I can provide you with a list of top 10 AWS Textract interview questions and answers, which a professional cloud developer with years of experience in AWS, Google, and Microsoft cloud services might encounter:
- Q: What is AWS Textract, and how does it differ from traditional OCR systems?
A: AWS Textract is a machine learning-based service that extracts text and structured data from scanned documents, images, or PDFs. Unlike traditional OCR systems that focus only on extracting text, Textract can identify different elements like tables, forms, and key-value pairs, making it easier to process and analyze the data.
- Q: What are the supported input formats and file types for AWS Textract?
A: Textract supports the following file formats: JPEG, PNG, and PDF. The maximum file size is 5 MB for images (JPEG/PNG) and 300 pages or 500 MB for PDFs.
- Q: What are the primary components of AWS Textract?
A: The primary components of Textract are:
- Text detection and extraction
- Form data extraction (key-value pairs)
- Table data extraction (rows and cells)
- Q: How can you integrate AWS Textract with other AWS services?
A: AWS Textract can be integrated with services like Amazon S3, AWS Lambda, and Amazon Comprehend. For example, you can store input documents in an S3 bucket, trigger a Lambda function to process the document using Textract, and then analyze the extracted data using Amazon Comprehend.
- Q: Explain the process of working with AWS Textract asynchronously.
A: For large documents or a large number of pages, you can use Textract’s asynchronous operations. The process involves the following steps:
- Start an asynchronous job using StartDocumentTextDetection or StartDocumentAnalysis.
- Poll the job status using GetDocumentTextDetection or GetDocumentAnalysis.
- Once the job is complete, fetch the results using the provided JobId.
- Q: Can you describe how to use AWS Textract in a serverless architecture?
A: In a serverless architecture, you can use AWS Lambda and Amazon S3 to process documents with Textract. You can configure an S3 event trigger to invoke a Lambda function when a new document is uploaded. The Lambda function can then call Textract APIs to extract text and data from the document and store the results in another S3 bucket or a database.
- Q: How does AWS Textract handle low-quality or noisy input documents?
A: AWS Textract uses machine learning algorithms that are designed to handle varying levels of document quality. While the accuracy may decrease with lower quality inputs, Textract is still capable of extracting useful information in many cases. However, it is always recommended to provide high-quality images to maximize the accuracy of the extracted data.
- Q: What are the limits and quotas for using AWS Textract?
A: Some limits and quotas for Textract include:
- 5 TPS (transactions per second) for the synchronous API
- 2 TPS for the asynchronous API
- 500 MB or 300 pages per PDF document
- 5 MB per image (JPEG/PNG)
- Q: How is AWS Textract billed?
A: Textract has a pay-as-you-go pricing model, which means you only pay for the pages you process. The cost depends on the type of data extraction performed (text, forms, or tables) and the total number of pages processed.
- Q: How can you improve the accuracy of AWS Textract results?
A: To improve the accuracy of AWS Textract results, you can:
- Provide high-quality input images or documents: Ensure that the documents are clear, with minimal noise, distortion, or skewing. - Pre-process the images: Apply techniques like image resizing, binarization, or deskewing to enhance the quality of the input images. - Use custom vocabularies: If your documents contain domain-specific terminology, you can use Amazon Comprehend or Amazon Transcribe with custom vocabularies to improve the understanding of these terms. - Post-process the results: Implement data validation, correction, or enrichment by integrating with other AWS services like Amazon Comprehend, Amazon Translate, or Amazon SageMaker. - Train a custom machine learning model: In cases where Textract does not provide the desired accuracy, you can consider training a custom model using Amazon SageMaker to better suit your specific use case.