Simulado AWS Certified Data Engineer – Associate (DEA-C01) | Questões Atualizadas

Explanation: For processing the incoming car listings in a cost-effective, scalable, and automated way, the ideal approach involves using AWS Glue for data processing, AWS Lambda with S3 Event Notifications for orchestration, Amazon Athena for one-time queries and analytical reporting, and Amazon QuickSight for visualization on the dashboard. Let's break this down: AWS Glue: This is a fully managed ETL (Extract, Transform, Load) service that automatically processes the incoming data files. Glue is serverless and supports diverse data sources, including Amazon S3 and Redshift. AWS Lambda and S3 Event Notifications: Using Lambda and S3 Event Notifications allows near real- time triggering of processing workflows as soon as new data is uploaded into S3. This approach is event-driven, ensuring that the listings are processed as soon as they are uploaded, reducing the latency for data processing. Amazon Athena: A serverless, pay-per-query service that allows interactive queries directly against data in S3 using standard SQL. It is ideal for the requirement of one-time queries and analytical reporting without the need for provisioning or managing servers. Amazon QuickSight: A business intelligence tool that integrates with a wide range of AWS data sources, including Athena, and is used for creating interactive dashboards. It scales well and provides real-time insights for the car listings. This solution (Option D) is the most cost-effective, because both Glue and Athena are serverless and priced based on usage, reducing costs when compared to provisioning EMR clusters in the other options. Moreover, using Lambda for orchestration is more cost-effective than AWS Step Functions due to its lightweight nature. Reference: AWS Glue Documentation Amazon Athena Documentation Amazon QuickSight Documentation S3 Event Notifications and Lambda

Explanation: Amazon DynamoDB is a serverless, low-latency, NoSQL database ideal for semi-structured data. Adding DynamoDB Accelerator (DAX) provides microsecond response times for read-heavy workloads. "For applications requiring millisecond or sub-millisecond reads with serverless operation, use DynamoDB with DAX caching." Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

Explanation: Lambda layers are a way to share code and dependencies across multiple Lambda functions. By packaging the custom Python scripts into Lambda layers, the data engineer can update the scripts in one place and have them automatically applied to all the Lambda functions that use the layer. This reduces the manual effort and ensures consistency across the Lambda functions. The other options are either not feasible or not efficient. Storing a pointer to the custom Python scripts in the execution context object or in environment variables would require the Lambda functions to download the scripts from Amazon S3 every time they are invoked, which would increase latency and cost. Assigning the same alias to each Lambda function would not help with updating the Python scripts, as the alias only points to a specific version of the Lambda function code. Reference: AWS Lambda layers AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 3: Data Ingestion and Transformation, Section 3.4: AWS Lambda

Explanation: The issue described is that the AWS Glue job is reprocessing files from previous runs despite the bookmark feature being enabled. Bookmarks in AWS Glue allow jobs to keep track of which files or data have already been processed to avoid reprocessing. The most likely reason for reprocessing the files is missing S3 permissions, specifically s3 s3 is a permission required by AWS Glue when bookmarks are enabled to ensure Glue can retrieve metadata from the files in S3, which is necessary for the bookmark mechanism to function correctly. Without this permission, Glue cannot track which files have been processed, resulting in reprocessing during subsequent runs. Concurrency settings (Option B) and the version of AWS Glue (Option C) do not affect the bookmark behavior. Similarly, the lack of a commit statement (Option D) is not applicable in this context, as Glue handles commits internally when interacting with Redshift and S3. Thus, the root cause is likely related to insufficient permissions on the S3 bucket, specifically s3 , which is required for bookmarks to work as expected. Reference: AWS Glue Job Bookmarks Documentation AWS Glue Permissions for Bookmarks

Explanation: AWS Glue is a fully managed and serverless ETL platform that supports scripts in PySpark or Python shell jobs. Workflows in Glue orchestrate multiple job stages without infrastructure management. "Use AWS Glue Workflows to build and orchestrate complex multi-stage ETL pipelines using serverless AWS Glue jobs." Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf This meets the "serverless only" and "runs scripts" requirements precisely.