : This provides an overview of DataBrew's capabilities, including data quality rules and PII detection. AWS Glue DataBrew Documentation : Detailed documentation on using DataBrew, including creating and applying custom rules.
A company receives a data file from a partner each day in an Amazon S3 bucket. The company uses a daily AWS Glue extract, transform, and load (ETL) pipeline to clean and transform each data file. The output of the ETL pipeline is written to a CSV file named Daily.csv in a second S3 bucket. Occasionally, the daily data file is empty or is missing values for required fields. When the file is missing data, the company can use the previous day’s CSV file. A data engineer needs to ensure that the previous day's data file is overwritten only if the new daily file is complete and valid. Which solution will meet these requirements with the LEAST effort?
The correct answer is B because it provides the most efficient and automated way to validate data integrity before overwriting existing files. AWS Glue Data Quality rules, defined using DQDL, allow for declarative data quality checks within the ETL pipeline itself. This eliminates the need for separate Lambda functions or manual SQL queries. Glue Data Quality can detect missing values and empty files, failing the ETL job if the data doesn't meet the defined rules. The job failure prevents the overwriting of the previous day's data with invalid information.Option A, using Lambda, introduces additional operational overhead and complexity because it requires managing a separate function, deployment, and error handling. Option C is not robust; filling missing values with the "most common" value can introduce inaccuracies. Option D involves running a SQL query with Athena, which is a separate step and not integrated into the ETL pipeline, making it less automated and efficient. Therefore, using Glue Data Quality rules (option B) is the most streamlined, cost-effective, and reliable approach.Here are some authoritative links to support this justification:AWS Glue Data Quality: https://docs.aws.amazon.com/glue/latest/dg/data-quality-intro.html DQDL Reference: https://docs.aws.amazon.com/glue/latest/dg/dqdl.html AWS Glue ETL: https://aws.amazon.com/glue/features/
A marketing company uses Amazon S3 to store marketing data. The company uses versioning in some buckets. The company runs several jobs to read and load data into the buckets.To help cost-optimize its storage, the company wants to gather information about incomplete multipart uploads and outdated versions that are present in the S3 buckets. Which solution will meet these requirements with the LEAST operational effort?
Answer(s): C
The optimal solution is to use Amazon S3 Storage Lens because it provides a comprehensive, organization-wide view of object storage usage and activity trends. S3 Storage Lens offers built-in dashboards that automatically generate insights into storage usage, data tiering, activity, and data protection, including information about incomplete multipart uploads and noncurrent versions.Option A, using the AWS CLI, would require custom scripting and manual aggregation of data across buckets, which increases operational overhead. Option B, using Amazon S3 Inventory reports, can provide detailed information about objects and their versions, but requires configuring separate inventory reports for each bucket and then aggregating the reports to get a comprehensive view, increasing complexity and effort. Option D, using AWS Usage Reports, focuses primarily on billing and cost data, and does not provide granular insights into incomplete multipart uploads or outdated versions within S3 buckets.S3 Storage Lens, on the other hand, offers a single pane of glass to monitor storage across the entire organization, identify cost optimization opportunities, and track data protection trends with minimal configuration. Its pre-built dashboards automatically surface insights about incomplete multipart uploads (which consume storage space unnecessarily) and noncurrent versions (which incur storage costs without offering immediate value). This aligns perfectly with the company's goal of cost optimization with the least operational effort. S3 Storage Lens allows you to view the size and count of incomplete multipart uploads and noncurrent versions, helping to identify and address inefficiencies in storage utilization. It provides a centralized and automated approach to storage analysis.https://aws.amazon.com/s3/storage-lens/https://docs.aws.amazon.com/AmazonS3/latest/userguide/storage_lens.html
A gaming company uses Amazon Kinesis Data Streams to collect clickstream data. The company uses Amazon Data Firehose delivery streams to store the data in JSON format in Amazon S3. Data scientists at the company use Amazon Athena to query the most recent data to obtain business insights. The company wants to reduce Athena costs but does not want to recreate the data pipeline. Which solution will meet these requirements with the LEAST management effort?
Option A is the most suitable solution because it efficiently addresses the company's requirements for reducing Athena costs with minimal management overhead. Switching Firehose's output format to Apache Parquet directly improves Athena query performance. Parquet is a columnar storage format, which allows Athena to read only the necessary columns for a query, significantly reducing data scanned and thus costs. Configuring a custom S3 object YYYYMMDD prefix enables partitioning, further optimizing query performance by allowing Athena to target specific date ranges. Specifying a large buffer size in Firehose reduces the number of small files written to S3, improving Parquet file efficiency.For existing data, the AWS Glue ETL job is a straightforward way to convert the historical JSON data into Parquet format, combine small files, and add the partition prefix. AWS Glue is a fully managed ETL service, reducing the management overhead compared to self-managed solutions. The ALTER TABLE ADDPARTITION statement then updates the Athena table metadata to reflect the new partitioned data. This approach requires minimal changes to the existing data pipeline while optimizing Athena query performance and costs.Option B involves a more complex and resource-intensive approach using Amazon EMR and Spark, which adds operational overhead. Option C introduces a Kinesis Data Analytics application, which increases complexity for a task that can be accomplished with Firehose and Glue. Option D involves both Lambda and Glue ETL jobs which adds complexity. Lambda's role in Firehose is limited as it should be as thin as possible, Glue ETL handles bigger data transformations. Therefore, compared to the others, option A has the least management effort.Relevant documentation:Apache Parquet: https://parquet.apache.org/ Amazon Athena Cost Optimization: https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/ Amazon Kinesis Data Firehose Data Format Conversion: https://docs.aws.amazon.com/firehose/latest/dev/data-transformation.html AWS Glue: https://aws.amazon.com/glue/
A company needs a solution to manage costs for an existing Amazon DynamoDB table. The company also needs to control the size of the table. The solution must not disrupt any ongoing read or write operations. The company wants to use a solution that automatically deletes data from the table after 1 month. Which solution will meet these requirements with the LEAST ongoing maintenance?
Here's a detailed justification for why option A is the best solution:The problem requires automatic deletion of data in a DynamoDB table after one month with minimal maintenance and no disruption to read/write operations.Option A, using DynamoDB Time To Live (TTL), directly addresses these requirements in the most efficient way. DynamoDB TTL is a built-in feature that allows you to specify a timestamp attribute for each item. After the timestamp elapses, DynamoDB automatically deletes the item. This is a fully managed service, so it requires minimal maintenance. DynamoDB handles the deletion process in the background without impacting read or write performance.Option B, using EventBridge and Lambda, requires creating and maintaining an EventBridge rule, a Lambda function, and the code to scan and delete data. This adds complexity and maintenance overhead compared to TTL. While functional, it is not the least maintenance option.Option C, using DynamoDB Streams and Lambda, is triggered by every change to the table. While useful for other purposes (e.g., auditing), using it solely for data deletion is inefficient. The Lambda function would have to process every update, determine the age of the data, and then potentially delete it. This is more resource-intensive than TTL.Option D, using a Lambda function to periodically scan the table, requires developing and maintaining the Lambda function and configuring its schedule. Scanning a DynamoDB table can be resource-intensive and can impact performance, especially if the table is large. It's also less efficient than TTL, as it involves reading the data to determine its age before potentially deleting it.DynamoDB TTL provides a native, low-maintenance, and efficient solution for automatically expiring data based on timestamps. The other options involve extra complexity and resource utilization which do not align with the requirement for the LEAST ongoing maintenance. The solution utilizes DynamoDB's built-in capabilities, offering optimal efficiency and simplicity.Therefore, Option A, using DynamoDB TTL, is the best solution because it is a fully managed service that handles data deletion automatically without impacting performance and requires the least amount of ongoing maintenance.DynamoDB TTL Documentation
A company uses Amazon S3 to store data and Amazon QuickSight to create visualizations, The company has an S3 bucket in an AWS account named Hub-Account. The S3 bucket is encrypted by an AWS Key Management Service (AWS KMS) key. The company's QuickSight instance is in a separate account named BI-Account. The company updates the S3 bucket policy to grant access to the QuickSight service role. The company wants to enable cross-account access to allow QuickSight to interact with the S3 bucket.Which combination of steps will meet this requirement? (Choose two.)
Answer(s): D,E
D: Add an IAM policy to the QuickSight service role to give access to the KMS key.The QuickSight service role must have:kms:Decrypt kms:GenerateDataKeyWithout this, QuickSight cannot read encrypted objects.E: Add the KMS key as a resource that the QuickSight service role can access.The KMS key policy (in Hub-Account) must allow:The QuickSight service role from BI-AccountCross-account access to use the keyBoth the IAM policy (D) and KMS key policy (E) are required.
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